{ "inventory_year": 2025, "source": "Office of Management and Budget, 2025 Federal Agency AI Use Case Inventory (consolidated)", "source_url": "https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory", "total_use_cases": 3611, "agencies": 41, "stage_counts": [ [ "Pre-deployment", 1479 ], [ "Deployed", 1040 ], [ "Pilot", 440 ], [ "Not reported", 338 ], [ "Retired", 314 ] ], "high_impact_total": 445, "rules": [ { "key": "intake", "label": "Document intake and data extraction", "pattern": "\\b(ocr|optical character|intelligent document|document (processing|ingestion|intake)|data extraction|extract(s|ed|ing)? (key )?(data|information|text|fields)|digitiz\\w*|scanned (documents|forms|records|images of))" }, { "key": "review", "label": "Document review, classification and routing", "pattern": "\\b((classif\\w*|categoriz\\w*|triage|route|routing|sort\\w*) (of )?(incoming )?(documents?|records?|e-?mails?|correspondence|submissions?|requests?|comments?|tickets?|cases?)|document (review|classification|triage|routing))" }, { "key": "summary", "label": "Summarization", "pattern": "\\bsummar(y|ies|ize|izes|ized|izing|isation|ization) (of )?(\\w+ ){0,3}(documents?|reports?|records?|cases?|case files?|files?|comments?|correspondence|e-?mails?|calls?|meetings?|notes|text|articles?|transcripts?|filings?|applications?|claims?|contracts?|policies|regulations|feedback|responses|interviews?)\\b" }, { "key": "correspondence", "label": "Correspondence, calls and inquiries", "pattern": "\\b(correspondence|call (center|centre)|contact center|chat ?bot|virtual (agent|assistant)|inquir(y|ies)|public comments?|e-?mail (responses?|replies|drafting))" }, { "key": "forms", "label": "Forms, records and case files", "pattern": "\\b(forms? (processing|completion|review)|records management|foia|freedom of information|case files?|paperwork|claims? processing|application processing|redact\\w*)" }, { "key": "translation", "label": "Translation and transcription", "pattern": "\\b(translat(e|es|ed|ing|ion|ions) (of |between |into |from )?(\\w+ ){0,2}(documents?|text|languages?|speech|content|materials?|english|spanish)|language translation|machine translation|transcri(be|bes|bed|bing|ption|ptions))\\b" } ], "paperwork": [ { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Smartphone Information Forensics Triage", "bureau": "CBP", "problem": "Quickly translate and summarize the content of text messages, prompting the inspecting employee to review the actual raw text if warranted.", "outputs": "Translations and summary are shown only on the display/monitor, and are not saved or transmitted.", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "RAPTOR (Rapid Tactical Operations Reconnaissance)", "bureau": "CBP", "problem": "Provide Tactical Domain Awareness for CBP Agents, making law enforcement efforts more efficient.", "outputs": "Text transcription of vessel registration/documentation data and photographs of the vessel.", "stage": "Pre-deployment", "high_impact": false, "technique": "Computer Vision", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "FOIA processing automated redaction (RedactAI)", "bureau": "CBP", "problem": "This use case leverages AI technology to streamline the identification and redaction of sensitive content in documents related to FOIA requests. Using Vertex AI and other GCP services, the system identifies and categorizes content that may require redaction…", "outputs": "Output is recommendation of content to be redacted. Users are required to review and manually approve the suggested redactions based on the appropriate FOIA exemptions.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Speech Assist Virtual Interview Assistant", "bureau": "CBP", "problem": "Accurate audio processing automation and draft transcription generation to reduce the manual labor intensive associated with transcribing audio interviews and reports, while ensuring Officer-vetted accuracy and nuance.", "outputs": "The AI system will provide original audio clips alongside feature-rich output reports that include diarized transcriptions, transcript summaries, speaker summaries, and named entities extracted from…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Business Automation and Improved Search Ability with AI", "bureau": "CBP", "problem": "The Business Connection (BC ) and Technical Reference Model (TRM) teams have identified areas in which business automation can accelerate use for both business analysts in making determinations and improving the ability of end-users to search for the…", "outputs": "The AI will output a potential product classification and sub-classification that would best fit the product within the TRM and BC. The vendor vetting feature will output the headquarters of a…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "CBP Translate", "bureau": "CBP", "problem": "Assist officers and agents with immediate interpretation needs when human translators are not available.", "outputs": "The outputs of CBP Translate include translated text or audio in the form of chat bubbles, which store each interaction. Additionally, CBPOs can capture images of non-travel documents for text…", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "CBP Careers Bot - Leo", "bureau": "CBP", "problem": "The Natural Language Processing (NLP) chatbot is intended to help visitors to the CBP careers site navigate complex and extensive career resources quickly and easily. By providing a guided, interactive experience, the chatbot simplifies access to the most…", "outputs": "The NLP-chatbot will provide natural language responses to user queries.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "ChatCBP", "bureau": "CBP", "problem": "Workforce enablement and efficiency.", "outputs": "Generative LLM that will allow users to upload, search, delete, summarize documents, conduct advanced searches, and identify similar language in documents and receive natural language-style outputs…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "GenAI for Document Summarization and Content Generation", "bureau": "CBP", "problem": "Workforce enablement and efficiency through the implementation of Generative AI / Large Language Models (LLM)", "outputs": "Generative LLM applications deployed in a stand alone capacity or embedded in existing systems that will allow users to upload, search, summarize documents, conduct advanced searches, and identify…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Automated Detection of Personally Identifiable Information (PII) in Cybersecurity Data", "bureau": "CISA", "problem": "To enhance privacy, this AI tool uses Natural Language Processing (NLP) to automatically flag potential PII for review and removal by CISA analysts.", "outputs": "The system sends flagged data rows with potential PII to humans for review.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Commercial Generative AI for Text Generation (AI Chatbot)", "bureau": "DHS", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Individual Assistance Document Translation", "bureau": "FEMA", "problem": "This AI use case addresses three inter-related problems: It eliminates delays associated with human translation of documents originally in non-English languages; it reduces the cost of translation from approximately $40.00/document to pennies per document,…", "outputs": "The output is the English version of survivor-submitted documents that are non-English in their original version. Both the original submission and the translated version will be stored in the…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Grants Manager Artificial Intelligence ChatBot", "bureau": "FEMA", "problem": "The chatbot is intended to solve inefficiencies in FEMA’s grants application review process by streamlining access to policy information, reducing the time and effort required for manual research, and simplifying the interpretation of complex policy…", "outputs": "The AI system, powered by Azure OpenAI Services, generates outputs such as policy-based responses to user queries, concise summaries of complex policies, and historical chat records for reference.…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Program Integrity (RRAD-PI) AI Counter-Fraud Enhancement Measures", "bureau": "FEMA", "problem": "The AI use case aims to further expand and address RRAD-PI’s fraud detection and prevention challenges within disaster recovery programs. Specifically, it seeks to mitigate fraudulent activities, identity theft, and deceptive practices that compromise…", "outputs": "The AI model/system may generate outputs such as:_x000D_ • Fraud Risk Scores: Quantitative assessments of fraud likelihood for transactions, applications, or entities._x000D_ • Anomaly Alerts:…", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "review", "forms" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Hazard Mitigation Assistance Chatbot", "bureau": "FEMA", "problem": "Understanding of Hazard Mitigation Assistance (HMA) programs and data analysis of HMA data. The tool will lead to a consistent and accurate understanding of the programs and contribute to a standardization of project reviews.", "outputs": "The tool utilizes the OpenAI models through Azure OpenAI to respond to prompts asked through the chatbot.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Technical Resource for Mitigation Programs", "bureau": "FEMA", "problem": "The FEMA Hazard Mitigation Assistance (HMA) AI solution addresses the challenge of managing complex grant processes that currently rely on manual review of thousands of applications, modifications, and closeout packages. Analysts must manually extract and…", "outputs": "The AI system produces both machine-readable and human-readable artifacts to support grant management throughout the lifecycle. These include structured findings reports that categorize issues by…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Executive Summary GPT", "bureau": "FEMA", "problem": "Provides the ability for users to upload documents and provide an executive summary on those documents.", "outputs": "Leverages Azure Commercial OpenAI within the FEMA system boundary, currently leveraging ChatGPT4o. A summary of a document or documents that can be leveraged for high level summaries for leadership…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Real-Time Language Translation Services", "bureau": "ICE", "problem": "The AI is intended to solve the problem of ICE personnel often lacking timely access to interpreters in offline or low-connectivity environments, making it difficult to communicate with individuals who speak little or no English during operations.", "outputs": "The planned platforms and mobile applications use AI translation models to convert spoken or written language between English and other languages in near real time. Personnel can speak or type into…", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Email Analytics for Investigative Data", "bureau": "ICE", "problem": "This use case intends to solve the problem of the time-consuming and resource-intensive process of preparing multilingual email data for analysis.", "outputs": "The output is email data that has undergone spam message classification, translation, and entity extraction, including names, organizations, or locations.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Title III Semantic Search and Summarization for Translated Content", "bureau": "ICE", "problem": "This use case intends to solve the problem of efficiently searching through and analyzing large volumes of translated evidentiary data, which can be time-consuming and labor-intensive.", "outputs": "Outputs include semantic search results, concise data summaries, extracted key entities, identified trends and patterns, and generated investigative leads from large volumes of translated and…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Policy Analyst Assistant", "bureau": "ICE", "problem": "This use case intends to solve the problem of the significant manual effort it takes SEVP staff to research and respond to regulatory and policy questions related to foreign students in the F and M classes of admission, as well as schools that are either…", "outputs": "Generated outputs provide an initial analysis of applicable material, which analysts refine, modify, and review as part of their process.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "SEVP Response Center Chatbot - SID (SEVIS Interactive Dialog)", "bureau": "ICE", "problem": "This use case intends to solve the problem of handling a high volume of routine inquiries from students and officials, which can overwhelm human agents and delay response times.", "outputs": "SID answers frequently asked questions from callers. If the SID cannot answer a caller’s question, it turns the caller over to an agent in the response center. The chatbot captures the interaction…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Digital Records Manager (DRM) User Assistance Chatbot", "bureau": "ICE", "problem": "The AI tool, the Digital Records Manager (DRM) User Assistance Chatbot, is designed to address the problem of efficiently searching and gathering information within the Investigative Case Management (ICM) system used by Homeland Security Investigations…", "outputs": "The outputs of the DRM User Assistance Chatbot will be natural language responses to user questions, based on a custom Knowledge Base of DRM documentation artifacts, supported by the natural…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Burlington Finance Center Voice Bot", "bureau": "ICE", "problem": "This use case intends to solve the problem of the manual and time-consuming process of handling routine bond inquiries.", "outputs": "It will use Natural Language Understanding (NLU)/Natural Language Processing (NLP) to perform voice-to-text and text-to-voice translations, giving it the ability to recognize voices and meaning. The…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence", "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "ICE Enterprise AI Assistant", "bureau": "ICE", "problem": "This use case aims to (1) address cybersecurity concerns related to ICE personnel using externally hosted commercial chatbots and (2) solve the issue of out-of-the-box LLMs lacking tailored capabilities for ICE, such as the integration of internal ICE…", "outputs": "The solution’s AI outputs vary depending on the user’s request, but generally help personnel quickly access relevant data, reducing the time spent searching through internal resources. The chatbot…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "ICE Terminology and Data Asset Discovery Chatbot", "bureau": "ICE", "problem": "This use case intends to solve the problem of the time-consuming process of manually searching internal ICE resources for dataset information to create logical data definitions.", "outputs": "The AI produces text responses to user queries and generates candidate logical table and column names, which Data Management Team personnel review and validate against source information and may…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "AI-Assisted eDiscovery Search", "bureau": "ICE", "problem": "The use case addresses inefficiencies in traditional document search and retrieval processes, such as time-consuming manual searches, the complexity of crafting SQL queries, low relevance and accuracy in search results, difficulties in digesting lengthy…", "outputs": "The solution’s AI outputs include precise search results and summarized content, enabling users to quickly access information, make informed decisions, and take follow-on actions without requiring…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Mobile Fortify", "bureau": "ICE", "problem": "The AI is intended to solve the problem of confirming individuals’ identities in the field when officers and agents must work with limited information and access multiple disparate systems to identify individuals and retrieve existing data relevant to…", "outputs": "Mobile Fortify runs on a mobile device and can capture facial images, contactless fingerprints, and photographs of identity documents. The application transmits this data to U.S. Customs and Border…", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "License Plate Capture and Analysis", "bureau": "ICE", "problem": "The AI is intended to solve the problem of time-consuming manual reviews of license plate images and data, which makes it challenging for investigators to identify relevant vehicle movements and patterns.", "outputs": "The system processes images and metadata from ICE-owned and commercial license plate recognition cameras. It uses computer vision and optical character recognition to detect and read license plates…", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Translation and Transcription for Investigative Data", "bureau": "ICE", "problem": "This use case intends to solve the problem of the time-consuming process of translating and transcribing data for investigative purposes.", "outputs": "The Translation and Transcription Service leverages neural machine translation (NMT) models for text translation and automatic speech recognition (ASR) and deep neural network (DNN) models with…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "AI-Assisted Audio/Video Redaction", "bureau": "ICE", "problem": "This use case intends to solve the problem of the labor-intensive process of redacting audio and video evidence.", "outputs": "The AI output in this use case are redactions to the media file. Users will further edit the redacted media prior to exporting the final redacted file to ensure completeness. Homeland Security…", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "forms" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Blockchain Analysis", "bureau": "ICE", "problem": "By utilizing this AI-powered blockchain analysis platform, investigators can uncover hidden connections across blockchain networks, detect illicit activities, and significantly reduce the time required for manual analysis, enhancing HSI’s ability to combat…", "outputs": "The platform’s outputs include confidence scores for address attributions, risk flags based on behavioral typologies, identification of hidden connections across blockchain ecosystems, and…", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "summary" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Enhanced Lead Identification and Targeting", "bureau": "ICE", "problem": "The AI is intended to solve the problem of unstructured, hard‑to‑read address information in records like rap sheets and warrants, which makes it difficult and time‑consuming for Enforcement and Removal Operations officers to extract accurate addresses and…", "outputs": "The outputs of Enhanced Leads Identification & Targeting for Enforcement (ELITE) are enriched leads that include AI-extracted addresses. Enforcement and Removal Operations officers review these…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Intelligent Document Processing", "bureau": "ICE", "problem": "This use case intends to solve the problem of the manual effort required to validate and extract data from forms.", "outputs": "This platform will provide Optical Character Recognition and machine learning models to verify, extract, and classify information from ICE forms.", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Mobile Language Translation Services", "bureau": "ICE", "problem": "", "outputs": "", "stage": "Retired", "high_impact": true, "technique": "", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Machine Translation\n(Previously Language Translator)", "bureau": "ICE", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "LIGER Generative AI Toolkit", "bureau": "MGMT", "problem": "LIGER® for FPS will enable FPS users to employ the power of a Large Language Model (LLM) against non-public and sensitive Agency documents to save time and effort conducting time-intensive tasks, such as draft documents to include: Position Descriptions…", "outputs": "LIGER® uses Natural Language Processing (NLP) to return an easily readable text narrative. Like all GenAI, the text response should be verified for accuracy.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "DHS-Chat", "bureau": "MGMT", "problem": "DHS personnel need a reliable solution to quickly access accurate information, process documents, and support work tasks within the DHS Workspace. Existing workflows are often time-consuming and inefficient, and there is a need to streamline operations while…", "outputs": "The internally available generative AI tool outputs text based on the users input.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "ESEC Inquiry (STORM) Summarization", "bureau": "MGMT", "problem": "This use case is intended to decrease the time it takes to enter incoming requests into Storm. By summarizing the request, analysts are able to more quickly take action and assign to relevant parties. It will also lead to future capabilities to draft…", "outputs": "When a user creates a new work package and uploads a letter, it activates a Power Apps workflow that creates a summary.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Electronic Evidence/Video Recording Transcription and Summarization Tool", "bureau": "TSA", "problem": "This will cut investigative processing time significantly, while also increasing accuracy.", "outputs": "The AI will transcript audio/video data and provide a printable artifact. The AI only provides recommendations the final usable data is reviewed and certified by TSA staff.", "stage": "Pre-deployment", "high_impact": true, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Document Translation Service (DTS)", "bureau": "TSA", "problem": "Leveraging accurate cross-language translation with the latest Azure AI cloud service", "outputs": "Translates documents to and from 100 languages and dialects while preserving document structure and data format. (See Section 10.9 TAZ Azure Service Utilization)", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "TSA Contact Center Virtual Assistant", "bureau": "TSA", "problem": "The AI use case is designed to enhance customer service accessibility, improve operational efficiency, and provide valuable data insights to support TSA's mission and decision-making processes: 1. Limited Accessibility and Availability: The chatbot addresses…", "outputs": "As a predictive AI capability, the TSA's Virtual Assistant chatbot functions by correlating existing content within the TCC's knowledge library with the NLP to identify the most relevant knowledge…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "AskTSA", "bureau": "TSA", "problem": "The AI is intended to address several challenges within the AskTSA customer service process. These include reducing the time it takes for agents to respond to inquiries, improving the accuracy and consistency of responses, and streamlining the categorization…", "outputs": "Summarized Inquiries: Condensed explanations of why a customer is reaching out; Recommended Responses: Suggested replies tailored to the summarized inquiries; Categorized Inquiries: Labels or…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "USCIS Document Translation Service", "bureau": "USCIS", "problem": "Reduce the man hours needed for translating evidence documents by leveraging AI document translation technology", "outputs": "The service delivers an image-to-image translation that is displayed side by side with the original document to aid officer in reviewing the evidence and preparing for the interview.", "stage": "Pre-deployment", "high_impact": true, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "USCIS Speech Translation Service", "bureau": "USCIS", "problem": "Provide efficient communication, consultations, and other live applicant interactions in USCIS offices.", "outputs": "Provides speech to speech and speech to text translation in multiple languages through government-issued iPads.", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Extended Automated Name Harvesting (eANH)", "bureau": "USCIS", "problem": "OIT is developing a solution that systematically extracts text from evidence documents, identifies aliases and DOBs from the extracted text.", "outputs": "Extracted names and DOBs", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Private Artificial Intelligence (AI) Tech Hub (PAiTH)", "bureau": "USCIS", "problem": "USCIS staff currently lack access to AI tools that can accelerate routine knowledge work while maintaining the security, compliance, and privacy requirements specific to USCIS operations. Existing commercial AI solutions cannot access USCIS-specific…", "outputs": "PAiTH will generate text-based outputs customized to the user's organizational persona: Legal Persona: Legal research summaries, statute and regulation citations (INA, CFR), case law analysis, draft…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Text Analytics Data Science Sentence Similarity Model", "bureau": "USCIS", "problem": "The Text Analytics capability employs machine learning and data graphing techniques to identify patterns that may indicate potential fraud, national security, and/or public safety concerns by scanning the digitized narrative sections of the associated…", "outputs": "Text Analytics does not make predictions, recommendations, or decisions. It is merely a research tool that identifies potential patterns, while remaining agnostic as to whether those patterns…", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Automated Realtime Global Organization Specialist (ARGOS) for Company Registration Submissions to E-Verify", "bureau": "USCIS", "problem": "The goal of the use case is to leverage sentiment analysis in ARGOS to streamline the process and accelerate work for the individual who is researching a company that has submitted its information for registration to E-Verify.", "outputs": "Responses back to a user dashboard accessible internally only by VAC Management and Program Analyst (MPA) personnel. Keywords relating to the MPA's work interest are extracted if present and risk…", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "Intelligent Document Processing (IDP) for I-539 Form Digitization", "bureau": "USCIS", "problem": "Before the use case, all pages of a I-539 application were scanned and stored as a single document in the content management system, delaying adjudication and not meeting National Archives and Records Administration (NARA) standards. The tool uses a learning…", "outputs": "Input - one digital file comprised of all pages of a 539 benefit application. Output - multiple digital files comprised of the individual documents submitted as part of the 539 benefit application.…", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "PDF Intake (PDFI) for myUSCIS", "bureau": "USCIS", "problem": "Scanned PDFs submitted through MyUSCIS must be validated against form-specific business rules related to both the overall document and the contents of specific fields. Constructed a service that can process a scanned input document and return all information…", "outputs": "The output of this AI system is structured information about the validation rules applied to the input form as well as the extracted contents of filled fields on the form, presented in a JSON format…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "OCR for Scanning and Cataloging Documentation [Captiva Open Text]", "bureau": "USCIS", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "intake" ] }, { "agency": "DHS", "agency_name": "Department of Homeland Security", "name": "License Plate Reader", "bureau": "USSS", "problem": "To identify license plate information quickly, efficiently, and accurately from low-quality imagery, advanced image processing and machine learning techniques are typically employed. Need to be able to enhance the clarity of the image, correct distortions,…", "outputs": "Optical Character Recognition (OCR) technology is utilized to detect and interpret the alphanumeric characters on the license plate. The extracted license plate information serves as a decision…", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "Meeting Transcription Summarization", "bureau": "BEA", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "translation" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "Global Business Navigator Chatbot", "bureau": "ITA", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "NWS Public Safety Language Translation", "bureau": "NOAA", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "translation" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "AI-powered Chatbot for Federal Funding Assistance (Grants) Guidance Dissemination", "bureau": "NOAA", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "Support Chat Bot", "bureau": "NOAA", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "Storm Events Knowledge Graph Chatbot", "bureau": "NOAA", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "Grants Program Administration - Inquiry management Chatbot Pilot", "bureau": "NTIA", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "BAS Assist", "bureau": "OS", "problem": "BAS Assist, a generative AI chatbot that supports users of the enterprise BAS applications. BAS Assist provides real-time guidance in natural, conversational language, enabling users to more effectively complete application tasks, resolve common issues, and…", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "Virtual Assistant (Public)", "bureau": "USPTO", "problem": "Chatbot on USPTO 's public-facing website to answer patent and trademark questions from the general public", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "Call Center Automations (Internal)", "bureau": "USPTO", "problem": "Leverage ServiceNow’s AI-based NowAssist to support Tier 1 and Tier 2 service desk staff to support basic IT requests s", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOC", "agency_name": "Department of Commerce", "name": "Pre-Exam Application Processing: Trademark Center (TM Center) AI Automation", "bureau": "USPTO", "problem": "Auto-analyze mark drawing images and suggest associated design search codes, auto-examine the literal element field and generate potential pseudo mark text, auto-examine goods and services to preliminarily assign one or more international classes to an…", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "forms" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "OpenText for Records Management (Email Auto-Classification)", "bureau": "EE HQ - EE Headquarters (EE)", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "forms" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "AI Support Agent Chatbot", "bureau": "EE HQ - EE Headquarters (EE)", "problem": "Manual helpdesk tickets consume staff time and delay issue resolution.", "outputs": "Conversational responses, step-by-step guidance, and links to support documentation.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "OpenText for Records Management (File share auto-classification)", "bureau": "EE HQ - EE Headquarters (EE)", "problem": "Records retention and disposition.", "outputs": "OpenText will categorize each file located on the network drives.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "FY19 Lab Call – Livewire Data Sharing Platform", "bureau": "EE HQ - EE Headquarters (EE)", "problem": "Lack of easily-accessible, organized information on transportation and mobility-related projects.", "outputs": "GenAI chatbot feature providing info on datasets in the platform or FAQs on how to use the platform", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "AI-Based Chat Bot", "bureau": "IM-60 - IM Enterprise Operations and Shared Services (IM)", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "ServiceNow Predictive Intelligence", "bureau": "LANL - Los Alamos National Lab (LAFO)", "problem": "Improve helpdesk efficiency and data quality", "outputs": "Field classification data", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "ServiceNow Virtual Agent", "bureau": "LANL - Los Alamos National Lab (LAFO)", "problem": "Provide chatbot services to help customers resolve issues or open service requests that do not require human intervention", "outputs": "Recommendation", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "LANL AI Portal", "bureau": "LANL - Los Alamos National Lab (LAFO)", "problem": "\"Democratized access to open-source/open-weights Large Language Models (LLMs) for general purpose office productivity, research of AI models, software development, operational streamlining, code development \"", "outputs": "Interactive text chat replies from user prompts, summaries of documents user submitted for Retrieval Augmented Generation (RAG), Replies to API queries from enterprise and scientific applications", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "M365 Copilot", "bureau": "LANL - Los Alamos National Lab (LAFO)", "problem": "Collaboration and Productivity improvements", "outputs": "Contextual responses, action results, agentic orchestration for Copilot Studio, apply templates, and a host of outputs depending on the M365 App it is being used with.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "LISA Chatbot Pilot", "bureau": "LLNL - Lawrence Livermore National Laboratory (LFO)", "problem": "Quick access to specific mission data sets and documentation", "outputs": "Specific, data-driven answers to mission science questions", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "Amazon Q Chatbot Pilot", "bureau": "LLNL - Lawrence Livermore National Laboratory (LFO)", "problem": "Quick access to AWS cloud engineering reference architectures and general AWS services information", "outputs": "Specific AWS architecture and services use-case responses to questions", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "INL AI Virtual Assitant (AiVA)", "bureau": "NE INL - NE Idaho National Laboratory (NE)", "problem": "work productivity", "outputs": "Outputs are consistent with commercial chatbot products, such as ChatGPT.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "EES&T Document Processing", "bureau": "NE INL - NE Idaho National Laboratory (NE)", "problem": "Not available", "outputs": "Information summaries", "stage": "Pilot", "high_impact": false, "technique": "Other", "categories": [ "intake", "review" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "Microsoft AI Builder", "bureau": "NE INL - NE Idaho National Laboratory (NE)", "problem": "Microsoft AI Builder solves the problem that most businesses want to use AI but lack the technical expertise and resources to build it themselves. It provides easy-to-use, pre-built AI tools that business users can implement without needing data scie", "outputs": "Microsoft AI Builder outputs structured business data and automated actions, including extracted information from documents, data predictions, and workflow automations that integrate directly into…", "stage": "Pilot", "high_impact": false, "technique": "Agentic AI", "categories": [ "intake" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "ServiceNow Virtual Agent Natural Language Understanding", "bureau": "NR HQ - Naval Reactors (NR)", "problem": "Aid in users receiving better IT support for incident reporting and service delivery.", "outputs": "Pre-built IT Service Management incident and request templates. The Virtual Agent NLU is only used to understand the users intent and entity, where it then performs a search of the service catalog…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "SpyglassGPT Chat Assistant", "bureau": "NR HQ - Naval Reactors (NR)", "problem": "Aid in routine administrative tasks", "outputs": "Varies based on user prompts; it is a general purpose chatbot using government instances of popular Open AI models.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "Lex Natural Language Interface", "bureau": "NREL - National Renewable Energy Laboratory (EE)", "problem": "Find insights in a database with a LLM summarizing the results", "outputs": "The system executes a query against a postgres database and displays an LLM-generated textual summary of returned query records. The system also displays the LLM generated queries.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "PARSGPT", "bureau": "PM HQ - Office of Project Management (PM)", "problem": "Project Management internal investigations and audits", "outputs": "Free form text response to questions (Chatbot)", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "AI Incubator Sandbox", "bureau": "PNNL - Pacific Northwest National Laboratory (SC43 OIM)", "problem": "Provide a secure, multimodal AI chatbot sandbox for experimentation without internet access.", "outputs": "Produce high-quality, contextually relevant, and coherent responses or content based on user input.", "stage": "Deployed", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "AI Chat Bot for IT User Services", "bureau": "PPPL - Princeton Plasma Physics Laboratory (SC43 OIM)", "problem": "Interactive RAG chat bot model trained on existing, updated and new IT resource documentation in the user space. Platform will be used as an informative method for users to handle tier 1 IT issues and help guide to the correct place.", "outputs": "AI output will be recommendations and instructions based on training data from IT administrators in more user friendly responses", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "AI Chat Bot for Facility Sustainability Practices", "bureau": "PPPL - Princeton Plasma Physics Laboratory (SC43 OIM)", "problem": "", "outputs": "AI output will be recommendations and instructions based on training data from facility data on recycling, trash and composting", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "LexisNexis", "bureau": "RL - Hanford - Richland Operations Office - Hanford (EM)", "problem": "searching legal database for case law", "outputs": "search results to input inquiries", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "Records Digitization", "bureau": "SEPA - Southeastern Power Administration (PMA)", "problem": "Enhance lookup of agency records and electronic documents", "outputs": "Acurate metadata assignment in accordance with SEPA's NARA-approved file plan/records schedule.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "Raytheon Multimedia Monitoring System (M3S)", "bureau": "Y-12 - Consolidated Nuclear Security Y-12 (YFO)", "problem": "Video transcript generation", "outputs": "XML representation of the speech detected in a pre-recorded video", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOE", "agency_name": "Department of Energy", "name": "Cognitive Prescreen Tool (CPT)", "bureau": "Y-12 - Consolidated Nuclear Security Y-12 (YFO)", "problem": "Classification recommendations to assist the Derivative Classifier in making a document's overall classification determination.", "outputs": "Sensitive information detection bound to DOE classification guidance to help reduce IOSC and prevent information loss", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "BIA-GEO Assist", "bureau": "BIA", "problem": "The Branch of Geospatial Support (BOGS) and Regional Geospatial Coordinators (RGCs) offer technical support to BIA and Tribal GIS users that participate in the DOI Enterprise Licensing Agreement (ELA). The BOGS ELA Helpdesk Coordinator and RGCs allocates…", "outputs": "Answers to user questions are generated by summarizing the results obtained from web queries that reference the predetermined web references built into the custom HTML code that was used to create…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "General Land Office Records Modernization", "bureau": "BLM", "problem": "Using optical character recognition and natural language processing, the GLO system will automatically extract text and data elements from scanned land record documents that define mineral and lands ownership. Specifically, publicly available land records…", "outputs": "digitized records / data extraction", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Assessment Inventory and Monitoring (AIM) Chatbot", "bureau": "BLM", "problem": "The AIM program provides access to data from 73,000+ monitoring locations and offers tools, guidance, and workflows to support BLM staff in structured, repeatable data collection and application. While resources enhance decision-making, their volume makes…", "outputs": "Text-based recommendations based on internal guidance documents, monitoring methodologies, and example uses case pertaining to monitoring. The outputs direct the users that the AI responses are…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "GraphRAG Comment Analysis and Management Solution", "bureau": "BLM", "problem": "Comment analysis - identifying and binning comments from the public. Will intake comments from a variety of sources and AI then breaks them down by issue area. Anticipate training that model with NEPA projects, rulemaking, and public comments on a draft…", "outputs": "Analysis, binning, categorization, and responses.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Summarization of documents and output to ECOSphere species workflow [2024 INV#DOI-63]", "bureau": "FWS", "problem": "The ECOSphere species workflow relies on extracting relevant ecological and biological insights from a vast and continuously growing repository of unstructured documents, currently numbering in the millions. Manual review and summarization of these documents…", "outputs": "Structured Summaries of Documents Concise, machine-readable summaries of scientific, regulatory, and technical documents. Key metadata extraction (e.g., species name, habitat, threats, geographic…", "stage": "Pre-deployment", "high_impact": false, "technique": "Other", "categories": [ "summary" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "DocketScope", "bureau": "FWS", "problem": "Facilitate review of public comments.", "outputs": "Produce extractive summaries of comments. Detect general sentiment and requests for comment period extensions or public meeting requests. AI tools are supplemental and intended to aid in efficiently…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "summary", "correspondence" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Atlas.ti (Qualitative Data Analysis Software)", "bureau": "FWS", "problem": "Existing software, such as Excel, was insufficient for the increasing quantity of qualitative data collected, necessitating the need for specialized software geared towards qualitative data analyses.", "outputs": "There are currently no or limited AI outputs as those features are not currently supported. However, future outputs could include automated assessment of themes, sentiment analysis, and…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "ePermits: Chatbot Implementation and Permit Application Wizard Tools", "bureau": "FWS", "problem": "Users face significant difficulties when interacting with the system due to a lack of personalized experiences, delayed and imprecise query responses, poor application form guidance, and an overall unintuitive user interface—resulting in reduced efficiency,…", "outputs": "Natural Language Responses, Intent Recognition & Routing, Dynamic Form Assistance, Knowledge Base Integration, Contextual Follow-ups, Escalation to Human Agent, Multimodal Outputs. We do not use…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Intelligent Optical Character Recognition [2024 INV#WO0000000110733]", "bureau": "IBC", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "intake" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Everlaw AI Assistant for Responsive Review", "bureau": "OCIO", "problem": "Everlaw AI Assistant’s Coding Suggestions lets OTAR and DO set natural-language criteria (case, category, and code descriptions) to assess document responsiveness. By tagging documents using keywords, account numbers, and contextual clues, it reduces manual…", "outputs": "The AI assistant provides one of four coding suggestions: Yes (direct match), Soft Yes (plausible relevance), Soft No (weak relevance), or No (not relevant). DOI reviewers can filter by category,…", "stage": "Pilot", "high_impact": false, "technique": "Agentic AI", "categories": [ "review" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "FBMS UPC Chatbot", "bureau": "OCIO", "problem": "An OIG draft finding identified a significant number of purchase requests where the \"Other\" UPCs were used instead of the IT related UPCs that would trigger an IT Approval. This misclassification undermines OCIO's ability to track IT spending", "outputs": "The correct UPC will be provided to the requestor and used on the Purchase Request in FBMS. Longer term solution would be for the auto generation of the Purchase request with the correct UPC…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Data Extraction Using MS Power Automate AI Functionality [2024 INV#WO0000000110500]", "bureau": "ONRR", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "intake" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Public Comment Analysis Tool (PCAT): Leveraging Artificial Intelligence (AI) for Enhanced Efficiency [2024 INV#WO0000000106351]", "bureau": "OS", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "FOIA Record Pre-Review Tool", "bureau": "SOL", "problem": "Records review is a the resource intensive step of FOIA request processing that would most benefit from AI assistance and optimization.", "outputs": "Spreadsheet of \"Hits\" or items that will be uploaded to the document review platform to queue reviewers attention to information that may fall under a FOIA or Privacy Act Exemption.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "review", "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Document Review Platform Generative AI", "bureau": "SOL", "problem": "FOIA Request Processing is the resource intensive step of the process that would benefit from AI assistance and optimization.", "outputs": "TBD", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "review", "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Microsoft eDiscovery Attorney-Client Privilege Detection", "bureau": "SOL", "problem": "Identifying information subject to a FOIA exemption.", "outputs": "The model provides an attorney-client privilege score and indicates if an attorney is a participant in the document.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "FOIA Request Lexical Similarity Tool in the Document Review Platform (Term Frequency – Inverse Document Frequency (TF-IDF) - Cosine)", "bureau": "SOL", "problem": "Identifying Similar FOIA Requests", "outputs": "Percentage similarity between documents. Developed in house selected because this is not an out of the box function available in the document review platform.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "review", "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Enabling FOIA Request Clustering Capability in the Document Review Platform (Density Based Algorithm alongside Term Frequency – Inverse Document Frequency (TF-IDF))", "bureau": "SOL", "problem": "Identifying Contextually Similar FOIA Requests. Developed in house is selected because while the document review platform is used it is not an out of the box functionality.", "outputs": "Grouped requests into clusters based on TF-IDF weighted vocabulary. Documents with distinctive overlapping terms form conceptual groups. This provides a visualization of requests that are…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "review", "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "FOIA Request Clustering Tool (Embedding based clustering alongside Term Frequency – Inverse Document Frequency (TF-IDF))", "bureau": "SOL", "problem": "Identifying FOIA requests that share lexical and/or semantic similarity.", "outputs": "Cluster Visualization to facilitate identifying similar requests.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "FOIA Request Conceptual Similarity Tool (Semantic Similarity Score Generation)", "bureau": "SOL", "problem": "Identifying contextually similar FOIA requests received by the department.", "outputs": "Semantic Similarity Score", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "Performance Modeling / Performance Forecasting", "bureau": "SOL", "problem": "Estimating the number or requests that can be processed by a FOIA processor or bureau each year.", "outputs": "Expected number of requests to be processed.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "VoiceAtlas no-code chatbot framework", "bureau": "USGS", "problem": "Many business areas in USGS could benefit from AI tools, however do not have technical expertise on staff.", "outputs": "Chatbots with guardrails and knowledge bases that can be created and maintained by non-technical employees.", "stage": "Pilot", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "DOI", "agency_name": "Department of the Interior", "name": "ATLAS", "bureau": "USGS", "problem": "Literature review and data compilation is the most time-consuming phase of the mineral resource assessment workflow (https://www.usgs.gov/media/images/usgs-mineral-resource-assessment-workflow). Identifying and extracting datasets referenced in historical…", "outputs": "A catalog of metadata about published datasets that supports user queries through agentic AI.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Adobe Suite Applications", "bureau": "Department of Justice / ATF", "problem": "The purpose of the AI use case in Adobe Suite products, is to support the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) in enhancing the quality and efficiency of multimedia content handling within their operations. The out-of-the-box artificial…", "outputs": "The AI features within Adobe Suite products output automated enhancements and suggestions to improve the efficiency and quality of multimedia content handling. In applications like Photoshop and…", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake", "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Unmanned Aerial Systems (UAS)", "bureau": "Department of Justice / ATF", "problem": "Airship is an enterprise video management platform used to manage, secure, store, and analyze video surveillance obtained through criminal investigations. The airship platform includes AI-based capabilities for optical character recognition (OCR) for car…", "outputs": "Bounding boxes for video frames and metadata regarding the detected object characteristics, alphanumeric digitized text from OCR", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Azure Data Factory", "bureau": "Department of Justice / ATF", "problem": "ATF recently enabled Azure Data Factory subscription in Azure, which included smart and intelligent features like anomaly detection, data quality analysis, data completeness, and prediction. While we do not use these features directly, they are embedded…", "outputs": "Data extraction transformation/load pipelines used to integrate data from disparate datasets", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Axon Evidence.com", "bureau": "Department of Justice / ATF", "problem": "ATF's use of Axon body-worn cameras and the associated evidence.com service are integral to ensuring ATF's compliance with DOJ policy documented in DOJ OIG, Inspector General Manual, Volume III, Chapter 236, Body Worn Camera Program. Body cam videos are…", "outputs": "Evidence.com AI capabilities will output bounding boxes around heads that are identified in videos, for input to redaction processes. These AI functions are not being operationally used.", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Federal Docket Management System", "bureau": "Department of Justice / ATF", "problem": "This system allows agencies to receive comments that are submitted electronically in response to rulemaking initiatives. The system has features that categorize comments based on text analytics, allows agencies to make those comments public facing or redact…", "outputs": "It is the place where agencies receive comments that are electronically submitted in response to a rulemaking. It allows agencies to make those comments public facing.", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "review", "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Mark43 Public Safety Records", "bureau": "Department of Justice / ATF", "problem": "The purpose of the AI use case in Mark43 Public Safety Records is to organize, categorize, and correlate extensive law enforcement data from various sources. By employing AI-driven classification, entity resolution, and data linking, it reduces the manual…", "outputs": "The AI features categorize documents, flag repeated factors, and highlight patterns hidden without assistance.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "TransUnion TLOxp Online Investigative Services", "bureau": "Department of Justice / ATF", "problem": "The purpose of the AI use case in TransUnion TLOxp Online Investigative Services is to gather and analyze diverse personal, financial, and asset-related data more effectively. By implementing AI for entity resolution, risk scoring, and pattern detection, it…", "outputs": "The AI features highlight key personal details, indicate anomalies, and present data in a structured format for deeper inquiry.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Veritone Redact", "bureau": "Department of Justice / ATF", "problem": "The purpose of the AI use case in Veritone Redact is to support ATF in processing Freedom of Information Act (FOIA) requests by automating the redaction of sensitive information within audio and video files.", "outputs": "The AI system in Veritone Redact outputs recommendations and automated actions to support the redaction of sensitive information within audio and video files. It intelligently identifies personally…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Veritone Illuminate", "bureau": "Department of Justice / ATR", "problem": "Veritone Illuminate is a cloud-based application that provides machine translation and audio and video analysis transcription to aid human review in investigations. The product allows us to leverage artificial intelligence (AI) to systematically turn…", "outputs": "audio/video transcription text face detection (not used) object/scene detection results in images/videos text extraction from images speaker identification in audio pattern recognition results…", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Bloomberg (AI Assisted Legal Research)", "bureau": "Department of Justice / ATR", "problem": "Provides AI-assisted research to help streamline legal research and document review.", "outputs": "Summarization of caselaw", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "review" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "ATR Generative Artificial Intelligence Test", "bureau": "Department of Justice / ATR", "problem": "Facilitate access to AI tools by ATR personnel.", "outputs": "Open-source research, summarizing publicly available documents.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Enabling eDiscovery Platform AI", "bureau": "Department of Justice / CIV", "problem": "Migrate CORA data to Relativity One or Everlaw, cloud based solutions to enable Relativity's and Everlaw's AI tools for review prioritization, privilege review and other advanced eDiscovery utilities. Utilize internal or third party-tools for AI-assisted…", "outputs": "Prioritizations, Classifications, Recommendations: document priority rankings, privilege classifications, review recommendations.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Claims Program Predictive Fraud Analytics", "bureau": "Department of Justice / CIV", "problem": "Deploy advanced AI analytics to detect fraudulent claims and suspicious patterns in compensation claims programs, including the September 11th Victims Compensation Fund, Radiation Exposure Compensation Act, Camp LeJeune Justice Act, and other federal victim…", "outputs": "Predictions, Classifications, Scores: fraud probability scores, claim authenticity classifications, risk alerts.", "stage": "Pre-deployment", "high_impact": true, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "AI-generated Content Detector", "bureau": "Department of Justice / CIV", "problem": "Federal litigators face increasing challenges with AI-manipulated evidence and documents from opposing parties. An AI tool could be used to flag potentially AI-generated content.", "outputs": "Classifications, Predictions: AI-generation probability scores, document authenticity flags, content manipulation alerts, disclosure requirement notifications.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "review" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "AI Powered Data Governance", "bureau": "Department of Justice / CIV", "problem": "This initiative deploys AI-driven data governance and metadata management to auto-tag, catalog, and enforce retention, while identifying duplicate/low-value files. Aligns with M-25-21 governance & E.O. 14179 innovation.", "outputs": "Classifications, Recommendations, Automated Actions: content categorization, retention recommendations, duplicate identification.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "AI Meeting Assistant", "bureau": "Department of Justice / CIV", "problem": "This initiative deploys AI-enabled meeting assistants that provide real-time transcription, generate concise summaries, identify action items, and tag outcomes to case files. Aligns with E.O. 14179’s innovation goals and M-25-21’s efficiency and transparency…", "outputs": "Transcriptions, Summaries, Extractions: meeting transcripts, summary reports, action item lists, outcome tags.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary", "forms", "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Mass Claim Tool", "bureau": "Department of Justice / CIV", "problem": "Components with mass claims, such as RECA must respond to tens of thousands of similar claims within statutorily defined timelines, or the U.S. forfeits its defenses. Tools to quickly process mass claims, including template letter generation, claim…", "outputs": "Content Generation, Classifications, Automation: template letters, claim category assignments, automated data entry, standardized filing generation, quality control flags.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "AI Evidence and Claim Consolidation", "bureau": "Department of Justice / CIV", "problem": "This initiative applies AI to synthesize records, summarize expert reports and depositions, and identify duplicate claims. It also uses AI to identify inconsistencies between records and plaintiff claims, identify red flag legal issues, and create templates…", "outputs": "Summaries, Classifications, Predictions: document summaries, duplicate detection, inconsistency flagging, settlement predictions.", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "summary", "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "ChatBot", "bureau": "Department of Justice / COPS", "problem": "The Chatbot responds to the questions, allowing staff to focus on other priorities.", "outputs": "Response", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Thomson Reuters CLEAR - License Plate Recognition", "bureau": "Department of Justice / CRM", "problem": "CLEAR LPR enables investigators to identify location history for license plates, connect addresses and individuals of interest to a vehicle's location and obtain images of a vehicle. CRM uses CLEAR LPR to aid in investigations.", "outputs": "Object recognition, OCR, prediction", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Veritone", "bureau": "Department of Justice / CRM", "problem": "Improve transcription and translation of written, video, and audio files", "outputs": "For text files that are transcribed into English the output contains the translated text. For Audio and video files a text file transcribing the translation is created as well as a closed captioning…", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Systran Translate Server", "bureau": "Department of Justice / CRM", "problem": "CRM uses Systran Translate Server for machine translation.", "outputs": "Translation", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Camtasia", "bureau": "Department of Justice / CRT", "problem": "Dynamic captions use AI tech within the local client software to convert speech to text and produce a transcript file.", "outputs": "Contemporaneous closed captioning. The transcription saving is disabled.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Dragon", "bureau": "Department of Justice / CRT", "problem": "Use natural language processing to convert spoken audio to text for employees with vision or mobility limitations.", "outputs": "Text transcription of spoken audio. Navigation of laptop operating system.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Evidence.com", "bureau": "Department of Justice / CRT", "problem": "Use the auto-transcribe feature to transcribe body worn camera footage to text making it more easily searchable. Also use the redaction assistant feature to remove sensitive information from videos.", "outputs": "Text transcriptions, video with sensitive images redacted", "stage": "Pre-deployment", "high_impact": true, "technique": "Classical ML", "categories": [ "forms", "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Civil Rights Public Reporting Portal", "bureau": "Department of Justice / CRT", "problem": "With limited staff, we use AI to summarize incoming public complaints to assist in determining if a complaint is actionable by our teams.", "outputs": "Generates report summaries and tags on reports for CRT staff analysis.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "summary" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Automated Count of Items in Photos", "bureau": "Department of Justice / DEA", "problem": "DEA needs a way to validate the drug forms, shapes, sizes, and counts contained in drug seizure exhibits so that this information can be effectively used in court proceedings.", "outputs": "Outputs recommended counts with image labels.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Data & Analytics: Transportation OCR", "bureau": "Department of Justice / DEA", "problem": "DEA needs a reliable, accurate way to identify the cartel or other organization associated with a seized drug exhibit.", "outputs": "The technology can \"fingerprint\" drug seizures of various forms (e.g. packaging, powder, pills). The output is a series of best matches for experienced DEA personnel to evaluate for possible matches…", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Data & Analytics: Chemistry Instrument Library", "bureau": "Department of Justice / DEA", "problem": "DEA needs a way to identify unknown drug samples, which can be done by comparing them against a library of known spectra for the best match.", "outputs": "Outputs a recommended list of best matches to be decided upon by the analyst and included in the case file as supporting evidence for the identified substances reported.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Financial and Cryptocurrency Analysis: Federal Partner", "bureau": "Department of Justice / DEA", "problem": "Intelligence Analysts and DEA agents require tools that enable them to accelerate triage, discovery, investigations, and reporting while preserving the context, control, and credibility necessary to make their intelligence actionable. These tools must also…", "outputs": "Provides summary materials, on-demand briefings from a chatbot, target profiles, and language search results, along with outputs such as recommended search results, link analysis, alerts, and data…", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Financial and Cryptocurrency Analysis: Commercial Solution", "bureau": "Department of Justice / DEA", "problem": "Intelligence Analysts and DEA agents require tools that enable them to accelerate triage, discovery, investigations, and reporting while preserving the context, control, and credibility necessary to make their intelligence actionable. These tools must also…", "outputs": "Provides summary materials, on-demand briefings from a chatbot, target profiles, and language search results, along with outputs such as recommended search results, link analysis, alerts, and data…", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Intelligence Data Platform", "bureau": "Department of Justice / DEA", "problem": "DEA has a mission to protect communities and save lives. It requires a need to understand communications within criminal operations. Multi-language machine transcription of audio files from lawfully seized devices, authorized correctional facility, and other…", "outputs": "Outputs vary by use case.", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Call Center Management and Service Delivery Support", "bureau": "Department of Justice / DEA", "problem": "DEA wants to improve the effectiveness of the Diversion Control Registrant Call Center by automating the routing of incoming calls, providing answers to common questions for Diversion Control Registrant call center agents to identify and quickly propose…", "outputs": "Outputs a recommended course of action for review by DEA staff along with a prioritization classification and provide customer response metrics.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Fingerprint (Friction Ridge) Optical Character Recognition (OCR)", "bureau": "Department of Justice / DEA", "problem": "DEA, when conducting fingerprint analysis to identify individuals who may be connected to evidence, needs to be able to compare friction ridge prints to other prints within the boundaries of a case. Product enables linking of cases where individuals are not…", "outputs": "Outputs images and portions of print cards.", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Extracting Data from Receipts to Speed Travel Reimbursement or Provide Logbook Documentation", "bureau": "Department of Justice / DEA", "problem": "Gas receipts need to be reconciled / logged into FIRM – the AI would capture the data and can be used to record the receipt (reducing loss of paper) and eventually upload into FIRM reducing human data entry and time. IN also inspects receipts as part of…", "outputs": "The output is a CSV file.", "stage": "Pilot", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Chatbot to Answer Internal Employee Policy Queries", "bureau": "Department of Justice / DEA", "problem": "It is often challenging for DEA employees to manually search through our voluminous collection of manuals, books, CBP chemical codes, CFR, U.S.C., etc. to find an answer to their specific questions about policy, law, and rules. Training the AI on these…", "outputs": "The solution will enable users to ask questions through a chatbot interface, where the AI system, trained on the Agents Manual, will generate comprehensive answers and recommendations. These…", "stage": "Pilot", "high_impact": true, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Audio and Written Transcription and Translation", "bureau": "Department of Justice / DEA", "problem": "This technology automates the transcription and translation of Spanish and Mandarin Chinese audio files from lawfully seized devices and authorized communications.", "outputs": "Outputs transcription of the original language and the English translation with speaker differentiations including search results of the predetermined relevant terms defined by the analyst.", "stage": "Pilot", "high_impact": true, "technique": "Classical ML", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Managing Document Digital Signatures", "bureau": "Department of Justice / DEA", "problem": "It is currently time-consuming, and challenging, to collect digital signatures from DOJ personnel into one source document. Utilizing AI would intend to manage the document workflow by using a single upgraded storage location with advanced security features.", "outputs": "DEA/DOJ digital cloud base signatures can be integrated with an existing AI platform/application. AI outputs: AI assisted review, automated tagging, custom extractions, agreement summaries and…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Community Outreach Chatbot that Helps the Public Consume Prevention Resources", "bureau": "Department of Justice / DEA", "problem": "Many citizens may not have the time to explore the copious library of DEA Community Outreach and Prevention Support (CPO) resources. By training AI on these resources, citizens can ask plain language questions and get succinct answers.", "outputs": "1. Delivery of DEA publications (digital, hard copies for individuals, and bulk copies for organizations / events); 2. Connect users with best content fit; 3. Allow users to request DEA…", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Chatbot to Answer Diversion Registrants' Queries About Registration and Compliance Issues", "bureau": "Department of Justice / DEA", "problem": "TOMSS (Technical Operations Management Support System will provide call-deflection to ease the customer interaction burden on call center representatives, registration program specialists, and Diversion Investigators. The AI will provide consistent, accurate…", "outputs": "• Policy-grounded responses to registrant inquiries (text output). • Deflection metrics and analytics (call volume reduction, FAQ trends). • Context-based prompts or redirects to DEA.gov resources.…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Capture and Convert Structured Data from Scanned Case Documents to Support Advanced Analysis and Trend Forecasting", "bureau": "Department of Justice / DEA", "problem": "The Targeting & Special Projects Unit (DOIT) has identified a critical need to capture, convert, and process both structured and unstructured text from scanned case documents to support advanced analysis and trend forecasting. This initiative will leverage…", "outputs": "The anticipated output for this AI use case will include text for documentation and CSV for excel spreadsheet analytical work.", "stage": "Pre-deployment", "high_impact": true, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Analyzing Prescription Monitoring Program Data", "bureau": "Department of Justice / DEA", "problem": "DEA investigators currently rely on manual manipulation of data in spreadsheets to conduct Prescription Monitoring Program (PMP) analysis. This requires 8-10 hours of labor (data extraction, cleaning, cross referencing, narrative synthesis), and, since…", "outputs": "Quantitative data reports and analyses", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "eLitigation Tools", "bureau": "Department of Justice / Department Wide", "problem": "Addresses errors and speed delays found in exclusively manual human review of voluminous electronic information.", "outputs": "Outputs vary by use case.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Digital Forensics", "bureau": "Department of Justice / Department Wide", "problem": "Addresses key problems in processing digital data related to law enforcement including efficiency in processing digital evidence, automating time consuming tasks such as organizing and classifying data, and accuracy in evidence analysis.", "outputs": "Outputs vary by use case.", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "ServiceNow", "bureau": "Department of Justice / Department Wide", "problem": "ServiceNow automates IT helpdesk tickets triage and classification and can handle routine inquires and simple tasks to free up resources to focus on higher priority issues.", "outputs": "Automated service requests Incident resolution recommendations Virtual agency responses Data insights and analytics reports Search results and recommendations Documented and categorized…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake", "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "CoPilot", "bureau": "Department of Justice / Department wide", "problem": "Features assist with calendaring, meeting summaries, and email drafting.", "outputs": "Meeting summaries and proposed email responses.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Veritone", "bureau": "Department of Justice / Department wide", "problem": "Improve transcription and translation of written, video, and audio files", "outputs": "For text files that are transcribed into English the output contains the translated text. For Audio and video files a text file transcribing the translation is created as well as a closed captioning…", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Public Comment Analysis", "bureau": "Department of Justice / Department wide", "problem": "DOJ strives to better serve the American public. For support timely responses following public comments submissions, especially for regulatory missions, this capability will facilitate efficient processing of duplicate and similar comments, while helping…", "outputs": "Data results, comparisons, and analyses for DOJ personnel to review and assess.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "FOIA Production Tools", "bureau": "Department of Justice / Department wide", "problem": "Enhances and streamlines the processing of Freedom of Information Act (FOIA) requests. Automates tasks such as document classification, intelligent identification and redaction of sensitive or confidential information, and deduplication of documents.", "outputs": "Classifications, recommendations, and predictions.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "review", "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Business Intelligence Tools", "bureau": "Department of Justice / Department wide", "problem": "Assists with data discovery and visualization.", "outputs": "Tables, graphs, link-node diagrams, and other visualizations of extracted data.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Qualtrics", "bureau": "Department of Justice / ENRD", "problem": "Qualtrics is a cloud-based service that can send out survey and record responses. ENRD uses it to send out questionnaires to its own employees to gather feedback on issues such as training needs and student feedback on training sessions. ENRD also uses…", "outputs": "Survey responses and reports summarizing data from responses.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Veritone", "bureau": "Department of Justice / ENRD", "problem": "Improve transcription and translation of written, video, and audio files", "outputs": "For text files that are transcribed into English the output contains the translated text. For Audio and video files a text file transcribing the translation is created as well as a closed captioning…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Record Digitization", "bureau": "Department of Justice / EOIR", "problem": "Many agency records, including immigration case files, are paper records. EOIR must digitize paper records into electronic records to comply with federal laws requiring the agency's transition to digital processes. Parties to EOIR immigration proceedings…", "outputs": "Digital agency records of sufficient authenticity, reliability, usability, and integrity to replace the original paper record.", "stage": "Pre-deployment", "high_impact": true, "technique": "Computer Vision", "categories": [ "intake", "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Immigration Hearing Transcription and Translation/Interpretation Services", "bureau": "Department of Justice / EOIR", "problem": "Language translation/interpretation and hearing transcription services for EOIR immigration proceedings are completed manually, requiring significant money and time costs. Current manual processes are slow and labor-intensive and prolong various stages of…", "outputs": "Real-time text translation of languages into English during immigration hearings. Preliminary draft transcripts of immigration hearings for manual review to complete.", "stage": "Pre-deployment", "high_impact": true, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Immigration Case and Filing Content Summary", "bureau": "Department of Justice / EOIR", "problem": "EOIR legal support staff and adjudicators review voluminous filings in EOIR immigration proceedings, sometimes ranging into hundreds of pages for a single filing, and parties to proceedings do not organize content clearly or at all, which hinders review and…", "outputs": "Summaries of court filing contents with references to the source of information within the summary. Tabbing, labeling, and identifying submission types within voluminous court filings. Information…", "stage": "Pre-deployment", "high_impact": true, "technique": "Generative AI", "categories": [ "review", "summary", "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Customer Service AI Agent/ChatBot", "bureau": "Department of Justice / EOIR", "problem": "Members of the public visiting EOIR's website have trouble locating information on the website.", "outputs": "Suggest EOIR webpage where customer can locate the relevant content or information.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Conduit AI", "bureau": "Department of Justice / EOUSA", "problem": "Platform is meant to provide a generative AI that can be used for document review and other use cases as a comparison point to other commercially available alternatives.", "outputs": "Automated transcriptions, metadata tagging suggestions, and object and facial recognition in media files", "stage": "Pilot", "high_impact": true, "technique": "Generative AI", "categories": [ "review", "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Cocounsel AI", "bureau": "Department of Justice / EOUSA", "problem": "Platform is meant to provide a generative AI that can be used for document review and other use cases as a comparison point to other commercially available alternatives.", "outputs": "Automated transcriptions, metadata tagging suggestions, and object and facial recognition in media files", "stage": "Pilot", "high_impact": true, "technique": "Generative AI", "categories": [ "review", "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Azure AI Foundry Platform", "bureau": "Department of Justice / EOUSA", "problem": "This AI solution is meant to provide chatbot responses in a secure enclave. In addition, this platform is meant to provide a generative AI that can be used for document review and other use cases as a comparison point to other commercially available…", "outputs": "Text outputs", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "review", "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "OCR and Translation", "bureau": "Department of Justice / FBI", "problem": "Transcription and translation", "outputs": "Digital text", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake", "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Human Language Extraction and Translation Technology", "bureau": "Department of Justice / FBI", "problem": "Machine translation of documents and digital files", "outputs": "Extracted and transcribed / translated text from documents and digital files", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Language Translation, Optical Character Recognition (OCR), Object Detection, Language Detection, Alert Noise Reduction", "bureau": "Department of Justice / FBI", "problem": "Autonomous Detection and Monitoring", "outputs": "Capabilities to support data and analytics, data synthesis, filtering, and linking of open source & threat intelligence.", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "intake", "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Transcription", "bureau": "Department of Justice / FBI", "problem": "Time intensive manual transcription", "outputs": "Form with transcribed text", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Symphony AD-Hoc Batch Processing", "bureau": "Department of Justice / FBI", "problem": "Translation, transcription, and summarization tool for language processing", "outputs": "Transcribed and translated language", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Redaction Tool 2", "bureau": "Department of Justice / FBI", "problem": "Time-intensive manual tasks", "outputs": "Suggested redactions", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Redaction Tool 1", "bureau": "Department of Justice / FBI", "problem": "Time-intensive manual tasks", "outputs": "Suggested redactions", "stage": "Pilot", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Administrative Chatbot", "bureau": "Department of Justice / FBI", "problem": "Time intensive responses to questions", "outputs": "Answers to questions", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Policy Chatbot", "bureau": "Department of Justice / FBI", "problem": "Chatbot for policy", "outputs": "Location of user manuals and documentation", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Optical Character Recognition Tool 3", "bureau": "Department of Justice / FBI", "problem": "Automating administrative tasks", "outputs": "Text data", "stage": "Pilot", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Optical Character Recognition Tool 1", "bureau": "Department of Justice / FBI", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Optical Character Recognition Tool 2", "bureau": "Department of Justice / FBI", "problem": "Digitization of data", "outputs": "Text", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Entity Extraction and Summarization", "bureau": "Department of Justice / FBI", "problem": "Data triage", "outputs": "Text", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Data Triage and Processing", "bureau": "Department of Justice / FBI", "problem": "Transcription, translation, summarization, and object detection in audio and video", "outputs": "Text", "stage": "Pilot", "high_impact": false, "technique": "Computer Vision", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Chatbot", "bureau": "Department of Justice / FBI", "problem": "General AI assistance", "outputs": "Multimodal output", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "AI Chatbot", "bureau": "Department of Justice / FBI", "problem": "Time intensive review of policy content", "outputs": "Text with citations", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Google Translate", "bureau": "Department of Justice / FBOP", "problem": "FBOP employees have periodically used Google Translate to translate documents and notifications for the Spanish speaking population from English to Spanish.", "outputs": "Google translate provides a word for word translation of the information entered though it does not always accurately translate in a given context.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "reCAPTCHA", "bureau": "Department of Justice / FBOP", "problem": "Uses reCAPTCHA to distinguish between a human and bot request.", "outputs": "Decision: It determines if the request is coming from a human or bot to allow the request to be submitted via email to the FOIA office or through the Inquiry Portal.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence", "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "UFMS ChatBot", "bureau": "Department of Justice / JMD", "problem": "Create a personal assistant to provide application-level support to Unified Financial Management System (UFMS) users based on their functional needs/tasks.", "outputs": "Formatted text response to specific user questions.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Internal Finance ChatBot", "bureau": "Department of Justice / JMD", "problem": "Create a personal assistant to provide application-level support to Workiva users based on their functional needs/tasks.", "outputs": "Formatted text response to specific user questions.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Machine Learning for Decision Support for Fusion Desktop", "bureau": "Department of Justice / JMD / OCDETF", "problem": "To take into account various criteria such as analyst workloads, similarities in case narratives and targets, and regional factors to suggest the optimal routing and assignment for product requests.", "outputs": "Suggested assignment / routing for HSTF NCC Product Requests (e.g. to an analytic unit or specific analyst). Suggested approver assignments based on referenced agency data.", "stage": "Pre-deployment", "high_impact": true, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Narrative Analysis for Fusion Desktop", "bureau": "Department of Justice / JMD / OCDETF", "problem": "Will create summaries or precis from large volumes of text narratives submitted on Fusions Desktop forms, for both information triage and trend analysis / emerging threat detection purposes. Gen AI would also allow interactive queries (e.g. you ask it…", "outputs": "Summary of long narrative text (on a per form basis). Summary of trends common across multiple forms' narrative texts.", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Narrative Analysis for DOJ MIS", "bureau": "Department of Justice / JMD / OCDETF", "problem": "Will create summaries or precis from large volumes of text narratives submitted on HSTF forms, for both information triage and trend analysis / emerging threat detection purposes. Gen AI would also allow interactive queries (e.g. you ask it questions) about…", "outputs": "Summary of long narrative text (on a per form basis). Summary of trends common across multiple forms' narrative texts.", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Speech to Text Managed Service - Voice Transcription to Text", "bureau": "Department of Justice / OIG", "problem": "Reducing turnaround times for transcribed services and to reduce the need for contracted transcription services of audio files", "outputs": "Speech-to-text recognition of spoken content within an audio file. In addition, a summary of the transcribed content can be created.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Internal Component-specific chatbot service", "bureau": "Department of Justice / OIG", "problem": "Provide human-like conversational responses to conduct general information queries and suggestions towards improving text without feeding into a commercially exposed AI model.", "outputs": "The AI provides generative AI human-like responses/answers to questions and/or statements from a user. In addition, the output can be a generated summary of an inputted excerpt or publicly available…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Dragon", "bureau": "Department of Justice / OIG", "problem": "Automated word processing dictation and speech transcription. The application is speech recognition software that initiates commands on a device or takes dictation into a word processing application. The application assists individuals with reasonable…", "outputs": "Transcription of dictated words into a word processing document or initiation of macro commands in the Windows operating system environment.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Nlets Nationwide License Plate Reader Pointer Index", "bureau": "Department of Justice / OIG", "problem": "Law enforcement usage of Optical Character Recognition (OCR) to assist with license plate reading under DOJ Policy. LPR data is managed and maintained by other entities for law enforcement purposes.", "outputs": "JWIN facilitates access to the Nlets Nationwide License Plate Reader Pointer Index, which provides access to states and/or federal agencies that maintain their own LPR repositories. LPR data is…", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Axon", "bureau": "Department of Justice / OIG", "problem": "No AI utilized. But, the use case captures raw video and audio footage that are accessed only to the extent needed for prosecution or investigation purposes. AI functionalities for analysis, voice transcription, and redaction are not utilized or relevant for…", "outputs": "No AI system outputs. Standard outputs are archival raw videos and audio footage.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "forms", "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Voicemail Transcription, Translation and Summarization", "bureau": "Department of Justice / PARDON", "problem": "PARDON would like to leverage transcription, translation and summarization services available in the GCC high cloud environment to help reduce the processing time and level of effort associated with responding to voicemail inquiries.", "outputs": "The primary output from this use case is an email that provides an AI generated summary of the voicemail and includes the following attachments: the original voicemail (.wav file), a text file that…", "stage": "Pilot", "high_impact": true, "technique": "Generative AI", "categories": [ "correspondence", "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "ProLaw", "bureau": "Department of Justice / PRAO", "problem": "ProLaw assists PRAO attorneys in searching a large quantity of prior PRAO inquiries and advice (stored within the database) to identify relevant historical inquiry files that will assist the attorneys in determining how PRAO has advised on similar matters in…", "outputs": "ProLaw's output is information. Specifically, PRAO uses ProLaw to identify all of the digital inquiry files in the database that are consistent with the user's selected search query.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "AWS Transcribe", "bureau": "Department of Justice / TAX", "problem": "Creates machine transcription of audio or video to facilitate review and evaluation of evidence.", "outputs": "Translated text", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "AWS Translate", "bureau": "Department of Justice / TAX", "problem": "Creates machine translation of foreign language documents to facilitate review and evaluation of evidence.", "outputs": "Machine translated text", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "JAWS (Text-to-Speech Assistant for Accessibility)", "bureau": "Department of Justice / TAX", "problem": "Visually impaired personnel need assistance interpreting pictures and other visual objects and interacting with documents in a non-linear way", "outputs": "Audio descriptions of images and summaries of text", "stage": "Deployed", "high_impact": true, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Document Processing", "bureau": "Department of Justice / TAX", "problem": "Use natural language processing to convert spoken audio to text for employees with vision or mobility limitations.", "outputs": "Structured data files, including spreadsheets", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Big Data Visualization Tools", "bureau": "Department of Justice / TAX", "problem": "Improving analytics and support for document review", "outputs": "Machine learning text, images, and diagrams", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Axon Video Retention Solution (VRS) - object recognition and redaction", "bureau": "Department of Justice / USMS", "problem": "The Object Recognition AI capabilities present in the Axon Evidence Redaction suite creates first-pass results for potential desired redactions of individual's faces, license plates and computer terminals. The AI is intended to reduce manual work effort and…", "outputs": "Draft video file with USMS-selected desired redaction areas (faces, license plates or computer terminals) Note: Redacted file is not complete until human intervention validates and/or corrects AI…", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "forms" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "UiPath OCR activity", "bureau": "Department of Justice / USMS", "problem": "Errors and delays in manual data interpretation and data entry impact critical events in customer journeys and business process efficiency. UiPath states that it is an automation software that has intelligent document creating capabilities to replace manual…", "outputs": "Extract and interpret data from a wide range of document types and formats, including images, PDFs, handwriting, signatures, checkboxes, and tables. It is designed to process documents…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Video Transcription Tool", "bureau": "Department of Justice / USMS", "problem": "USMS uses open source natural language processing technologies and python code to transcribe an audio or video file into plain text.", "outputs": "Transcription NLP algorithm", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Axon Video Retention Solution (VRS) - Transcription", "bureau": "Department of Justice / USMS", "problem": "The Transcription AI capabilities present in the Axon Evidence Digital Evidence Management System creates first-pass results for transcription of spoken language in video and audio files into text. The AI is intended to reduce manual work effort and reduce…", "outputs": "Draft transcribed text associated to video/audio file being transcribed. Note: Transcription is not complete until human intervention validates and/or corrects AI suggestions.", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "UiPath Document Understanding", "bureau": "Department of Justice / USMS", "problem": "Reduce errors and delays in manual data interpretation and data entry. Potential for significant time savings, additional security and reliability since staff are not working through multiple applications to get the same task done.", "outputs": "Extract and interpret data from a wide range of document types and formats, including images, PDFs, handwriting, signatures, checkboxes, and tables.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "Translation tool", "bureau": "Department of Justice / USMS", "problem": "Provide USMS with the tools that can be integrated with USMS and USNCB data stores such as email content and other files for multiple language translated to English and vice versa.", "outputs": "Translated text", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DOJ", "agency_name": "Department of Justice", "name": "AWS Textract", "bureau": "Department of Justice / USMS", "problem": "AWS Textract would be used to read the content off of certain PDF's sent into USMS by partner agencies. It will prepopulate screens for the user to review against a mailed in PDF.", "outputs": "OCR/ Scanned Data from PDFs", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "NLP Processing tool for ILAB", "bureau": "CEO-ILAB", "problem": "The Natural Language Processing tool is intended to be a conversational query and synthesis tool that summarizes and synthesizes evaluation and technical assistance reports, as well as responses to requests for public comments, and queries information…", "outputs": "The tool responds to queries by providing information summarized from reports and other documents.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Call Recording Analysis", "bureau": "EBSA", "problem": "Automatic analysis of recorded calls made to Benefits Advisors in the DOL Interactive Voice Repsonse (IVR) center. AI is used for Trend analysis and Sentiment Analysis", "outputs": "The output is a transcription of calls. The AI is only doing the transcriptions", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "EBSA Case File Summarization", "bureau": "EBSA", "problem": "Identify and describe electronic documents received by EBSA investigators for formal investigations - save time by automating the first phase of the document review process", "outputs": "Generation of a document's formal title and description of the document's contents", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "review", "forms" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Language Translation", "bureau": "OCIO", "problem": "Eliminates language barriers by translating documents and communications across multiple languages. Improves access to services and information for non-English-speaking stakeholders", "outputs": "Translation", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Electronic Records Management", "bureau": "OCIO", "problem": "Meeting NARA metadata standards for (permanent) federal documents by using AI to identify data within the document, and also using NLP to classify and summarize documents.", "outputs": "NLP Generated Summaries, AI classfication of documents", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "forms" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Note Taking Bot", "bureau": "OCIO", "problem": "The bot will take the transcript and summarize the meeting notes", "outputs": "The output will be a transcript of the meeting and from the transcript summary and action items", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Workforce Recruitment Program (WRP) Website Chatbot Assistant", "bureau": "ODEP", "problem": "This use case cuts down on the number of emails that ODEP’s WRP team needs to respond to in their program inbox, wrp@dol.gov. It helps address routine questions with straightforward answers without requiring WRP staff involvement. It also provides much…", "outputs": "Generate content from the WRP program", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "PII Redaction", "bureau": "OSHA", "problem": "Using Amazon Web Services Personal Identifying Information scrubber for ITA text fields and Named Entity Recognition to remove additional names from the text fields.", "outputs": "Detection/Extraction", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Website Chatbot Assistant", "bureau": "OTAA", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Claims Document Processing", "bureau": "OWCP", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "intake" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Automatic Document Processing", "bureau": "OWCP", "problem": "The AI is intended to allow the claims examiner more time to focus on more critical adjudication tasks. The CM-929 and CM-908 forms amount to tens of thousands of incoming correspondence on an annual basis. Claims examiners were cumulatively spending an…", "outputs": "AI tool will extract data from the forms for further processing", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "correspondence" ] }, { "agency": "DOL", "agency_name": "Department of Labor", "name": "Audio Transcription", "bureau": "OWCP||VETS||WHD", "problem": "The AI is intended to ensure accurate records of voicemails for tracking and follow-up purposes on claims. The transcription service allows more time for the claims examiner to focus on more critical tasks such as decisions on claims as opposed to spending…", "outputs": "Text transcription", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Enterprise \"Ask Dottie\" ChatBot Capability", "bureau": "CAIO, HASS, Volpe", "problem": "Secure enterprise AI-enabled question and answer ChatBot capabilities for natural language querying of documents and data.", "outputs": "Provides the Department with secure enterprise-wide LLM ChatBot capabilities that can be tailored to specific use cases.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Financial Policy LLM Document Search", "bureau": "FAA AFN", "problem": "The Financial Policy Chatbot (AI system) will search a variety of financial policy documents to answer users' financial policy questions, and more importantly, will link to the document(s) where it found the answer. The the chatbot will initially be used", "outputs": "The Financial Policy Chatbot (AI system) will search a variety of financial policy documents to answer users' financial policy questions, and more importantly, will link to the document(s) where it…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "AI Freedom of Information Act (FOIA) Request Classification", "bureau": "FAA AFN", "problem": "Will strengthen the Intake and Assignment Branch's precision of FOIA request assignments at the first take, freeing up time to actively engage with requesters and customers alike. Overall, these FOIA tools will allow for a more informed decision-making p", "outputs": "The output will provide guidance for the FOIA Xpress end user in determining the routing assigment to the approprioate LOB/Organization.", "stage": "Pre-deployment", "high_impact": false, "technique": "Other", "categories": [ "forms" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Court Reporting and Testimony Management Platform", "bureau": "FAA AGC", "problem": "This technology will provide solutions for remote, hybrid, and in-person proceedings that will include both transcription and translation services.", "outputs": "The FAA Office of General Counsel (AGC) personnel would receive detailed summaries for both rough and certified transcripts from a variety of proceeding depositions, arbitrations, expert interviews,…", "stage": "Pre-deployment", "high_impact": false, "technique": "Other", "categories": [ "translation" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Pilot-Controller Voice to Text Transcription", "bureau": "FAA AJO", "problem": "To transcribe recorded radio communications between pilots and air traffic controllers operating within the national airspace.", "outputs": "Text transcriptions and speaker role (pilot/controller)", "stage": "Pre-deployment", "high_impact": false, "technique": "Other", "categories": [ "translation" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "FAA Orders and associated supplemental change notices LLM Document Search", "bureau": "FAA AJO", "problem": "The intended purpose of the AI chatbot is to streamline the retrieval of information from multiple FAA Orders and change notices. The expected benefits include saving time, improving accuracy in finding specific citations, and reducing manual effort for u", "outputs": "The chatbot outputs specific citations and relevant sections from multiple FAA Orders and change notices, which provides quick access to information based on user queries. It delivers clear and…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Community Engagement Chat Bot", "bureau": "FAA APL", "problem": "Assisting the public in finding information on the FAA website.", "outputs": "We are using our AI Chat Bot to better inform the public. In our efforts to engage with the public we wanted to use this tool to direct the public to the tremendous amount of information that is on…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Improve and Speed up the Certification Service Oversight Process Using Intelligent Document Review", "bureau": "FAA AVS", "problem": "Before digitization: * The Certification Services Oversight Process (CSOP) is a manually intensive paper process * Requires the public and small businesses to provide input for Certification requests * No external applicant tracking which results in p", "outputs": "\"Before digitization: * The Certification Services Oversight Process (CSOP) is a manually intensive paper process * Requires the public and small businesses to provide input for Certification…", "stage": "Pre-deployment", "high_impact": false, "technique": "Other", "categories": [ "intake", "review" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Civil Aviation Registry Electronic Services (CARES) LLM Document Search", "bureau": "FAA AVS", "problem": "CARES will use AI (Azure chatbot) to scan thousands of pages and generate an accurate answer. The benefits will be a faster and deeper understanding of the business requirements so the FAA can speed up our development.", "outputs": "The Azure chatbot will find the information requested and output the information in a clear, easy to understand answer.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Public Comment Analyzer (PCA)", "bureau": "OST", "problem": "The business goal of this project is to use a powerful language model to analyze public comments. The model will: • Categorize comments into different topics • Detect the sentiment (positive, negative, or neutral) of the comments • Generate summaries of the…", "outputs": "Data is available on Regulations.gov.", "stage": "Deployed", "high_impact": false, "technique": "Agentic AI", "categories": [ "review", "summary", "correspondence" ] }, { "agency": "DOT", "agency_name": "Department of Transportation", "name": "Conversational Multi-modal Chatbot (Google Gemini)", "bureau": "OST", "problem": "DOT needs a large language model that can integrate with word processing, email, presentations and shared drives.", "outputs": "", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "EAC", "agency_name": "Election Assistance Commission", "name": "Routing emails from shared inboxes", "bureau": "All EAC Divisions", "problem": "This EAC uses a single inbox for the majority of its public communications. This tool is intended to route those incoming messages to the appropriate individuals or inboxes immediately to decrease response time and improve custormer engagement.", "outputs": "Emails forwarded to the appropriate individual or inbox.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "EAC", "agency_name": "Election Assistance Commission", "name": "AI Answers in Clearinghouse Network", "bureau": "Clearinghouse Division", "problem": "AI Answers is a chatbot integrated into the Civic Roundtable platform that allows election officials to query AI about discussions and resources within the Clearinghouse Network. It provides a natural language method for navigating community content without…", "outputs": "Natural-language answers, summaries of discussions, and links to on-platform documents/resources.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "ED", "agency_name": "Department of Education", "name": "Aidan Chat-bot", "bureau": "Federal Student Aid", "problem": "Manages programs authorized under the Higher Education Act of 1965 including financial aid for students attending higher education programs.", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "ED", "agency_name": "Department of Education", "name": "Generative AI - Information Summarization", "bureau": "Office of Postsecondary Education", "problem": "Works to strengthen the capacity of colleges and universities to promote reform, innovation and improvement in postsecondary education, promote and expand access to postsecondary education and increase college completion rates for America’s students.", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "summary" ] }, { "agency": "ED", "agency_name": "Department of Education", "name": "Generative AI - Information Summarization", "bureau": "Office of Special Education and Rehabilitee Services", "problem": "To improve early childhood, educational, and employment outcomes and raise expectations for all people with disabilities, their families, their communities, and the nation.", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "summary" ] }, { "agency": "ED", "agency_name": "Department of Education", "name": "Generative AI - Information Summarization", "bureau": "Office of the General Counsel", "problem": "Provides legal assistance to ED, including the provision of legal advice, litigation, and legislative services, and is responsible for performing certain law-related management functions, including managing the Department’s ethics program.", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "summary" ] }, { "agency": "ED", "agency_name": "Department of Education", "name": "Speech to Text Meeting Transcription", "bureau": "Office of the Under Secretary", "problem": "Coordinates policies, programs, and activities related to Postsecondary Education (OPE), Career, Technical Education, Adult Education (OCTAE), The Office of the Chief Economist, and Federal Student Aid (FSA).", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "translation" ] }, { "agency": "EPA", "agency_name": "Environmental Protection Agency", "name": "OPP Document Data Extraction", "bureau": "OCSPP", "problem": "Manual staff data entry of values and phrases currently only available by opening and reading each document", "outputs": "Document type classification and metadata extraction", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "EPA", "agency_name": "Environmental Protection Agency", "name": "Microsoft Power Automate (custom document processing model)", "bureau": "OFA", "problem": "Saving time and money by extracting only useful information from lengthy utility bills and putting it into an easy to read format automatically.", "outputs": "AI extracts relevant data from lengthy documents.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "EPA", "agency_name": "Environmental Protection Agency", "name": "Helpdesk chat assistant for common data system tasks", "bureau": "OFA", "problem": "The Chat Bot implementation increases Help Desk tier 0 resolution, resulting in higher customer satisfaction and better use of Help Desk resources.", "outputs": "IT solutions recommendations", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "EPA", "agency_name": "Environmental Protection Agency", "name": "RelativityOne Gov aiR", "bureau": "OFA", "problem": "aiR for Review is designed to perform an initial first-pass review in legal case or other document review, such as a FOIA matter. Relativity's \"aiR for Review\" identifies potentially relevant documents, describes why they are relevant using natural language,…", "outputs": "Relativity's \"aiR for Review\" identifies potentially relevant documents, describes why they are relevant using natural language, and demonstrates this relevance using citations from the document.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "review", "forms" ] }, { "agency": "EPA", "agency_name": "Environmental Protection Agency", "name": "AI Rules", "bureau": "OFA", "problem": "To speed up the time it takes to manually read and process public comments on proposed rules.", "outputs": "AI Rules processes public comments and identifies substantive material that requires response and save those as snippets. Snippets are categorized by sender and theme for review. Snippets are also…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "EPA", "agency_name": "Environmental Protection Agency", "name": "Agency Records Management System", "bureau": "OFA", "problem": "reduce burden on staff when identifying appropriate records schedules", "outputs": "records schedule prediction", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "EPA", "agency_name": "Environmental Protection Agency", "name": "Summarize and draft response to comments", "bureau": "Region 8", "problem": "summarize large volume of comments reproducibly", "outputs": "summary text", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "FCC", "agency_name": "Federal Communications Commission", "name": "SharePoint Copilot for Workflow Automation", "bureau": "Office of Engineering and Technology (OET)", "problem": "Enables staff to efficiently manage and organize SharePoint content by automating repetitive tasks such as document tagging, summarization, and permission updates, reducing manual effort and improving consistency across teams.", "outputs": "(1) Automatically tags and summarizes documents stored in SharePoint libraries; (2) Updates permissions and metadata based on user input or predefined rules; and (3) Generates structured content…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "FCC", "agency_name": "Federal Communications Commission", "name": "Comment Legal Analysis & Review Assistant", "bureau": "Wireless Telecommunications Bureau (WTB)", "problem": "Manually reviewing and analyzing large volumes of public comments, which is time-consuming, inconsistent, and resource-intensive for legal teams by automating the identification of key legal themes, sentiment, and duplicate content to support faster, more…", "outputs": "(1) Summarizes public comments to identify sentiment, key themes, and stakeholder positions across submissions; (2) Links relevant legal context—such as statutes and FCC rulings to help staff assess…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary", "correspondence" ] }, { "agency": "FCC", "agency_name": "Federal Communications Commission", "name": "Automated Attachment Intelligence for FCC Licensing Systems", "bureau": "Wireless Telecommunications Bureau (WTB)", "problem": "The AI system is designed to solve the challenge of extracting and organizing critical information embedded in attachments submitted across FCC licensing systems. Currently, staff must manually search through multiple systems and thousands of documents to…", "outputs": "The AI system outputs structured data extracted from document attachments, including named entities such as company names, individuals, and locations. These entities are categorized, normalized, and…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "FCC", "agency_name": "Federal Communications Commission", "name": "ECFS Comment Analytics Deduplication Services (CADS)", "bureau": "Wireline Competition Bureau (WCB)", "problem": "Solves the challenge of efficiently analyzing large volumes of public comments by automating sentiment analysis, deduplication, and summarization.", "outputs": "(1) Classifies public comments and filings by sentiment (pro, neutral, against); (2) Identifies and removes duplicate and near-duplicate submissions using clustering algorithms; and (3) Generates…", "stage": "Retired", "high_impact": false, "technique": "NLP", "categories": [ "summary", "correspondence" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "Data Extraction from Contract PDF Files", "bureau": "Division of Administration", "problem": "Reduce the time to extract specific information from electronic forms, saving time and reducing errors from this manual process.", "outputs": "The output will be a spreadsheet that flags any contract actions that do not meet FDIC procurement standards- specifically wherein the contracting officer signs after the effective date.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "AI Assisted Data Collection", "bureau": "Division of Depositor and Consumer Protection", "problem": "The current process to extract structured data from bank provided PDF documents of consumer loan applications takes FDIC personnel a significant amount of time to manually transcribe data from PDF loan applications, leading to delays, high error rates, and…", "outputs": "The output is a data file of extracted data (csv, text file, json file) from consumer credit reports required to conduct examination analytics for compliance and fair lending reviews.", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake", "translation" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "CFOO Admin Chatbot", "bureau": "Division of Finance", "problem": "Compiling data from internal FDIC website and targeted documents to answer questions about CFOO admin. The AI solution is intended to increase user access to already available data in a more digestible form. This helps internal employees self solve problems…", "outputs": "An answer to the users question the user asks the bot. This addresses topics on internal division administrative questions including office maintenance, training, HR questions, policy, and security.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "CFOO Travel Chatbot", "bureau": "Division of Finance", "problem": "Compiling data from internal FDIC website and targeted documents to answer questions about FDIC travel. The AI solution is intended to increase user access to already available data in a more digestible form. This helps internal employees self solve problems…", "outputs": "An answer to the users question the user asks the bot. This addresses internal agency questions on travel including directives, policy, training, limits, and alerts.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "Business Data Extraction from Loan Files", "bureau": "Division of Resolutions and Receiverships", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "intake" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "Data Extraction from Security Prospectus", "bureau": "Division of Resolutions and Receiverships", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "intake" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "Trust Document Entity Extraction", "bureau": "Division of Resolutions and Receiverships", "problem": "Currently, claims specialists spend countless hours manually reviewing trust agreements to extract grantor/settlor and beneficiary information. Implementing OCR and Llama to automate key data extraction from these agreements will significantly make the…", "outputs": "The extracted data, which will include key data elements, such as grantor/settler and beneficiaries from Trust Agreements, along with the original Trust Agreement will be presented to users through…", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "Trust Document Entity Extraction - LLM", "bureau": "Division of Resolutions and Receiverships", "problem": "The current trust document review process is labor intensive and may require further reviews by legal experts, hence, prolonging the processing time. By leveraging the use of a Large Language Model to automate the extraction of key information from trust…", "outputs": "By using the LLM, the extracted data will include key data elements, such as grantor/settler and beneficiaries from Trust Agreements along with the original Trust Agreement, which will be presented…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake", "review" ] }, { "agency": "FDIC", "agency_name": "Federal Deposit Insurance Corporation", "name": "Transcribing Structured Interviews", "bureau": "Division of Risk Management Supervision", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "translation" ] }, { "agency": "FERC", "agency_name": "Federal Energy Regulatory Commission", "name": "Summarization & Policy Analysis for Regulatory Comments (originally Leverage AI in the Rulemaking Process Use Case)", "bureau": "FERC", "problem": "Addresses duration, efficiencies, and accuracies in initial comment analysis.", "outputs": "1.Groups public comments by topic and identifies the overall sentiment: positive, negative, or neutral toward each issue. 2.Provides interactive summaries and visualizations that help analysts…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "FERC", "agency_name": "Federal Energy Regulatory Commission", "name": "Gas Blanket Certificates (Permitting)", "bureau": "FERC", "problem": "It is designed to streamline the classification, tracking, and summarization of filings and issuances related to blanket certificates, reducing manual workload and improving consistency across document types.", "outputs": "The system generates categorized summaries of filings and issuances, flags sensitive content, and produces searchable metadata tags that support downstream reporting and compliance reviews.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "FHFA", "agency_name": "Federal Housing Finance Agency", "name": "Public Comments Summarization and Topic Classifications Pilot Project", "bureau": "DBR", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "FHFA", "agency_name": "Federal Housing Finance Agency", "name": "Mathematical formula transcription using Commonlook Online", "bureau": "OCIO", "problem": "Commonlook Online is used to translate complex mathematical formula into plain language for publicly released documents.", "outputs": "Commonlook Online is used to translate complex mathematical formula into plain language for publicly released documents.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "FRB", "agency_name": "Federal Reserve Board", "name": "PDF Optical Character Recognition (Text)", "bureau": "Division of Information Technology", "problem": "Automate extraction of structured column data from unstructured PDF reports to reduce manual processing time and errors", "outputs": "The list of the appropriate column names in text format", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "FRB", "agency_name": "Federal Reserve Board", "name": "PDF Optical Character Recognition (Images)", "bureau": "Division of Information Technology", "problem": "PDF OCR (Images) – Extract textual information from images embedded within PDF documents for comprehensive document analysis", "outputs": "The AI system's output is the image details in text format for further analysis", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "FRB", "agency_name": "Federal Reserve Board", "name": "Comment Review System", "bureau": "Division of Information Technology", "problem": "Manage, analyze, and categorize large volumes of public comments", "outputs": "To assist the analyst in reviewing each comment, CRS uses traditional machine learning natural language processing (NLP) to provide text summarization, text matching with lists of topics, entity…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "FRB", "agency_name": "Federal Reserve Board", "name": "Virtual Benefits Assistant", "bureau": "Division of Management", "problem": "Virtual Assistant is designed to answer questions on how to navigate the site and answer general questions", "outputs": "The system provides general responses and employees have the option to speak to a representative if they have more detailed questions.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "FRB", "agency_name": "Federal Reserve Board", "name": "Body Worn Cameras Data Management System", "bureau": "Office of the Inspector General", "problem": "Body Worn Cameras Data Management – Ensure proper handling of information in body camera footage while making content searchable", "outputs": "Text versions of spoken word", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "forms", "translation" ] }, { "agency": "FRB", "agency_name": "Federal Reserve Board", "name": "Proposals and Public Comments", "bureau": "Office of the Secretary", "problem": "Efficiently process large volumes of public regulatory comments while ensuring proper handling of sensitive information", "outputs": "The Board’s processing of comments may use artificial intelligence (AI) to provide more efficient processing of public comments (e.g., PII redaction recommendations, sentiment analysis, text…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence", "forms" ] }, { "agency": "FTC", "agency_name": "Federal Trade Commission", "name": "Relativity Integrated Veritone", "bureau": "BC/BCP/OGC", "problem": "Manual document translation and audio file transcription is time consuming and of inconsistent level of accuracy.", "outputs": "Translated documents and audio transcripts", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "FTC", "agency_name": "Federal Trade Commission", "name": "Chatbot", "bureau": "BCP/DCRO", "problem": "Assisted chat is only available during business hours. Many consumers are left with no option to wait until assisted chat is available if they have questions.", "outputs": "Automated chat responses for ReportFraud.ftc.gov and IdentifyTheft.gov", "stage": "Retired", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "FTC", "agency_name": "Federal Trade Commission", "name": "Analytic Sandbox", "bureau": "BCP/DCRO", "problem": "Provides a managed data analytics platform to help FTC staff perform data analytics tasks supporting various business purposes, by utilizing Python and other tools, with ML (Machine Learning) capabilties and reporting capabilities to create interactive…", "outputs": "Providing a data analytics platform for FTC Data Analytics to perform data analysis and respond to questions from various departments within the agency.", "stage": "Deployed", "high_impact": false, "technique": "Other", "categories": [ "correspondence" ] }, { "agency": "FTC", "agency_name": "Federal Trade Commission", "name": "CRC Call Summarization", "bureau": "BCP/DCRO", "problem": "The process of summarizing consumer complaints relies heavily on the subjective judgment of CSRs (Consumer Service Representative) to identify and document the key points of each case.", "outputs": "Assist with summarizing call data while ensuring PII redaction for safeguarding sensitive information.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "forms" ] }, { "agency": "FTC", "agency_name": "Federal Trade Commission", "name": "IVR Automated Voice Assistant", "bureau": "BCP/DCRO", "problem": "Reduce cost of call volume for requests for information which are low value contacts for law enforcements while maintaining positive customer experience.", "outputs": "Automated IVR voice responses to consumer inquiries.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "FTC", "agency_name": "Federal Trade Commission", "name": "DNC IVR Audio File Transcription", "bureau": "BCP/DCRO", "problem": "The DNC IVR (Do Not Call Interactive Voice Response) comments audio files are not being transcribed, requiring users to click the link and listen to understand the IVR complaint.", "outputs": "Transcribed DNC IVR (Do Not Call Interactive Voice Response) Audio file and save the text to database.", "stage": "Retired", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "FTC", "agency_name": "Federal Trade Commission", "name": "Microsoft 365 Copilot", "bureau": "OCIO", "problem": "Staff performing manual, time-consuming activities, taking away focus from higher-value tasks.", "outputs": "Write, edit and summarize documents and project plans in Word, Excel, PowerPoint and Teams; Draft emails, chats and documents; Receive Outlook Coaching tips and suggestions on clarity, sentiment,…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Improve ASSIST Application Help", "bureau": "", "problem": "The AI is intended to solve the problem of users needing to sift through multiple documents to find help for specific ASSIST system functionalities. It aims to modernize application help by providing context-driven answers through an AI chatbot, leveraging…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "ASSIST ECF Autoclassification and Summary", "bureau": "", "problem": "The AI is intended to solve the problem of manual document review and categorization by Contracting Officers (COs) within the ASSIST Electronic Contract File (ECF) system. Currently, COs spend significant time reviewing and categorizing 20-50 documents per…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Content Management Analysis System Pilot", "bureau": "", "problem": "The AI is intended to solve the problem of automating data extraction from PDF, Excel, and Word documents stored in an isolated data lake. It aims to develop a prototype web application that can extract, structure, and enable focused analytics from IT…", "outputs": "", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "FAS Catalog Platform - Virtual Assistant", "bureau": "", "problem": "The AI is intended to solve the problem of users struggling to quickly find help and support documentation within the extensive FCP system. Currently, users often resort to contacting the Vendor Support Center (VSC) instead of sifting through existing user…", "outputs": "", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "AI in Public Experience (PX) Contact Center Services Blanket Purchase Agreement (BPA)", "bureau": "", "problem": "The AI is intended to solve the problem of high call, email, and chat volumes within the Public Experience (PX) Contact Center (CC) program, which currently handles over 2 million inquiries annually. By leveraging AI, the PX CC aims to decrease these…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Enterprise Management Program Management Office Chatbot", "bureau": "", "problem": "The AI is intended to solve the problem of efficiently answering a wide range of compliance-related questions from across the organization, which currently consume significant team resources. These questions involve policy interpretations, statutory…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Public Comments Analysis", "bureau": "", "problem": "The AI is intended to solve the problem of labor-intensive and time-consuming processing of public comments on US agencies' published notices, proposed rules, and deregulations, which are received in large amounts through OROS/eRulemaking managed…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Enterprise Chatbot (GSAi)", "bureau": "", "problem": "The AI is intended to solve the problem of providing GSA staff with an enterprise-wide chatbot tool for general productivity support. This will allow users to access an internally available chatbot for tasks such as generating initial drafts, outlines, and…", "outputs": "", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "USAi.gov", "bureau": "", "problem": "The AI is intended to solve the problem of making it easier for other agencies to rapidly deploy a chatbot to their organizations, as well as providing standardized API access to a suite of curated models and a mechanism for managing their user base. This…", "outputs": "", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Cybersecurity Chatbot", "bureau": "", "problem": "The AI is intended to solve the challenges associated with manual, labor-intensive compliance documentation and assessment processes within GSA IT. Currently, there's a lack of effective LLM tools for compliance, and vendors are still developing solutions…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "FAS Vision Agentforce", "bureau": "", "problem": "The AI, specifically the \"FAS Vision Agentforce\" system, is intended to solve several key customer service challenges by automating and improving operations. Its primary purpose is to efficiently handle customer inquiries, especially routine ones like order…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "ServiceNow Virtual Agent (Curie)", "bureau": "", "problem": "This use case is designed for use within GSA to allow employees to research IT issues presenting to their workstation or find a quick answer to an IT procedure.", "outputs": "", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "OCFO Chatbot", "bureau": "", "problem": "The AI is being developed to address the challenge of providing efficient and timely financial inquiry support by automating responses to common user questions. This aims to reduce the workload on financial personnel and improve the accessibility of accurate…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Office of Civil Rights (OCR) Case Crawler", "bureau": "", "problem": "The AI is intended to solve the problem of manually searching through extensive PDF documents of past EEOC OFO case decisions and OCR final agency decisions. This manual process is time-consuming and inefficient, and the AI aims to automate and streamline…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "National Computerized Maintenance Management System (NCMMS) AI Chatbot", "bureau": "", "problem": "The AI is intended to solve the problem of providing user support within National Computerized Maintenance Management System (NCMMS) (IBM Maximo) for facility managers, contractors, and management teams, specifically by offering contextual support for the…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "PBS AI Chatbot Domain Enhancements", "bureau": "", "problem": "The AI chatbot is intended to solve the problem of siloed information and time-consuming research across various Public Buildings Service (PBS) domains, including Portfolio Management, Facilities Management, Project Delivery, Acquisition, Client Strategy,…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Leasing Desk Guide Bot", "bureau": "", "problem": "The AI, specifically the \"Leasing Desk Guide Bot,\" is intended to solve the problem of GSA stakeholders needing to efficiently access accurate, consistent, and context-relevant information from the Leasing Desk Guide to perform their duties. By leveraging a…", "outputs": "", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "GSA", "agency_name": "General Services Administration", "name": "Pricing Policy Chatbot", "bureau": "", "problem": "The AI, specifically the Pricing Policy Chatbot, is intended to solve the problem of efficiently providing factually accurate, context-relevant, and consistent information related to Pricing Policy to internal stakeholders. By training a RAG-based internal…", "outputs": "", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Structuring Notice of Concern Data", "bureau": "HHS/ACF", "problem": "How can the Office of Refugee Resettlement (ORR) clear its backlog of notices of concern (NOC) and minimize backlog in the future? Notice of Concern (NOC) forms contain critical information regarding safety of children who have left ORR's care. Some forms…", "outputs": "Structured data parsed from the subset of NOCs that are scans of documents AI is not used to triage NOCs, just to parse information from scanned documents. The parsed information is presented to the…", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Analyzing Public Comments on Proposed Rule", "bureau": "HHS/ACF", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Child Welfare Information Automated Inquiry System\n\n(Note: previously named \"Child Welfare Information Gateway OneReach Application\")", "bureau": "HHS/ACF", "problem": "How can Child Welfare Information Hotline callers get the right information faster without increasing staffing? The Children's Bureau runs the Children Welfare Information Gateway, a connection to trusted resources on the child welfare continuum. The…", "outputs": "The Information Gateway Hotline connects to a phone interactive voice response (IVR). The Information Gateway hotline maintains a database of state hotlines for reporting child abuse and neglect…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Collective Bargaining Compass", "bureau": "HHS/ACF", "problem": "How can the HHS-NTEU collective bargaining agreement be more easily referenced? The HHS NTEU Collective Bargaining Agreement and associated rules are over 400 pages long, covering numerous topics related to employer-labor relations.", "outputs": "Suggested answer to a question, with thought process and links to relevant section of official document(s). The Collective Bargaining Compass provides a secure Virtual Assistant interface…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Discover User Assistant", "bureau": "HHS/ACF", "problem": "How can ACF Discover users better understand how to use the platform? ACF Discover is a relatively new platform designed to streamline various analyses and data maintenance responsibilities of ACF Executive Officers and other ACF administrative and…", "outputs": "Suggested answer to question, with thought process and links to relevant section of official document(s). The User Documentation Assistant provides a secure virtual assistant interface that is only…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Training and Technical Assistance (TTA) GenAI Chatbot", "bureau": "HHS/ACF", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Head Start Correspondence Categorizer", "bureau": "HHS/ACF", "problem": "How can regional management and program specialists with large grant workloads keep track of all the correspondence being received by staff in their regional offices? The Office of Head Start's (OHS) program specialists receive correspondence through the…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "review", "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Builder Buddy", "bureau": "HHS/ACF", "problem": "How can ACF staff more readily develop their own tailored virtual assistants on our enterprise genAI platform? ACF's enterprise generative AI platform gives users the tools to create their own tailored virtual \"agents\" to assist with more specialized tasks…", "outputs": "Draft virtual assistant configurations that users then further test and iterative on before deployment.", "stage": "Deployed", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Document Review for Alignment with Executive Orders: Position Descriptions", "bureau": "HHS/ACF", "problem": "How can ACF identify position descriptions that may need to be adjusted for alignment with recent executive orders? ACF needed to conduct an audit across position descriptions in accordance with HHS Secretarial Directives related to recent executive orders…", "outputs": "Initial list of position descriptions for further review, validation, and adjustments as applicable by ACF's team. AI was not used to make any final determinations. It was leveraged to more…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Document Review for Alignment with Executive Orders: Grant Materials", "bureau": "HHS/ACF", "problem": "How can ACF identify grants that may need to be reviewed for alignment with recent executive orders? ACF needed to conduct an audit across existing grants and new grant applications in accordance with HHS Secretarial Directives related to recent executive…", "outputs": "List of grants for program staff to review, with an initial assessment of compliance against executive orders and example passages from the grant materials for flagged grants. Staff are only able to…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Grant management support: structuring information in applications", "bureau": "HHS/ACF", "problem": "How can ACF staff more efficiently review grant applications that have non-standardized formats? Grant applications for ACF-funded programs can come in different formats, even when a standard set of questions or template is provided. ACF staff evaluating…", "outputs": "Varies, depending on the program office. Outputs generally involve summarizing information, extracting key information into a specific format, flagging potential gaps or inconsistencies, and…", "stage": "Deployed", "high_impact": false, "technique": "Agentic AI", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Acquisition support: assisting reviews and co-drafting technical evaluation documents", "bureau": "HHS/ACF", "problem": "How can ACF teams more efficiently review contract proposals and summarize technical evaluation discussions? In response to Requests for Information and Requests for Proposals, ACF teams receive responses from interested vendors. In the review process, ACF…", "outputs": "Draft language for technical evaluation documents, based on user-provided context, direction, analysis, and examples. For example, the user may provide a statement on why they assess a proposal to…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Chatbot", "bureau": "HHS/AHRQ/CQuIPS", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Chatbot", "bureau": "HHS/AHRQ/CQuIPS", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Certification and Testing/Program Administration – AI-enabled Internal Processes", "bureau": "HHS/ASTP", "problem": "ONC’s certification and related program operations rely on many manual, text-heavy, and repetitive tasks (e.g., analyzing surveillance reports, validating certification test results, generating public meeting materials, preparing communications, managing…", "outputs": "AI-generated or AI-assisted outputs include: (1) draft analytical summaries and reports (e.g., surveillance reporting analysis, RWT results validation, SED categorization, data visualizations); (2)…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "DHP Virtual Assistant", "bureau": "HHS/CDC", "problem": "To assist HIV researchers by retrieving relevant information related to HIV and HIV research, improving productivity and efficiency in literature review.", "outputs": "The AI assistant uses retrieval augmented generation (RAG) to return information related to HIV based on a user's query and other documents.", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "HIV Data Virtual Assistant", "bureau": "HHS/CDC", "problem": "To improve the data retrieval and automation process for HIV researchers by providing information from datasets and generating code for data analysis, thereby alleviating challenges in finding appropriate HIV-related data.", "outputs": "The AI system uses retrieval augmented generation (RAG) to return information related to HIV based on user queries. Outputs include lists of datasets, associated variable names, and code (SAS, R,…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Identify infrastructure supports for physical activity (e.g. sidewalks) in satellite and roadway images", "bureau": "HHS/CDC", "problem": "To streamline and automate the surveillance of sidewalks and other infrastructure that support physical activity, reducing the labor and cost associated with manual inspection.", "outputs": "Outputs include geocoded data tables, maps, GIS layers, or summary reports identifying sidewalks, bicycle lanes, and other relevant infrastructure from satellite and roadway images.", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "LaserAI", "bureau": "HHS/CDC", "problem": "To reduce screening time and improve accuracy in all phases of systematic reviews by automating title and abstract screening, PDF retrieval, full-text review, and data extraction.", "outputs": "The AI system outputs include prioritized lists of potentially relevant articles for screening, retrieved PDFs from PubMed, and suggested data for extraction from PDFs.", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "NewsScape", "bureau": "HHS/CDC", "problem": "Development of early warning indicators. News articles can form an early warning indicator of public health events and other pieces of information which can be utilized across all major domains of public health. Because of the quantity of news articles,…", "outputs": "NewsScape is a AI enabled news aggregation and summarization that is hosted within the 1CDP platform. The main motivation to build NewsScape was to develop a system that uses Large Language Models…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Transcribing Cognitive Interviews with Whisper", "bureau": "HHS/CDC", "problem": "Reducing the time and effort required to transcribe cognitive interviews for federal health survey research, enabling faster and higher-quality analysis of qualitative data.", "outputs": "The AI generates transcripts from recorded interviews, which are used by staff for qualitative research in support of federal health survey research.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Use of Natural Language Processing for Topic Modeling to Automate Review of Public Comments to Notice of Proposed Rulemaking", "bureau": "HHS/CDC", "problem": "Manual review of large volumes of public comments for Notices of Proposed Rulemaking is labor-intensive and time-consuming. The AI is intended to organize and cluster comments by theme, improving the efficiency and effectiveness of manual review and ensuring…", "outputs": "The AI generates clusters of similar public comments, organized by theme, to aid in manual review.", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "CDC Chatbot - Enterprise Data Assistant", "bureau": "HHS/CDC", "problem": "This tool is a general purpose assistant for CDC staff to ask questions of large sets of documents. Staff may take hours to find the relevant information, and this tool enables fast access powered by Retrieval Augmented Generation (RAG).", "outputs": "The system generates responses to staff questions based upon the provided available information. This includes citations and references to sections of available documents for staff to further explore.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Using Generative AI for Stance Analysis of Public Comments on CDC’s Proposed Rules", "bureau": "HHS/CDC", "problem": "Manual review of public comments for rulemaking is labor-intensive and time-consuming due to the volume and diversity of responses. The AI system automates stance analysis and topic modeling to improve efficiency, accuracy, and insight in the review process.", "outputs": "The system uses generative AI to analyze public comments, providing outputs such as stance (support/oppose/neutral), topics, and sentiment for each comment. These outputs aid regulatory analysts in…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "DGMH AI Chatbot", "bureau": "HHS/CDC", "problem": "Responding to inquiries is time-consuming for staff, especially when information is available but hard to find on public CDC webpages. The AI chatbot drafts initial responses using content from CDC’s website, reducing turnaround time and freeing staff to…", "outputs": "The AI chatbot generates an initial draft response to inquiries using content from CDC’s public-facing webpages. Each draft is reviewed and cleared through the existing CDC process before being sent.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "School LLM initial abstract review process", "bureau": "HHS/CDC", "problem": "Manual review and categorization of thousands of research abstracts related to school readiness science is time-consuming. The AI enables efficient extraction and categorization of themes, reducing human effort and time.", "outputs": "The AI uses an LLM to extract data from abstract reviews and categorize relevant themes and topics into a user-friendly dashboard, enabling users to pull resources from 2012–2022 for specific school…", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "CDC Chatbot", "bureau": "HHS/CDC", "problem": "This tool is a general purpose assistant for CDC staff powered by Large Language Models. Staff can upload documents, summarize information, extract information, create content, develop software code, or general tasks to support operational efficiency.", "outputs": "The system generates responses to staff questions in a general purpose manner including related to questions of uploaded documents. Staff may use the generated text in any manner they deem as…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "EDAV Virtual Assistant - Eva ( Microbot Service) - Bot as a Service (BaaS)", "bureau": "HHS/CDC", "problem": "This tools serves as a helpdesk to support CDC staff in finding relevant information related to the Enterprise Data, Analytics, and Visualization platform available across existing documentation.", "outputs": "Staff will ask the chatbot questions related to various platform documentation. The AI use cases supplies back information including references and citations to the existing documentation for staff…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "NCIRD SmartFind ChatBots - Public and Internal", "bureau": "HHS/CDC", "problem": "Improving efficiency and effectiveness of internal partner mailbox email management and knowledge base maintenance for program staff, and previously, providing public-facing answers to FAQs.", "outputs": "Conversational ChatBots that analyze free text questions and provide agency-cleared answers that best match the question. The system also flags complex or unanswerable queries for manual review.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Detecting, evaluating, and redacting PII in NAMCS HC Component", "bureau": "HHS/CDC", "problem": "How effective are open-source PII detection models in identifying and redacting PII? The National Center for Health Statistics’ (NCHS) Division of Health Care Statistics has collected millions of health records with laboratory results from encounters at…", "outputs": "A semi-automated process to conduct a quality control review of health data records, including potential PII records flagged for manual review.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "DHDSP NOFO Technical Assistance Chatbot", "bureau": "HHS/CDC", "problem": "The Division for Heart Disease and Stroke Prevention (DHDSP) experienced numerous challenges in coordinating with grantees/recipients to support questions related to their programs. These challenges often resulted in inefficiencies such as inconsistent…", "outputs": "The Technical Assistance chatbot incorporates a Large Language Model (LLM) AI to provide quick, accurate, plain-language answers to questions on grants policy and program processes, protocols, and…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "PDF information extraction for 889 Forms", "bureau": "HHS/CDC", "problem": "Document processing and extraction of text from required 889 compliance forms (PDFs and/or image files) into a searchable cloud-based database. Eliminating manual data entry and providing a more transparent log of compliance related information in a…", "outputs": "The 889 Document Processor is an Optical Character Recognition (OCR) model built using Microsoft AI Builder. The 889 Form Repository applies a Power Automate Flow, which triggers when a user uploads…", "stage": "Pilot", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "ATSDR Toxicological Assistant chatbot", "bureau": "HHS/CDC", "problem": "The ATSDR Toxicological Assistant chatbot via the CDC Chatbot has access to query 180 comprehensive toxicological profiles (A-Z Index of Tox Profiles | Toxicological Profiles | ATSDR). The chatbot can assist users in accessing toxicological data, answering…", "outputs": "Substance Lookup: Summarizes health risks, exposure pathways, and toxicological data from a pre-built library of over 180 ATSDR toxicological profiles. Interactive Q&A: Generates answers to user…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Text embedding analysis tool", "bureau": "HHS/CDC", "problem": "Reviewing large sets of documents can take hours to identify similar clusters or analysis. The tool helps non-technical staff explore large, text-based datasets by generating clusters of text and identifying similar documents.", "outputs": "The system generates text embeddings and then understand how their documents cluster in the embedding space. The system has no default output--it's primarily an AI-enabled canvas for drawing the…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Agentic RAG Chatbot", "bureau": "HHS/CDC", "problem": "The creation of accurate, detailed, and factually-grounded draft responses to public inquiries takes a large amount of time and could benefit from assistance from LLMs. Because of how complex the responses to the inquiries can be, LLMs alone did not perform…", "outputs": "Draft responses to an inquiry to serve as a base for staff. The inquiry draft will follow all CDC public health research, data, and recommendations based on the best science currently available.…", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "DHP Data Repository and Dashboard Chatbot", "bureau": "HHS/CDC", "problem": "HP uses data to drive action for preventing and addressing HIV in the United States (U.S.). Historically, DHP’s data sources have not been easily accessible internally due to a siloed organizational structure. The DHP Data Repository and Dashboard aims to…", "outputs": "The expected output from this AI focused project is to have a chatbot with a user interface with the model behind it utilizing narrative information specific to DHP. Users will be able to ask…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "NCHHSTP Social Media Modernization Strategy: Thought Leadership and Social Listening", "bureau": "HHS/CDC", "problem": "To improve communication and ensure health messaging on social media platforms is responsive to the needs of the American people. AI-powered social listening tools are increasingly used in public health to analyze public conversations and audience sentiment…", "outputs": "Visual network/cluster maps, volume and trend charts, sentiment analysis, top topics and keywords, influencers and top sources, demographics and audience insights, custom segments and thematic…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AI-Assisted Extraction of Circumstance Information from National Violent Death Reporting System Narratives", "bureau": "HHS/CDC", "problem": "The National Violent Death Reporting System (NVDRS) compiles both quantitative and qualitative data on the circumstances surrounding violent deaths, including homicides and suicides, from three key data sources related to each death: the death certificate,…", "outputs": "The AI system produces structured data outputs derived from the free-text narratives in the NVDRS. For each narrative, the system identifies and extracts predefined circumstance categories (e.g.,…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Automating Influenza Vaccine Virus Data Processing", "bureau": "HHS/CDC", "problem": "Information for candidate vaccine virus (CVV) data from public-facing websites is highly labor intensive to gather. Previously R scripts—text files with computer programming commands—automate some scraping processes but still rely on manual methods to…", "outputs": "The use case automates extracting key phrases, recognizing text, processing forms, and identifying entities to streamline data extraction and supplement missing CVV data from PDFs. This tool is…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Beryllium exposure reconstruction", "bureau": "HHS/CDC", "problem": "Using machine learning to extract information about job title and task and assign relevant exposure codes.", "outputs": "Exposure codes related to Beryllium exposure.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "PPE Concerns Copilot", "bureau": "HHS/CDC", "problem": "The PPE Concerns Mailbox received inquiries that NIOSH/NPPTL staff must review and respond to. To date, over 10,000 questions have been received with answers provided. Responding to an inquiry requires a multi-step process that staff must complete manually,…", "outputs": "The Copilot will analyze incoming questions, search the existing dataset for relevant responses, and generate draft replies for human review. After staff revise and approve the response, the Copilot…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Assessing public comment responses to draft NIOSH wildland fire smoke document", "bureau": "HHS/CDC", "problem": "Efficiently compile and synthesize the public comments received for a draft NIOSH document.", "outputs": "Public comment responses compiled in various formats to make the response process more efficient.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Mining.AI", "bureau": "HHS/CDC", "problem": "Mining.AI is an innovative initiative led by a small team at NIOSH tasked to develop a domain-specific generative LLM AI platform focused on improving health and safety in the mining industry. The project consolidates NIOSH's latest peer-reviewed research…", "outputs": "A NIOSH specific internal chatbot tool that researchers can interact with to assist in the digestion of all previous NIOSH published articles.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Delegations Repository Assistant", "bureau": "HHS/CDC", "problem": "The Delegations Library, which houses critical guidance on authorities and approvals, has been selected as the pilot use case due to its centralized structure and high relevance. Staff frequently report difficulty locating the right documents, resulting in…", "outputs": "Staff will receive responses with information from existing delegation-related documents within the internal Delegations Library. The responses will include citations and references to the…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AIP Assist", "bureau": "HHS/CDC", "problem": "AIP assist supports user engagement with the Palantir Platform. It is an LLM-powered support tool that is able to answer inquiries about the platform and can also guide users in developing their own applications by supporting users to write their own code or…", "outputs": "AIP Assist is an LLM-powered tool available to all 1CDP. It provides accessibility to the user in relation to the capabilities of the platform by using generative AI and internal documentation. This…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Chatbot within Hub", "bureau": "HHS/CMS/CCIIO", "problem": "(Other) Currently the following help support activities are handle by a human: - Personalized FAQ?s - Clarify questions with schema and onboarding, etc. - Handle \"Where is 'My file' or 'My Request'?\" inquiries - Provide a \"Talk to Agent\" feature - Schedule a…", "outputs": "Help support team in day-to-day communication with external partners", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Review Regulatory Comments", "bureau": "HHS/CMS/CCIIO", "problem": "(Marketplace) Public comment analysis in the Letter to Issuers (LTI) and Notice of Benefits and Payment Parameters (NBPP) to streamline review of comments.", "outputs": "Reduce the amount of time and resources CCIIO and contracting staff would need to review the LTI and NBPP by an estimated 25%.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Improved Data Quality Checks", "bureau": "HHS/CMS/CCIIO", "problem": "(Marketplace) Improve consumer experience by providing an asynchronous (not at the same time) solution to detect and provide near real time feedback via outreach to the consumer, shortening the overall return cycle time without requiring UI changes.", "outputs": "Improvement of user experience", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "SBC Content Review", "bureau": "HHS/CMS/CCIIO", "problem": "(Marketplace) The SBC Content Review identifies cost-sharing discrepancies between the Plans and Benefits Template and SBC Form. If there are discrepancies in the numerical values input in the two data sources, reviewers must manually review the…", "outputs": "Reduce time in identifying true discrepancies.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "MFT UI Chatbot", "bureau": "HHS/CMS/CCIIO", "problem": "Hub/MFT team manually supports operations related to onboarding, Q/A, file tracking and technical inquiries. ? Monthly Average of inquiries: ? ~800 Email inquires ? ~40 Jira/SNOW Tickets? The Communication is manual via emails, Zoom Meetings, and Slack. This…", "outputs": "Real-Time File Status includes Detailed Processing Information? FAQs Ability to chat with Live-Agent Survey Form Historical File Tracking Limited CMS Stakeholder Testing Develop Quality of Service…", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "MLMS Upgrade MILA from CARLA to Druid", "bureau": "HHS/CMS/CCIIO", "problem": "New chatbot proposed by CMS MLMS team will be integrated into the existing MLMS application. It aims to improve user experience, designed to maintain or boost deflection rates, and will be hosted within the CMS environment for smooth integration.", "outputs": "Improve user experience and maintain or boost deflection rates", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Feedback Analysis Solution (FAS)", "bureau": "HHS/CMS/CCSQ", "problem": "Speeds up the manual and time-consuming process of analyzing public comments on frequently posted regulations.gov and FDMS dockets by employing advanced natural language processing and machine learning technologies.", "outputs": "FAS categorize stakeholder feedback (collected in multiple venues), thereby enabling CMS analysts to use the system to quickly identify comments that may impact program/policy decisions. The system…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AI-assisted comment triaging tool", "bureau": "HHS/CMS/CM", "problem": "To reduce labor hours of manual comment review we use AI to assist in comment review and triage, and in identifying form letters.", "outputs": "Compiles public comments by topic in the rule.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Docketscope Public Comment Processing", "bureau": "HHS/CMS/CM", "problem": "Docketscope is the platform used to triage public comments submitted on the OPPS/ASC and ESRD PPS proposed rules.", "outputs": "The AI functionalities used in Docketscope include a clustering feature that automatically groups similar public comments together using text analysis and heuristic techniques, an issue mapping…", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AI Language Translation Support in Smartling", "bureau": "HHS/CMS/OC", "problem": "Improved translation", "outputs": "Suggestion", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AI Agent Orchestrator POC", "bureau": "HHS/CMS/OIT", "problem": "The AI Agent Orchestrator solves the problem that current chatbots are limited to question-and-answer interactions and cannot orchestrate complex, multi-step analytical workflows that require calling external data science notebooks and tools in the correct…", "outputs": "The output is a natural language interface that enables users to easily interact with complex data science tooling and analytical workflows without requiring specialized knowledge of the underlying…", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Agentic Web Search", "bureau": "HHS/CMS/OIT", "problem": "The AI-powered web search agent solves the problem that CMS's custom chatbot, CMS Chat currently lacks internet access and is limited to training data with a cutoff date, preventing staff from accessing real-time information", "outputs": "The agent will pull real-time web search results into CMS Chat context", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "IDR Support Bot", "bureau": "HHS/CMS/OIT", "problem": "The IDR Support Bot is an AI-powered chatbot that makes it easier for CMS users to get the IDR information they need, when they need it. It answers questions about IDR terminology, tools and services, onboarding steps, available data, training content, key…", "outputs": "The IDR Support Bot leverages retrieval-augmented generation (RAG) technology to provide accurate and contextually relevant responses to user queries.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Eligibility & Enrollment Medicare Online (ELMO) Chatbot", "bureau": "HHS/CMS/OIT", "problem": "Assist Medicare caseworkers in navigating a fairly complex application", "outputs": "Answers questions about the ELMO tool, how to navigate it, and where to find information", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AI to Improve Public Access to the Administrative Appeals Process", "bureau": "HHS/DAB/OS", "problem": "More effectively leverage online resources to better assist the public and reduce the number of misfiled appeals submitted to the Departmental Appeals Board (DAB) via electronic filing.", "outputs": "Reliable data on appellant filing activity and meta data the agency will use to analyze workloads and allocate resources.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Process Large Amount of Submitted Docket Comments", "bureau": "HHS/FDA/CBER", "problem": "Deduplication of public comments, and generating draft sentiment analysis and grouping of comments", "outputs": "To provide reviewers adhoc VAERS queries.", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "HIVE AI Pilot", "bureau": "HHS/FDA/CBER", "problem": "The AI is intended to assist in solving several problems related to the review process for INDs. Specifically, it aims to address issues of inefficiency, delays in identifying deficiencies and information overload. By providing recommendations for review…", "outputs": "The system's output include: 1. review discipline recommendations - automated suggestions for the most appropriate review disciplines for each incoming submission. 2. highlighted key data -…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "MedWatch Dashboard", "bureau": "HHS/FDA/CDER", "problem": "Need to proactively identify emerging issues and clusters within MedWatch reports using advanced analytics.", "outputs": "Identify product risk signals from MedWatch reports by using time series analysis to flag products and topic modeling to summarize the comments.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Renamed: Information Visualization Platform (InfoViP) to Support Analysis of FAERS safety reports\nPreviously: Information Visualization Platform (InfoViP) to Support Analysis of adverse event reports", "bureau": "HHS/FDA/CDER", "problem": "The AI is intended to support the analysis of FAERS Individual Case Study Reports in post-market safety surveillance by automating duplicate detection, creating temporal visualizations, and classifying reports by information quality.", "outputs": "Performs Natural Language Processing (NLP) and applying Machine Learning (ML) algorithm to extract data from unstructured case narratives and combine with structured data to support analysis and…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Packaging Materials and Suppliers", "bureau": "HHS/FDA/CDER", "problem": "FDA needs to efficiently identify which Drug Master Files (DMFs) contain specific packaging materials and understand how these materials connect to drug applications, but this information is currently difficult to locate across numerous documents.", "outputs": "Support information extraction from unstructured documents", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Real World Data/Evidence", "bureau": "HHS/FDA/CDER", "problem": "FDA receives numerous submissions containing real-world data and evidence, but identifying and cataloging these studies across various submission types is time-intensive and critical for regulatory reporting requirements under PDUFA (Prescription Drug User…", "outputs": "Supports extracting text from unstructured documents and tagging for documents containing Real World Evidence and Real World Data", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Creating a development network", "bureau": "HHS/FDA/CDER", "problem": "The AI is intended to solve the problem of inconsistent data formats and inefficient access to unstructured clinical data across multiple healthcare sites. It aims to standardize EHR and claims data conversion into the Sentinel Common Data Model and develop…", "outputs": "Creates a network of organizations that can support development of algorithms and use of AI tools such as Natural Language Processing (NLP).", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Data Extraction from IND Safety Reports using OCR/AI Technologies", "bureau": "HHS/FDA/CDER", "problem": "Manual extraction of data from IND safety reports is labor-intensive and time-consuming for regulatory staff.", "outputs": "Using ThinkTrends, a COTS tool, extracted data is converted into E2B(R2) format for automatic ingestion into FAERS LSMV", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Use case package 1: Empirical application of the Sentinel EHR and claims Data Partner network to address ARIA insufficient inferential requests (UC1)", "bureau": "HHS/FDA/CDER", "problem": "This AI project is designed to solve the problem of determining whether available data sources and analytical methods are suitable for specific pharmacoepidemiologic research questions. It aims to systematically evaluate data fitness-for-purpose and identify…", "outputs": "Natural Language Processing (NLP) for Electronic Health Record (EHR) unstructured data extraction", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "FE5: Incorporate a range of frequently used engineering features from EHRs into the Sentinel common data model in the Sentinel EHR and claims linked Data Partner network", "bureau": "HHS/FDA/CDER", "problem": "This AI project is designed to solve the problem of extracting valuable clinical information trapped in unstructured free-text fields within electronic health records. It aims to create a systematic feature engineering approach that can convert narrative…", "outputs": "NLP for EHR unstructured data extraction", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Sentinel System Task Order to address an Emerging Safety Need", "bureau": "HHS/FDA/CDER", "problem": "This AI project is designed to solve the problem of efficiently validating emerging safety signals through chart review when traditional structured data is insufficient. It aims to use NLP-supported tools to extract and analyze information from unstructured…", "outputs": "Claims and EHR data", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "FOIA REDACTION (FRED) TOOL", "bureau": "HHS/FDA/CDER", "problem": "FRED is designed to support FOIA staff in redacting records more efficiently and consistently. FOIA redaction can be a time-consuming process and experience large back-logs of requested documents. It aims to use AI to analyze, identify, and generate…", "outputs": "FRED produces a PDF with boxes around text that it recommends for redaction along with comments for the redaction code", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "CoreDF", "bureau": "HHS/FDA/CDER", "problem": "Current nonclinical review processes require manual extraction and analysis of sponsor findings from lengthy PDF study reports, creating inefficiencies in data quality assessment and regulatory timeline.", "outputs": "Sponsor findings from non-clinical study reports", "stage": "Pilot", "high_impact": false, "technique": "Other", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Product Label and Text Extraction System (PLATES)", "bureau": "HHS/FDA/HFP", "problem": "Manual food label data extraction causes slow data accessibility and insights. Manual data processing and standardization causes slow data accessibility and insights. Decentralized food label data limits research and regulatory processing of industry…", "outputs": "The system includes a user interface that allows users to upload food product images to receive extracted, standardized (utilizing FoodTrak standards), and metadata attached, structured data for 30+…", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Data Ingestion and Content Explorer (DICE)", "bureau": "HHS/FDA/HFP", "problem": "Multiple stakeholders across HFP have the business need to search for content within artifacts and documents uploaded to various systems. For systems like CARA and CARTS, document search capabilities are limited due to the Appian technology stack utilized by…", "outputs": "The system includes a user interface that allows users to view returned search results, templatize unstructured documents using the intelligent document processing workflow, and view extracted text…", "stage": "Pilot", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Smart Solution for Docket Management (SSDM)", "bureau": "HHS/FDA/OC", "problem": "The AI is intended to solve the extremely labor-intensive and time consuming process of manually collating, de-duplicating, and categorizing public comments on FDA dockets.", "outputs": "The AI-enabled tool provides two main outputs: 1) a line listing excel file that organizes comments into groups based on similarity/deduplication thresholds and tags them with AI-identified and…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "ChatBot for Safety Reporting Portal Adverse Events and Product problems submissions", "bureau": "HHS/FDA/ODT", "problem": "Increases data integrity and aid with routing the user to the correct commodity group to submit their adverse event or product problem. DRUID AI, a COTS tool, boasts a comprehensive suite of functionalities, encompassing conversational flows and seamless…", "outputs": "The AI routes to the correct form and helps the user to complete the report faster and endures data integrity. The outputs uses knowledgebase to answer questions, format responses, routes to correct…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence", "forms" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Machine Learning as a Service: Translate and extract text from images using AI", "bureau": "HHS/FDA/ODT", "problem": "This AI project is designed to solve the problem of reviewing foreign products, extracting ingredient lists, and border inspections", "outputs": "Translated text into English, in JSON format", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake", "translation" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Machine Learning as a Service: Extract data from product labels, business forms, and image files", "bureau": "HHS/FDA/ODT", "problem": "This AI project is designed to assist in capturing data submitted to & reviewed by the FDA by extracting text and data, determining its structure, and saving the information in a more useful format", "outputs": "Parsed text and data structured in JSON format, with key/value pairs where appropriate (such as for specific fields in a form or nutrient name & amount on a product label)", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Electronic translational services for regulatory documents for articles offered for import", "bureau": "HHS/FDA/OII", "problem": "FDA' Office of Information Operations (OIO) seeks to implement automated translation capabilities for foreign language documents essential to import operations. By integrating translation services directly into OIO staff workflows, the Agency can improve…", "outputs": "A translation service interface developed within an imports entry review system that utilizes Google Translate API provides the required translation of the entries entering US supply-chain. This…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Intelligent Document Processing to analyze current import entry documentation for potential discrepancies", "bureau": "HHS/FDA/OII", "problem": "The intent is to assist in the process of discrepancy identification between documentation submitted by trade and CBP line data submitted.", "outputs": "List of discrepancies between document data and CBP line level information", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "review" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AI Audit Resolution Assistant (AIARA)", "bureau": "HHS/HRSA", "problem": "The DFI PRF AIARA project leverages AI to streamline the single audit process. The team utilizes the Robotic Process Automation (RPA) to extract data from the Federal Audit Clearinghouse to create templates for both the audit notification and management…", "outputs": "HRSA utilizes generative AI to streamline the Single Audit resolution process and the creation of Management Decision Letters to formally close out and resolve Single audit findings. The AI Audit…", "stage": "Pilot", "high_impact": false, "technique": "Agentic AI", "categories": [ "intake", "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Knowledge Navigator", "bureau": "HHS/HRSA", "problem": "The objective is to develop an AI model that can answer detailed and complex questions about the key programmatic document, Application and Program Guidance (APG), that is issued annually before the application cycle opens. The PoC LLM has Applicant and…", "outputs": "BHW has implemented a proof of concept Generative AI (GenAI) Large Language Model (LLM) Knowledge Navigator (KN) to support National Health Service Corps (NHSC) and Nurse Corps loan repayment and…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Medical records summarization", "bureau": "HHS/HRSA", "problem": "The AI will reduce the amount of time required to review and evaluate a claim.", "outputs": "AI will facilitate the collection and preprocessing of unstructured data, and create a condensed (and indexed) document for AI to intelligently review the thousands of pages of medical/legal…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "PRF Program Chatbot", "bureau": "HHS/HRSA", "problem": "Answer the inquiries related to GAO, FOIAs, and Litigation quickly and efficiently", "outputs": "The app will use retrieval augmented generation (RAG) to answer questions using a knowledge base composed of PRB's programmatic SME documents. The responses will be sourced and cited from program…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence", "forms" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "FOIA Exemption-Aligned Redaction", "bureau": "HHS/HRSA", "problem": "The FOIA staff will leverage this solution to review records in response to FOIA requests, search the internet for publicly available information (names, email addresses, contact information of federal and non-federal individuals), and identify personally…", "outputs": "HRSA uses agentic AI to propose redactions and comments for potentially sensitive data elements for Freedom of Information Act (FOIA) staff that reviews grant documents. The proposed redactions by…", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "forms" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "HRSA Data Warehouse ChatBot", "bureau": "HHS/HRSA", "problem": "HRSA Data Warehouse Chatbot to respond to public Inquiries.", "outputs": "Help public to gain quick access to program data (e.g.; Area Health Resources Files, Find Healthcare services, Service Delivery Sites etc.)", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Program Reporting System Knowledge base Chatbot", "bureau": "HHS/HRSA", "problem": "Help users get relevant information quickly instead of in lengthy manuals, FAQs, or scattered documentation and reduce support cost.", "outputs": "AI chatbot integrated with post award performance reporting system which serves as a knowledge base for FAQs and system step by step guides. Both grantees and internal HRSA users will use the…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Program Compliance and Reporting Knowledge Base Chatbot", "bureau": "HHS/HRSA", "problem": "Users often need to navigate multiple technical assistance webpages, manuals, documents, FAQs, or submit inquiries through the contact form to find answers. This can be time-consuming and increases time and support capacity.", "outputs": "AI chatbot integrated with programmatic requirements and post award performance reporting system which servers as a knowledge base for FAQs and system step by step guides. Both grantees and internal…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Applying for Grants Chat Bot", "bureau": "HHS/NIH", "problem": "Save time for applicants by guiding users through the application process step-by-step, recommending additional resources for grant writing, and helping determine eligibility.", "outputs": "Input: Grants and Funding information/processes and FAQs for prospective grantees. Output: Targeted resources related to probing questions for end users.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Collections Summarization Chatbot", "bureau": "HHS/NIH", "problem": "Complex, time-consuming methods for discovery of content, and difficulty for users to understand the scope of content through summarization.", "outputs": "Summary of the resource presented in text format.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "CSR Public Chatbot (CPC)", "bureau": "HHS/NIH", "problem": "This tool answers questions from the public, potential reviewers, and applicants by directly pulling relevant information from official government web pages, instead of searching FAQ lists.", "outputs": "This tool recommends original source material that seems to answer the user's question, and allows the user to check the accuracy of the answer.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "DAIT AIDS-Related Research Solution", "bureau": "HHS/NIH", "problem": "DAIT POs need to identify grant applications that involve AIDS-related research (ARR) so they can evaluate them for additional funding.", "outputs": "This application extracts text from grant applications as input, and then uses classification models to predict the priority and category of each grant application as the output. The output is…", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Generative AI (GenAI) Still Image Tagging", "bureau": "HHS/NIH", "problem": "Lack of a cost-effective mechanism to label and describe digital images.", "outputs": "Classifications tags and/or image summary in text form", "stage": "Pre-deployment", "high_impact": false, "technique": "Other", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "NBS Virtual Assistant", "bureau": "HHS/NIH", "problem": "Service Desk Operations", "outputs": "\"Supporting all NBSC business workstreams, the ONBS Assistant will enhance user engagement with NBSC Applications by providing comprehensive and personalized assistance A variety of support document…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "NIAMS AI Chatbot Pilot", "bureau": "HHS/NIH", "problem": "To provide a secure, protected environment for NIAMS staff (OD, EP, IRP and IT) to explore, test, and understand how to use AI to be more efficient with a wide variety of administrative tasks such as general research, summarizing/querying documents, drafting…", "outputs": "Input: natural text in the form of user questions, user uploaded documents. Output: Generated text in the form of answers to user questions, generated answers (summaries/queries) based on user…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "OIT Help Desk Chatbot", "bureau": "HHS/NIH", "problem": "The OIT help desk would like to shorten resolution time for tickets, help empower users, generate a non-trivial response for a complex question in a desired format such as security, architecture, PMO policy, reports.", "outputs": "Publicly available help desk data, NIST policy in pdf format as user-provided data. Prompt and output are in natural language.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "TB Case Browser Image Text Detection", "bureau": "HHS/NIH", "problem": "Without OCR technology, validation of text on images is manually intensive and, without proper controls, can create the risk of sensitive information coming into the system.", "outputs": "User uploads a DICOM image, which is converted and passed to the AWS Rekognition service. Output is a JSON block with predictions on the location of text within an image.", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "NIH Grants Virtual Assistant", "bureau": "HHS/NIH", "problem": "Provides easy access to grants information and policies.", "outputs": "The inputs include information available on the NIH Grants and Funding site, such as FAQs. The outputs are answers to questions/prompts provided by the user.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "NIGMS Azure Open AI", "bureau": "HHS/NIH", "problem": "There are a number of situations in which administrative activities can be augmented by generative AI, especially when classification of documents is needed but no training data exist.", "outputs": "Input: Text from various components of NIH grant applications. Output: Open AI chat completions (text) or text embeddings (vectors of numbers).", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "MirBot", "bureau": "HHS/NIH", "problem": "The AI tool addresses the challenge of efficiently and consistently processing incoming study PDFs by automatically extracting key information needed for grant review workflows.", "outputs": "The system produces an indexed and preprocessed version of each submitted PDF, enabling context-aware question-answering by the AI. A predefined set of questions is automatically asked to extract…", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "ChIRP - A ChatGPT Model for the NIH Intramural Community", "bureau": "HHS/NIH", "problem": "We hoped to load text into the chatbot to assist in summarization and identification of prominent themes. ChIRP was moderately helpful for our use case, primarily because we used it at a stage in development where documents could not be directly uploaded…", "outputs": "We hoped to use ChIRP to thematically analyze text.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Help Desk AI Assistant", "bureau": "HHS/NIH", "problem": "Help Desk staff need to write new responses to IT service desk requests and inquiries. The agent will provide Help Desk staff with ideal language when responding to tickets.", "outputs": "The AI agent provides responses to IT service desk tickets that staff can copy & paste into ServiceNow fields.", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AWS Exscribo", "bureau": "HHS/NIH", "problem": "Typical transcription services struggle to handle scientific and biomedical terminology, and many platforms do not include the ability to pretrain for specific words, or offer the ability to perform queries on that text in the transcription system itself.", "outputs": "The AI systems output includes transcriptions of audio recordings, and outputs of results from Gen AI prompts on those transcriptions.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Virtual Assistant for the NIAMS Grant Management Applications", "bureau": "HHS/NIH", "problem": "New investigators and staff often struggle to locate policies, workflows, and troubleshooting steps buried across SharePoint guides, SOP documents, and in?app help screens for the two NIAMS Grants Management applications. In addition, support tickets and…", "outputs": "Input : NIAMS Grant Management Training material and Job aids such as SOPs, user guides, and job aids. Output : Responses to end user questions with citation from user guides, training materials,…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Coding translation", "bureau": "HHS/NIH", "problem": "Translates code between programming languages, e.g. rewrite python script in equivalent R commands; translate natural language to code, e.g. write a python script that will convert this gene matrix to a transcript matrix etc.", "outputs": "Output is computer code in designated programming language.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Grants Portfolio Analysis", "bureau": "HHS/NIH", "problem": "Facilitate scientific program management and grant portfolio assessment", "outputs": "Summaries of sections of grant applications and progress reports. Categorization of grants and applications.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "RAG System For Travel Planning", "bureau": "HHS/NIH", "problem": "The chatbot answers questions from the users based on the user guide and potentially categorizing the IT helpdesk tickets.", "outputs": "Recommendations to users on filling out travel documentation.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "AI-enabled User Support and Impact Monitoring", "bureau": "HHS/NIH", "problem": "User support for DSLD data inquiries and monitoring of DSLD usage and impact in the research community.", "outputs": "Chatbot-style interface", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Section 508 Azure OpenAI Chatbot", "bureau": "HHS/NIH", "problem": "This tool will streamline access to compliance resources, guidance, and support, empowering NIH staff and stakeholders to meet federal accessibility requirements. Designed to efficiently handle help desk inquiries, the chatbot will adhere to NIH’s security…", "outputs": "Recommendations to Section 508 resources and guidance", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Enhancing the RCDC with Generative AI", "bureau": "HHS/NIH", "problem": "The goal is to assess whether generative AI can enhance the RCDC process by minimizing time-consuming, resource-intensive routine and manual tasks and improving overall efficiency.", "outputs": "The Azure cloud platform provides secure access to OpenAI’s ChatGPT model, which is connected to search indexes pre-loaded with relevant datasets. These datasets include internal agency-provided…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "NIH Travel Policy AI Chatbot", "bureau": "HHS/NIH", "problem": "The AI-powered chatbot is designed to address a key operational challenge: the high volume of routine travel policy inquiries directed toward NIH administrative staff. These questions, often repetitive and time-consuming, divert valuable human resources from…", "outputs": "The chatbot outputs text-based, interactive responses tailored to helping NIH staff plan and manage official travel in compliance with policy. These outputs are designed to be helpful,…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Federal Assistant AI Agent", "bureau": "HHS/OCIO", "problem": "HHS and its Family of Agencies have a diverse, spread out basis of content that can be confusing to navigate and requires a user to have knowledge of HHS org structure to be able to obtain answers to their questions; leveraging Agentic AI using Turnkey app…", "outputs": "Plain language, conversational responses to customer inquiries via chatbot, providing sources for information as needed to increase accuracy of information and direct end-users to relevant resources.", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "CoPilot Outlook", "bureau": "HHS/OCR", "problem": "Staffing shortage", "outputs": "Previously used to revised emails", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HHS", "agency_name": "Department of Health and Human Services", "name": "Applicant Help Chatbot", "bureau": "HHS/OS/ASFR", "problem": "Answer user questions about Grants.gov", "outputs": "System help documentation and funding opportunity listings", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HUD", "agency_name": "Department of Housing and Urban Development", "name": "Email Assistant", "bureau": "Office of Housing", "problem": "The Federal Housing Administration (FHA) Resource Center is receiving a surge of an unprecedented number of emails requests in the contact center and needs a way to reduce the email processing time.", "outputs": "Recommended actions for completing the email service request.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "HUD", "agency_name": "Department of Housing and Urban Development", "name": "FHA Resource Center Chatbot", "bureau": "Office of Housing", "problem": "Expanding the ability to answer calls with a Virtual Assistance Chatbot and Voice Bot on the Interactive Voice Response system 24/7 at the Federal Housing Administration (FHA) Resource Center / Contact Center.", "outputs": "Responses with information from the 2200+ publicly published FAQs for inquires received by the FHA Resource Center / Contact Center on the FHA FAQ website proposed Virtual Assistant Chatbot along…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "HUD", "agency_name": "Department of Housing and Urban Development", "name": "Voice of the Customer", "bureau": "Office of the Chief Financial Officer (OCFO)", "problem": "The Office of the Chief Financial Officer, Customer Experience Team works across the department to develop a deep understanding of who our customers are and how we can best serve their needs.", "outputs": "Text visualizations in reports/dashboards to trend, baseline, and identify key drivers of overall customer sentiment and service delivery performance.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence", "translation" ] }, { "agency": "NARA", "agency_name": "National Archives and Records Administration", "name": "A1 Museum AI Project", "bureau": "National Archives & Records Administration (NARA)", "problem": "The AI is intended to solve the problem of limited document discoverability and intensive manual labor by using automated tagging to personalize the visitor experience and make millions of digital records instantly accessible.", "outputs": "This AI use provides enhanced visitor access to NARA records through an easy-to-use interface that identifies NARA records based on visitor responses to a chatbot conversation.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "NARA", "agency_name": "National Archives and Records Administration", "name": "AI Pilot Project to Screen and Flag for Personally Identifiable Information (PII) in Digitized Archival Records", "bureau": "National Archives & Records Administration (NARA)", "problem": "The AI is intended to solve the problem of manual processing bottlenecks and privacy risks by automating the identification and redaction of sensitive personal information within massive digitized collections to ensure safe and rapid public access.", "outputs": "This AI use case will produce redacted records, a prioritized list for redaction, and a tool for staff to redact unpublished records.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "forms" ] }, { "agency": "NARA", "agency_name": "National Archives and Records Administration", "name": "Auto-fill of Descriptive Metadata for Archival Descriptions", "bureau": "National Archives & Records Administration (NARA)", "problem": "The AI is intended to solve the problem of the \"descriptive gap\" created by labor-intensive manual cataloging by automatically generating metadata and summaries from document content, allowing NARA to process massive backlogs and make millions of records…", "outputs": "This AI use case will create more complete and informative descriptions for records, making them easier to find in the National Archives Catalog.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "NARA", "agency_name": "National Archives and Records Administration", "name": "Freedom of Information Act (FOIA) Discovery AI Pilot", "bureau": "National Archives & Records Administration (NARA)", "problem": "The AI is intended to solve the problem of larger FOIA backlogs and manual review bottlenecks by automating the discovery of relevant records and the redaction of sensitive data, ensuring responses are both rapid and compliant with privacy laws.", "outputs": "This AI use case will output relevant records matching FOIA requests and redacted versions of those records with sensitive information removed.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "NextGen Data Analytics: Letters of Agreement", "bureau": "ARC: Ames Research Center", "problem": "Today, operation constraints are documented via Standard Operating Procedures (SOP) and Letters of Agreement (LOA) and are not made available to the public in a consistent manner. SOPs are specific to an air traffic control facility and specify the…", "outputs": "Test-based digitized SOPs and LOAs.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "Science Translation with AI", "bureau": "GSFC: Goddard Space Flight Center", "problem": "This work uses natural language processing and bidirectional encoder representations fromm tansformers (BERT) to perform Science Translation on relevant documents or text files.", "outputs": "Predictions", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "ChatGSFC", "bureau": "GSFC: Goddard Space Flight Center", "problem": "ChatGSFC is an AI-powered chatbot designed for Goddard Space Flight Center personnel Powered by a state-of-the-art large language model (currently Claude 3.5 Sonnet) Capable of assisting with a wide range of tasks: General questions and quick research…", "outputs": "Potential use cases across multiple domains: Engineering tasks Scientific research Project management Technical writing Administrative support Bring your own data up to FISMA-Moderate/CUI…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "RAG Chatbots: Enhancing User and Internal Support Through Dual Knowledge Systems", "bureau": "GSFC: Goddard Space Flight Center", "problem": "This work presents two complementary RAG-based chatbot systems developed for NASA's Community Coordinated Modeling Center. These tools represent practical applications of retrieval-augmented generation to enhance both external user support and internal team…", "outputs": "The public-facing chatbot provides quick answers about CCMC tools, services, and general heliophysics information, optimized for speed and accuracy when addressing common user queries.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "correspondence" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "MADI - Modular AI for Design and Innovation", "bureau": "GSFC: Goddard Space Flight Center", "problem": "MADI (Modular AI for Design and Innovation) is a decentralized, open-source AI platform that identifies unexplored research \"whitespace\" between scientific disciplines through secure plugin architecture and interactive visualization. Unlike traditional…", "outputs": "Research gap identification reports, cross-disciplinary connection recommendations, interactive 3D knowledge graph visualizations, whitespace analysis summaries, technology combination suggestions,…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "review", "correspondence" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "OCFO External Performance Reporting", "bureau": "HQ: Headquarters", "problem": "Gen AI uses performance data provided by MDs to write a draft summary performance report", "outputs": "Written Summary of MD-provided performance data in a formal report style", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "Ask Tech Port", "bureau": "HQ: Headquarters", "problem": "Ongoing development and enhancement of the NASA Technology Portfolio Management System (TechPort) \"AskTechPort\" generative AI tool. AskTechPort leverages the TechPort data set of over 18,000 current and historical NASA applied research and experimental…", "outputs": "Interactive responses", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "Noise suppression in Human Spaceflight audio systems", "bureau": "JSC: Johnson Space Center", "problem": "Noise. Is. Aggravating. Especially on voice and video calls. It’s certainly an unwelcome party to most conversations, but it has always been around.", "outputs": "noise suppression", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "JSC SMA NT Paper WAD Data Extraction", "bureau": "JSC: Johnson Space Center", "problem": "This application aims to provide the Quality Flight Equipment Division with a strong paper Work Authorization Document (WAD) data extraction capability using AI models. The data inputs include scanned PDF versions of original paper Work Authorization…", "outputs": "Information on scanned PDF documents.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "SVM Classification of Write-in Topics from MSD Customer Sat Survey", "bureau": "LaRC: Langley Research Center", "problem": "Support Vector Machine used to analyze write-in feedback from annual NASA MSD Customer Sat Survey, to classify comments into roughly 30 topics for visualization by Voice of the Customer Explorer (VOCE) dashboard", "outputs": "Classification of comments into a taxonomy of themes", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "General AI LLM Chatbot for NASA CUI Data: Accelerated Langley Transformation Initiative Research Assistant (ALTIRA)", "bureau": "LaRC: Langley Research Center", "problem": "Accelerated Langley Transformation Initiative Research Assistant (ALTIRA) is an artificial intelligence (AI) chatbot planned for general-purpose use for processing NASA CUI (not ITAR) data.", "outputs": "The first phase of this project will establish a minimum-viable-product utilizing Anthropic Claude 3.5 - based large language model (LLM) AI chatbot as quickly as possible to meet NASA needs. Phase…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "Intelligent Chatbot for Science using Microsoft Copilot", "bureau": "MSFC: Marshall Space Flight Center", "problem": "It uses Microsoft's Large Language model with scientifically curated information from NASA's VEDA (Visualization, Exploration, and Data Analysis) platform to assist users in search, discovery and analysis.", "outputs": "scientifically curated information", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "NASA", "agency_name": "National Aeronautics and Space Administration", "name": "Athena (EM32 Document Intelligence)", "bureau": "MSFC: Marshall Space Flight Center", "problem": "Converts 490K+ EM32 repository files into a searchable knowledge fabric. Supports 691+ file extensions, builds cross-document graphs, enables text-to-image queries, OCR, and compliance-aware retrieval. Provides sub-second query response for cached content.", "outputs": "cross-document graphs, enables text-to-image queries, OCR, and compliance-aware retrieval.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "NEA", "agency_name": "National Endowment for the Arts", "name": "External \"Public Facing\" Chatbot", "bureau": "NEA", "problem": "Public service chatbot to provide responses to grant applicant and NEA constituent questions", "outputs": "Grant application answers and NEA answers", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "NSF", "agency_name": "National Science Foundation", "name": "A document summarizer via Natural Language Processing", "bureau": "Directorate of Engineering (ENG)", "problem": "The AI system utilizes state-of-the-art transformer-based models (e.g., BART, Pegasus, T5, SciBERT) to perform text summarization. These models are deployed entirely within the agency’'s secure firewalls, ensuring no external data exposure and no external…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "", "categories": [ "summary" ] }, { "agency": "NSF", "agency_name": "National Science Foundation", "name": "ServiceNow GenAI \"Now Assist\"", "bureau": "Office of Information and Resource Management (OIRM)", "problem": "ServiceNow GenAI is a FedRAMP High certified system to accelerate digital transformation using Now Assist. Automatically generate content such as responses, work notes, and knowledge base articles related to customer service experience. Increase productivity…", "outputs": "", "stage": "Deployed", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "NSF", "agency_name": "National Science Foundation", "name": "Open-source topic identification for the Public Engagement with Science Initiative data analysis.", "bureau": "Office of Integrative Activities (OIA)", "problem": "OIA team proposes using unsupervised Natural Language Processing (NLP) to identify topical themes within responses submitted to the NSF public engagement questionnaire. The AI system will include multiple open-source techniques used together to achieve this…", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "", "categories": [ "summary" ] }, { "agency": "NSF", "agency_name": "National Science Foundation", "name": "Microsoft 365 (M365) AI Builder pre-built Generative Pre-trained Transformer (GPT) for Summarization", "bureau": "Office of Integrative Activities (OIA)", "problem": "OIA team requests to use the existing AI Builder pre-built Generative Pre-Trained Transformer (GPT) functions within Microsoft Power Platform to perform summarization tasks on text and tag data in Microsoft 365 (M365) to support: 1. Creation of Planner tasks…", "outputs": "", "stage": "Deployed", "high_impact": false, "technique": "", "categories": [ "summary" ] }, { "agency": "NTSB", "agency_name": "National Transportation Safety Board", "name": "Voice to text transcription", "bureau": "NTSB", "problem": "Speed and efficiency of screening vehicle voice recorders", "outputs": "Draft speech to text transcription", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "NTSB", "agency_name": "National Transportation Safety Board", "name": "FOIAXpress AI assistant", "bureau": "NTSB", "problem": "Timely review of documents for public release", "outputs": "Identifies potential PII or other details that may requre redaction prior to release", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "PBGC", "agency_name": "Pension Benefit Guaranty Corporation", "name": "IT Service Management Virtual Agent and Automation", "bureau": "ITIOD/Enterprise", "problem": "Automation, Content Generation Information Technology (IT) incident management is a continuous and at times repetitive, resource-consuming task.", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "PBGC", "agency_name": "Pension Benefit Guaranty Corporation", "name": "Pension Plan Summary Report Assistance", "bureau": "OBA", "problem": "Automation, Research, Data Analysis Complex, cumbersome, and often labor intensive/long time duration to evaluate large documents and data stores", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "PBGC", "agency_name": "Pension Benefit Guaranty Corporation", "name": "AI-Assisted Recognition and Redaction for Legal/Litigation Matters", "bureau": "OGC/GLOD", "problem": "Automation, Data Analysis Manual review and redaction of audio and images can be labor intensive and potentially error prone.", "outputs": "", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Chatbots for internal research", "bureau": "Advocacy: Office of Advocacy", "problem": "OFO agents need quick access to relevant policy documents and guidance materials to efficiently respond to inquiries without manually searching through knowledge repositories.", "outputs": "Access AI chatbots (e.g., claud, chatgpt) to improve internal research. Inputs would be questions on research topics relevant to SBA policy and programs. Outputs would be synthesized information…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Amazon Q in Connect", "bureau": "OCIO: Office of the Chief Information Officer", "problem": "Staff need quick access to relevant information and guidance to efficiently respond to inquiries and make decisions.", "outputs": "GenAI customer service assistant for Real-time transcription & recommendations; automatically detects customer intent during calls and chats; provides agents with immediate, real-time generative…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence", "translation" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Generative AI for SBA.gov product training", "bureau": "OCIO: Office of the Chief Information Officer", "problem": "Staff need quick access to relevant information and guidance to efficiently respond to inquiries and make decisions.", "outputs": "ChatGPT can serve as an interactive learning tool for training new and existing staff on MySBA Customers & Portal best practices, policies, and procedures through engaging and effective training…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Secure IL5 AI", "bureau": "OCIO: Office of the Chief Information Officer", "problem": "Government teams need secure AI capabilities that meet federal security requirements for analyzing sensitive data, generating content, and streamlining knowledge work without compromising data security or compliance.", "outputs": "FedRAMP High and DoD IL5/IL6/Top Secret authorized generative AI platform designed for government and defense. Provides secure access to multiple large language models for text, image, and audio…", "stage": "Retired", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Employee Work Prioritization AI Agent", "bureau": "OCIO: Office of the Chief Information Officer", "problem": "Staff require versatile AI assistance for diverse tasks including content creation, coding, translation, research, and problem-solving. Single-purpose tools create inefficiencies and context-switching.", "outputs": "Multimodal AI assistant with advanced capabilities in text generation, code generation, machine translation, question answering, image analysis, and creative problem-solving. Trained on extensive…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Developer Code Assistant AI", "bureau": "OCIO: Office of the Chief Information Officer", "problem": "Software developers spend significant time on repetitive coding tasks, writing boilerplate code, creating documentation, generating unit tests, and conducting code reviews. Legacy code modernization is complex and time-consuming. Understanding large…", "outputs": "Generative AI-powered coding assistant for building, operating, and transforming software with advanced agentic capabilities. Provides real-time code suggestions from snippets to full functions…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Multi-Agent Orchestration", "bureau": "OCIO: Office of the Chief Information Officer", "problem": "SBA maintains millions of lines of legacy ColdFusion and COBOL code supporting mission-critical lending systems (CAFS) with minimal test coverage and significant technical debt. Manual code refactoring, test case generation, and project management across…", "outputs": "Multi-agent system using Amazon Bedrock with foundation models. Agent Teams coordinate specialized agents for: (1) Code Refactoring — analyzes legacy ColdFusion/COBOL code and recommends…", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Email, Document, and Composing AI Editor", "bureau": "OCPL: Office of Communications & Public Liaison", "problem": "Government communications require professional, clear, and error-free writing across diverse documents and correspondence. Manual proofreading is time-consuming and may miss subtle errors or tone inconsistencies.", "outputs": "AI-powered writing assistant that provides real-time grammar, spelling, punctuation, style, and tone corrections. Uses natural language processing and machine learning to analyze text context and…", "stage": "Pilot", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "SBA.gov External Customer Chatbot", "bureau": "OCPL: Office of Communications & Public Liaison", "problem": "Customer service teams are overwhelmed with routine inquiries that slow response times and prevent agents from focusing on complex, high-value customer issues. Manual case classification and routing is time-consuming and inconsistent. Customers expect 24/7…", "outputs": "AI-powered autonomous customer service agent and chatbot integrated into Salesforce CRM. Uses natural language processing (NLP), generative AI, and machine learning to understand customer intent,…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Meeting Minutes Summary Tool", "bureau": "OFO: Office of Field Operations", "problem": "Manually creating meeting minutes and summaries is time-consuming and may result in inconsistent documentation of key discussion points and action items.", "outputs": "Today we hosted a Teams meeting with partners and stakeholders and one of them added Read Assistant to the meeting. We did receive an email after the fact providing the meeting summary if we had a…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "Internal OFO Chatbot Recommender", "bureau": "OFO: Office of Field Operations", "problem": "OFO agents need quick access to relevant policy documents and guidance materials to efficiently respond to inquiries without manually searching through knowledge repositories.", "outputs": "OFO agents will access the chatbot via the desired internal website interface, initiating conversations by typing inquiries. The chatbot analyzes inputs and provides approved documents as relevant…", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "SBA", "agency_name": "Small Business Administration", "name": "ChatGPT for OMS Marketing", "bureau": "OFO: Office of Field Operations", "problem": "Staff need quick access to relevant information and guidance to efficiently respond to inquiries and make decisions.", "outputs": "ChatGPT (OpenAI) allows users to input prompts/data and then provides answers and outlines. An example of an OMS using it would be to help with an outline for an upcoming newsletter for NSBW and…", "stage": "Retired", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "8-K Filing Solution", "bureau": "Division of Corporate Finance (CF)", "problem": "Business users now can quickly go through the details to make a informed decisions using AWS image recognition AI service", "outputs": "Extracts of filing data.", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "CF Staff Review Comments", "bureau": "Division of Corporate Finance (CF)", "problem": "The solution will help in classifying comments letters.", "outputs": "SEC staff comment categories", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Disclosure Review Chatbot", "bureau": "Division of Corporation Finance (CF)", "problem": "Disclosure Review Automation (DREAM) Chatbot helps division staff search within review guides and generating detailed responses to disclosure review questions", "outputs": "Real-time answers to staff questions about disclosure rules, filing procedures, and review protocols. Contextual guidance based on historical filings, internal policies, and SEC regulations.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Optical Character Recognition (OCR) for Records Analysis", "bureau": "Division of Enforcement (ENF)", "problem": "Extract structured data from unstructured digital image files of checks.", "outputs": "A text string of all text recognized from the associated image.", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Westlaw Co-Counsel", "bureau": "Division of Enforcement (ENF), Division of Examinations (EXAMS), Office of the Chief Data Officer (OCDO), Office of the General Counsel (OGC)", "problem": "The AI output will facilitate legal research, document analysis, and document production.", "outputs": "Document summaries, document drafts, workflow analysis, and answers to user-selected questions", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Lexis Protege", "bureau": "Division of Enforcement (ENF), Division of Examinations (EXAMS), Office of the Chief Data Officer (OCDO), Office of the General Counsel (OGC)", "problem": "The AI output will facilitate legal research, document analysis, and document production.", "outputs": "Document summaries, document drafts, workflow analysis, and answers to user-selected questions.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Chatbot with SEC Documents", "bureau": "Division of Examinations (EXAMS)", "problem": "This application includes a knowledge base of internal SEC guidelines and procedures. It will help answer staff questions on those guidelines/procedures.", "outputs": "This application provides summarizations and responses to questions based on the contents of a corpus of documents and internal SEC guidelines..", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Knowledge base of federal securities law and chatbot", "bureau": "Division of Investment Management (IM)", "problem": "Using AI to carry out basic research on federal securities laws.", "outputs": "Summary and links to relevant snippets from documents from IM-curated library.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Knowledge base of crypto comments and chatbot", "bureau": "Division of Investment Management (IM)", "problem": "Using AI to carry out basic research on opinions and submitted comments related to crypto assets.", "outputs": "Summary and links to relevant snippets from documents from IM-curated library.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Rule and Comment Organization and Summarization", "bureau": "Division of Trading and Markets (TM), Office of the Chief Data Officer (OCDO), Division of Economic and Risk Assessment (DERA), Division of Investment Management (IM), Division of Corporation Finance (CF)", "problem": "Downloading, navigating, searching, and understanding comments and rule filings can be slow and burdensome using existing tools.", "outputs": "The tool generates summaries and summaries of summaries using LLMs.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "EDGAR Technical Support Public Chatbot", "bureau": "EDGAR Business Office (EBO)", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Comparing EDGAR system access request forms", "bureau": "EDGAR Business Office (EBO)", "problem": "The AI helps extract information from scanned documents and compares it with information provided on a digital form", "outputs": "Verification that the scanned documents information matches the submitted digital forms.", "stage": "Pre-deployment", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Extracting data from FOIA request forms", "bureau": "Office of Information Technology (OIT)", "problem": "Capture and load data from a FOIA form into a SharePoint list for tracking through the eDiscovery Lifecycle. This alleviates time demands for manual entry from one system to another.", "outputs": "Extracts data from Microsoft word document to a SharePoint.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "forms" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Legal Research (Lexis+)", "bureau": "Office of the Chief Data Officer (OCDO)", "problem": "More efficient legal research.", "outputs": "AI-enabled extractions of text and AI-enabled type-aheads in search bars", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Legal Research (Westlaw Precision)", "bureau": "Office of the Chief Data Officer (OCDO)", "problem": "More efficient legal research.", "outputs": "AI-enabled extractions of text and AI-enabled type-aheads in search bars", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "SEC", "agency_name": "Securities and Exchange Commission", "name": "Create description of documents for records management in sharepoint", "bureau": "Office of the Chief Operating Officer (OCOO)", "problem": "Creating descriptions and other record keeping metadata on documents.", "outputs": "The AI system creates metadata for documents stored in SharePoint.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Handwriting Recognition of Forms; Intelligent Document Processing Platform - Forms Classification and Data Extraction", "bureau": "Chief Information Officer", "problem": "This AI use case classify, identify and extract structured, semi-structured forms containing printed and handwritten fields. The use case increases automation, reduces clerical errors and reduces customer wait times.", "outputs": "The output of extracted data fields on the form is represented in JSON format that can be available to downstream systems using API and through other integration mechanism such as queues.", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Agency Support Companion (ASC) chatbot", "bureau": "Chief Information Officer", "problem": "Generative AI model to create content, summarization and research capabilities", "outputs": "Chatbot responses to users as well as management information reports for executives Management information reports, in the form of a dashboard, for executives to track ASC usage.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Policy Assistance Tool (PAT)", "bureau": "Chief Information Officer", "problem": "The system is designed to help employees quickly and easily find accurate policy information, improving efficiency and the user experience for those who need to access agency policies.", "outputs": "Chatbot responses to users that are grounded in SSA policy data and provide links to policy documents referenced in the response. Management information reports, in the form of a dashboard.", "stage": "Pilot", "high_impact": true, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "General Use Chatbot", "bureau": "Chief Information Officer", "problem": "Generative AI model to create content, summarization, analyzation and research capabilities", "outputs": "Chat responses to users", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "AI Comment Analysis", "bureau": "Chief Information Officer", "problem": "The AI comment analysis system supports the decision-making of SSA public comment reviewing staff by making preliminary designations as to whether a given public comment (or select attachment content) contains any of a set of predefined types of flagged…", "outputs": "The system outputs a spreadsheet containing a catalog of comment content reviewed, designations as to whether the AI model concluded each targeted form of content was or was not present. If deemed…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Continuing Disability Review Model", "bureau": "Chief Information Officer, Analytics and Improvements", "problem": "This AI use case identifies disability cases with the greatest likelihood of medical improvement and flag them for a continuing disability review to improve the accuracy of program administration.", "outputs": "The system outputs numerical scores for each case: the score represents the likelihood of medical improvement. The probabilities are binned according to low, medium, or high.", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Proactive Triage and Analysis of Hearings (PATH)", "bureau": "Chief Information Officer, Analytics and Improvements", "problem": "This AI use case flags high likelihood favorable claims and refers them to human adjudicators for further review to determine eligibility for on-the-record hearing decisions.", "outputs": "The system outputs sorted case listings based on likelihood of favorable hearing decision.", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Customer Insight Tool", "bureau": "Chief Information Officer, Analytics and Improvements", "problem": "SSA uses a survey system to deliver and manage customer surveys. Part of the deployed system includes an AI capability for performing text analytics for identifying and tracking customer sentiments and key trends.", "outputs": "Survey analysis tool that evaluates sentiment and categorization. Current capabilities include: 1. Machine Translation - using algorithms to automatically translate text from one language to another…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Mobile Wage Reporting", "bureau": "Chief Information Officer, Digital Customer Solutions", "problem": "Uses AI to extract text/data from scanned images/documents representing pay stubs or payroll information to enable faster processing.", "outputs": "Gross pay and pay date from the paystub image", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Speech to Text Video Transcription", "bureau": "Chief Information Officer, Disability Information Systems", "problem": "Generate transcript from recorded audio of disability hearings", "outputs": "Transcript", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Therapy Chatbot - Text-Based Mental Health Support for SSA Employees", "bureau": "Human Resources", "problem": "This AI use case is a therapy chatbot that employees can access on their own personal devices and is designed to provide confidential, immediate mental health support to employees, helping address stress, emotional challenges, and limited access to…", "outputs": "The chatbot delivers real-time emotional support, personalized coping strategies, wellness recommendations, and alerts to live counselors when needed.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Human Resource Chatbot", "bureau": "Human Resources", "problem": "Generative AI chatbot grounded in SSA Personnel Policy Manual and other HR related documents that provides HR staff with the ability to search HR policy and related documents using natural language to answer SSA employee inquiries, analyze policies and HR…", "outputs": "Chatbot responses to users prompts that are grounded in SSA HR policy data and other associated HR documents.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "SSA", "agency_name": "Social Security Administration", "name": "Quick Disability Determinations Model", "bureau": "Law and Policy, Disability Policy", "problem": "Used to screen initial applications to identify cases where a favorable disability determination is highly likely and medical evidence is readily available to prioritize this workload and expedite case processing.", "outputs": "System output consists of probability model scores for each separate scoring service, Scoring Service for Allowance and Scoring Service for Processing Time, and the overall QDD Score.", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "AI Input in Translation", "bureau": "A/PRI", "problem": "Leveraging machine-based tools to streamline workflows in translation work. Machine output is carefully post-edited by professional human translators to ensure highest quality product.", "outputs": "Translated text in draft form.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "FOIA Web ML Document Indexer", "bureau": "A/SKS", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "forms" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Travel.State.Gov (TSG) Enhanced Search and Chatbot", "bureau": "CA", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Translation of Consular Content using AI", "bureau": "CA", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Live Consular AI Language Augmentation (LCALA) - Visa Interview Pilot", "bureau": "CA", "problem": "Language gaps between applicants and non-native-speaker adjudicators create inconsistent, ad-hoc translation during brief (~3-minute) interviews or is limited to questions the adjudicator is familiar with asking, increasing the risk of misunderstanding…", "outputs": "LCALA provides real-time transcription and neural machine translation of spoken exchanges at the interview window, delivering translated audio and on-screen text on the device; when enabled, it can…", "stage": "Pilot", "high_impact": true, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Live Consular AI Language Augmentation (LCALA) - OCS/ACS Pilot", "bureau": "CA", "problem": "LCALA OCS/ACS Pilot aims to close language gaps in routine, time-sensitive citizen services by providing on-demand interpretation for calls and in-person interactions. Today, limited interpreter availability and uneven reliance on bilingual staff can cause…", "outputs": "LCALA provides real-time transcription and neural machine translation of spoken exchanges on demand, delivering translated audio and on-screen text on the device; when enabled, it can generate a…", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Within Grade Increase Data Extraction Automation", "bureau": "CGFS", "problem": "Moving large amounts of data from paper and/or digital forms into a digital system can be challenging, time intensive, and costly.", "outputs": "Tabulated dataset of extracted values referred for human review.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "DS-5528 Promissory Note Automation", "bureau": "CGFS", "problem": "Moving large amounts of data from paper and/or digital forms into a digital system is challenging, time intensive, and costly.", "outputs": "Dataset of extracted information referred for human review.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "StateInsight", "bureau": "CGFS", "problem": "The need to summarize documents to produce a description of procurement awards and to allow authorized users to ask questions of the documents.", "outputs": "Summary of documents to produce a description of procurement awards and responses to questions asked about the documents.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "FOIA 360 AI Matching Tool", "bureau": "DT", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "forms" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "AI Research Engine (AIRE)", "bureau": "DT", "problem": "The need to improve the quality of reporting from the continuously growing sources of information through the categorization, summarization, and translation of information. Includes formerly titled use case \"J Reports Data Collection & Management Tool (DCT).\"", "outputs": "Summarized information, translated documents, and sorted data.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "StateChat", "bureau": "DT", "problem": "The need to safely and securely access, share, summarize, and research Sensitive But Unclassified (SBU) Department information. Improve and reduce time to translating documents, summarizing reports, and drafting emails. StateChat is the Department's…", "outputs": "StateChat is a chatbot interface enriched with tools that generate formatted paper products, offer rapid and transparent searches of internal documents, and allow for research, synthesis, and…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence", "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Electronic Health Record AI Enhancements", "bureau": "DT", "problem": "Provide efficient, accurate, and comprehensive medical electronic health record (EHR) management, patient care, and administrative workflows by leveraging AI-powered tools, including large language models (LLMs) and natural language processing (NLP).", "outputs": "Automated data extraction, validation, sentiment analysis, categorization, identification of document types, data discrepancies, and text summarization into structured medical chart components.", "stage": "Deployed", "high_impact": true, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "TIP Report Research Translation", "bureau": "DT", "problem": "The TIP Report Translation AI system provides informal, unofficial translations for materials related to the annual Trafficking in Persons report. This capability saves staff time and allows researchers and drafters to focus on critical report needs, such as…", "outputs": "Informal, unofficial translations of PDFs and Microsoft word documents. Each document includes a watermark denoting that the AI translation is unofficial and must be reviewed by a human.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "FA.gov RedactAid", "bureau": "F/FAO", "problem": "The need to improve data quality of information and prevent sensitive data from being published to ForeignAssistance.gov.", "outputs": "Sensitive information is identified and flagged prior to ForeignAssistance.gov publication.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Digital Media Analytics Platform", "bureau": "GPA", "problem": "Using open-source neural machine translation models to translate global media articles and Department and public foreign social media posts into English.", "outputs": "Summaries of large volumes of foreign language news and online posts to help teams identify and understand trends in a more efficient manner.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Rosie Chat Bot", "bureau": "MGT", "problem": "The need for employees to locate consistent, accurate answers to HR-related questions quickly based on internal information.", "outputs": "Contextual answers and emails.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Utility Invoices Data Extraction", "bureau": "MGT", "problem": "The need to reconcile and process payments faster.", "outputs": "Key details from utility invoices, such as the account name, amount due, and consumption for processing payments and for preparing the monthly consumption reports.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "AI SharePoint Chatbot", "bureau": "MGT", "problem": "The need for efficient access to post-specific information.", "outputs": "Answers to post-specific questions via a chatbot integrated into the Embassy SharePoint site.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "Creating Persistent Virtual Reality Personas for Dynamic Training", "bureau": "NFATC", "problem": "There is a need to improve effectiveness of immersive training in the future. The team develops a dynamic integration between multiple AI tools to create stored/persistent \"personas\" that can be interacted with during training-related virtual reality (VR)…", "outputs": "Persistent Personas; Speech-to-text API; Language Translation", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "STATE", "agency_name": "Department of State", "name": "WHA/EX Information Management", "bureau": "WHA", "problem": "Knowledge Management for administrative and IT related processes. Formerly known as \"Low Earth Orbit (LEO) – Budget Office Inquiries\" to answer questions frequently asked about budget.", "outputs": "Generative AI or logic-based responses with answers to administrative/IT related FAQs.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "STB", "agency_name": "Surface Transportation Board", "name": "Copilot for Appian", "bureau": "Surface Transportation Board", "problem": "Facilitate analysis of filings and data submitted by parties in proceedings", "outputs": "Summaries and analyses of filings and data submissions.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "AskBEP AI ChatBot", "bureau": "Bureau of Engraving and Printing (BEP)", "problem": "The AskBEP chatbot addresses the challenge of quickly and consistently accessing policy information within the BEP community. Employees often need clear, accurate answers without having to search through lengthy documents or rely on staff availability. The…", "outputs": "The AskBEP chatbot generates natural language responses to user queries in a conversational format. These outputs consist of policy information, guidance, or references to relevant resources.…", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Large Language Model (LLM) Chatbot for Proof of Concept", "bureau": "Bureau of the Fiscal Service (BFS)", "problem": "Code Assistance - Generating, debugging, and documenting code", "outputs": "Code comments, documentation, improvement suggestions", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Large Language Model (LLM) Coding for Proof of Concept", "bureau": "Bureau of the Fiscal Service (BFS)", "problem": "Searching for agency information using a knowledge retrieval system. Contextual Research (Exploring policies, regulations, trends, and strategic topics).", "outputs": "FAQ handling and Case Deflection", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "GenAI Productivity Tool Pilot", "bureau": "Bureau of the Fiscal Service (BFS)", "problem": "Transcribing, summarizing, or other efforts that improve the accessibility of a virtual meeting or interview using AI. (Meeting Summarization and Recap - Generating notes, action items, and summaries from meetings). Generating first drafts of documents,…", "outputs": "Multiple depending on application used or general browser based use.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "translation" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Knowledge Retrieval Chatbot POC", "bureau": "Bureau of the Fiscal Service (BFS)", "problem": "Contextual Research (Exploring policies, regulations, trends, and strategic topics). Knowledge Retrieval (Locating known information and presenting or analyzing the information).", "outputs": "Chatbot to search documents and other data", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "FORMS AI", "bureau": "Bureau of the Fiscal Service (BFS)", "problem": "Data Analysis and Manipulation", "outputs": "Convert PDFs to extract data fields that can be used to automate workflows", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "review" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Establish localized Large Language Model (LLM)", "bureau": "Bureau of the Fiscal Service (BFS)", "problem": "AI Enablers (Enterprise solutions that enable AI to integrate into IT services infrastructures)", "outputs": "AI-Powered Service Performance Metrics. Efficiency and Responsiveness: Track reductions in response times versus the pre-LLM baseline, first-contact resolution without escalation, and inquiry…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence", "forms" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "SBSE - Payments Topics Chatbot", "bureau": "Internal Revenue Service (IRS)", "problem": "Provide self service capabilities to taxpayers seeking information about certain tax topics.", "outputs": "The NLP algorithm functions by classifying user inputs into predefined topics using advanced natural language processing techniques. It evaluates the input against a set of trained models and…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Joint Committee on Taxation Review Research Aide", "bureau": "Internal Revenue Service (IRS)", "problem": "The Joint Committee on Taxation Review (JCTR) Research Aide is a generative AI (GenAI) system that uses Retrieval Augmented Generation (RAG) search to answer questions on JCTR policies and procedures that are provided in natural language. This tool has a use…", "outputs": "This system uses an embedding model to produce a vector representation of texts for contextual searching, and Large Language Model to generate a response to user inquiries. Outputs of the tool…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Internal Revenue Manual Research Aid (IRMA)", "bureau": "Internal Revenue Service (IRS)", "problem": "The Internal Revenue Manual (IRM) Research Aide (IRMA) is a generative AI (GenAI) system that uses Retrieval Augmented Generation (RAG) search to answer questions on IRM policies and procedures that are asked in natural language. This tool has a custom user…", "outputs": "This system uses an embedding model to produce a vector representation of texts for contextual searching and a Large Language Model (LLM) to generate a response to user inquiries. Outputs of the…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Volunteer Income Tax Assistance (VITA) Generative AI Chatbot Proof of Concept", "bureau": "Internal Revenue Service (IRS)", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Ask-CFO Research Aide", "bureau": "Internal Revenue Service (IRS)", "problem": "Ask-CFO is an extension of the Research Aide developed using a COTS platform originally designed for the Joint Committee on Taxation Research Aide (JCR) and Internal Revenue Manual Research Aid (IRMA) use cases. It is a Retrieval Augmented Generation (RAG)…", "outputs": "This system uses an embedding model to produce a vector representation of texts for contextual searching, and Large Language Model to generate a response to user inquiries. Outputs of the tool…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Digital Forensics Chatbot for IRS Employees", "bureau": "Internal Revenue Service (IRS)", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Taxpayer Services Chatbot", "bureau": "Internal Revenue Service (IRS)", "problem": "This project implemented the Taxpayer Services chatbot, which provides self-service Frequently Asked Questions (FAQ) to taxpayers and international taxpayers regarding refunds, status of refunds, amended returns, Employer Identification Number (EIN), Earned…", "outputs": "The NLP algorithm functions by classifying user inputs into predefined topics using advanced natural language processing techniques. It evaluates the input against a set of trained models and…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Machine Translation", "bureau": "Internal Revenue Service (IRS)", "problem": "To better facilitate and automate content translation efforts at the IRS, a cloud-based commercial off-the-shelf (COTS) solution is being leveraged for use by the Linguistic, Policy, Tools and Services (LPTS) Team. The Machine Translation (MT) application is…", "outputs": "The MT application focuses on translation for existing text and labels into Spanish and other languages with the ultimate goal of becoming an enterprise solution for a variety of non-English…", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "translation" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Employee Resource Center (ERC) Chatbot", "bureau": "Internal Revenue Service (IRS)", "problem": "Enable self-service for employees seeking information about various employee-related topics.", "outputs": "The NLP algorithm functions by classifying user inputs into predefined topics using advanced natural language processing techniques. It evaluates the input against a set of trained models and…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Winnie Chatbot for Employee IT FAQs", "bureau": "Internal Revenue Service (IRS)", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Synthetic Data Engine", "bureau": "Internal Revenue Service (IRS)", "problem": "This AI based synthetic data generator provides synthetic data for a robust, large-scale dataset consistent with US Population Data Heuristics for testing and advanced analytics by dimensionalizing over 180 elements of socioeconomic data and over 300…", "outputs": "AI is capable of outputting 3 tax years of IMF and BMF returns starting in Tax Year 2022. With each XML Schema Definition (XSD) that is released under the Modernized e-File (MeF), system implements…", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "translation" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "AI Contract Document Toolbox", "bureau": "Internal Revenue Service (IRS)", "problem": "Extensive documentation is required in official contract files when procuring goods and services for agency mission needs. Procurement document drafting is currently largely manual and time-consuming. Further quality reviews of documents add more time to the…", "outputs": "The AI system generates accurate, contextually relevant, and human readable text outputs based on user input queries and uploaded documents. Its outputs include: Document Summaries., Draft…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Modular Code Assistant (Explain & Refactor Small Methods) for Automation Test Application Development", "bureau": "Internal Revenue Service (IRS)", "problem": "Assist test automation team by explaining or suggesting improvements to individual Java methods or classes below 100 lines. Ideal for improving understanding and accelerating onboarding to automation test as a new software engineer in application development.", "outputs": "Answer detailed project-specific queries. Interpret and simplify complex automation test reports. Potentially aid in troubleshooting and debugging automation scripts.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Modernization Accelerator - Legacy Applications Chatbot & Code Conversion", "bureau": "Internal Revenue Service (IRS)", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Ticket Management Generative AI Pilot", "bureau": "Internal Revenue Service (IRS)", "problem": "As part of a Chief Technology Officer (CTO) sponsored objective to enable and utilize Generative Artificial Intelligence (Gen AI) capabilities the User and Network Services Information Technology (IT) Service Desk in cooperation with Enterprise Operations…", "outputs": "Incident Summarization; providing agents with concise summary of case notes and history, Resolution Notes generation; generating accurate resolution notes based on actions taken and solution…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Statistical Information Services Customer Correspondence Analytics", "bureau": "Internal Revenue Service (IRS)", "problem": "Statistics of Income (SOI) has collected years' worth of customer correspondence. While having this information has been useful for historical documentation proposes, it is apparent that moving forward we need to evaluate the trends in requests, identify…", "outputs": "The output of the AI system will be summary evaluations, via [name removed] capabilities, of the past several years of customer correspondence.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "1040X AI Transcription", "bureau": "Internal Revenue Service (IRS)", "problem": "This project is meant to improve IRS capabilities to extract information from images and pdfs.", "outputs": "The system takes in images, classifies them based on what IRS form they are, and then extracts requested information which is then stored in a table.", "stage": "Pre-deployment", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake", "translation" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Integrate Word Processor AI for Digitalization", "bureau": "Internal Revenue Service (IRS)", "problem": "The intended purpose of the Digitalization Enablement Platform (DEP) and the integration of AI into processing taxpayer submitted documents is to minimize the transcription and storage of paper documents, streamline case management, and enhance the accuracy…", "outputs": "The AI will extract taxpayer information from IRS forms agnostic of any demographic bias, in accordance with federal AI use regulations and standards.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake", "translation" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Using Text Summarization for Open-Ended Survey Responses", "bureau": "Internal Revenue Service (IRS)", "problem": "Analyzing open-ended survey responses is very time intensive. This project seeks to reduce the time that analysis takes.", "outputs": "The AI will output a summary of all the open-ended responses. This summary will capture all the main themes and important details of the open-ended responses.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "TP360 IRM Research Companion", "bureau": "Internal Revenue Service (IRS)", "problem": "AI is to assistant CSR as Knowledge Management Librarian for natural language querying to find solutions, improving IRM research thus minimizing time spent and redundant research. Currently CSR do a basic search on SERP…", "outputs": "Provide concise and precise answer to the query and provided IRM references for further investigation if needed", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Form 4546 IDR Question-Answering Automation", "bureau": "Internal Revenue Service (IRS)", "problem": "Use of Large Language Models (LLMs) to automatically assess whether taxpayers have addressed the questions raised by Large Business and International (LB&I) Revenue Agents in Information Document Requests (IDRs) responses.", "outputs": "Document that details if the IDR has been answered and if it is answered then generate a summary.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "ChatOFR AI Chatbot", "bureau": "Office of Financial Research (OFR)", "problem": "In-house AI platform providing OFR staff with access to multiple large language models for question-answering guidance, text generation and summary, and code development assistance", "outputs": "Responses to queries using multiple LLM models provided by vendor. Includes access to specialized knowledge bases. Provides IT and Research staff with code generation, debugging, and optimization…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Automated Document Processing", "bureau": "Office of Financial Research (OFR)", "problem": "Converting legacy PDFs and chart images into structured datasets for faster research ingestion", "outputs": "Structured datasets extracted from PDFs and images including tables charts and text data in machine-readable formats", "stage": "Pre-deployment", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Operational Support Bots", "bureau": "Office of Financial Research (OFR)", "problem": "Chatbots for IT service-desk tasks ticket triage password resets and resource provisioning to free analysts for higher-value work", "outputs": "Automated responses to IT support queries ticket categorization and routing password reset confirmations and resource provisioning status updates", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "OCC.Chat", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "GenAI chatbot that generates human-like responses to user questions using public knowledge for reference", "outputs": "The system generates human-like responses to user questions, drawing from public knowledge databases for accurate reference. The outputs include text-based answers that are contextually appropriate,…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "OCC.InfoAssist", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "GenAI chatbot that generates human-like responses to user questions using OCC Comptroller handbooks and PPMs documents", "outputs": "The system generates human-like responses to user questions, specifically referencing content from OCC Comptroller handbooks and PPMs documents. The outputs include detailed, contextually relevant…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Rulemaking Comment Analytics", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "The rulemaking process generates large volumes of public comments that require timely and consistent analysis. Manual review is labor-intensive, slow, and prone to variability, creating delays in identifying sentiment, themes, and stakeholder concerns. We…", "outputs": "The AI tool will produce structured summaries of public comments, including sentiment analysis, key themes, and recurring issues. It's output will provide regulators with a consolidated view of…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary", "correspondence" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "OCC Guidance Summarization", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "OCC staff must frequently review a wide range of OCC regulatory sources—Handbooks, Bulletins, statutes, old Circulars, Interpretive Letters, Corporate Actions, and related guidance—to understand requirements and develop supervisory positions. Manually…", "outputs": "The AI will generate targeted summaries of relevant regulatory documents, highlight key obligations and thematic concepts, and provide references to specific OCC guidance sources applicable to the…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Regulation Summarization", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "Regulatory updates and final rules associated with the regulatory programs are extensive, complex, and often highly technical. Staff must manually review lengthy documents to extract relevant requirements, interpret changes, and understand impacts on…", "outputs": "The AI will produce concise summaries of regulatory sections, highlight significant changes introduced by the Final Rule, and extract obligations, definitions, and compliance considerations relevant…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Meeting Minute Summarization", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "Ability to synthesize information based on extensive discussions, action items, and decisions that must be captured accurately in meeting minutes. Manual compilation of these minutes is time-consuming, varies in quality, and can distract staff from active…", "outputs": "The AI will generate structured, concise summaries of meeting minutes, capturing major discussion points, decisions made, action items, and assigned responsibilities. Outputs may include narrative…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Regulatory Rule Analysis", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "Comment letters on major rulemakings can be lengthy and numerous, making it difficult for staff to quickly understand the range of viewpoints and the main arguments presented. The AI helps by reviewing these submissions and distilling them into clear…", "outputs": "The system delivers concise summaries of comment letters, capturing key themes, concerns, recommendations, and areas of consensus or disagreement. These outputs give staff a clearer picture of the…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Public Information Analysis", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "Review of transcripts can be time-consuming, especially when trying to compare themes, identify shifts in tone, or spot notable changes. The AI helps streamline this work by quickly analyzing the transcripts and pulling together the main similarities,…", "outputs": "The system generates summaries of individual transcripts, identifies patterns and highlights noteworthy variations, and produces a consistent executive-level overview. These outputs give users a…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Narrative Summary and Thematic Analysis", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "The AI helps cut down the time spent reading through lengthy reports. These documents can contain a lot of detail, and staff often need a quick understanding of the main points. The AI provides an easier way to get to the core issues without manually working…", "outputs": "The system generates a clear, readable summary of the report, calling out major themes, noteworthy issues, and any items that may need follow-up. These outputs help staff quickly understand what…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Framework Analysis", "bureau": "Office of the Comptroller of the Currency (OCC)", "problem": "Feedback on NPR's can be extensive and highly technical, making it challenging for staff to quickly absorb the main points raised by commenters. The AI helps by reviewing this feedback and organizing it into clear themes and insights, reducing the time…", "outputs": "The system produces organized summaries of comment feedback, highlighting recurring themes, areas of disagreement, emerging concerns, and key technical points. These outputs make it easier for staff…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "TREAS", "agency_name": "Department of the Treasury", "name": "Public Chatbot", "bureau": "Terrorism and Financial Intelligence (TFI)", "problem": "Public data is hard to sift through and results in calls to the helpline.", "outputs": "Aspirational, not yet funded. Chatbot interface that provides search results and summaries of public OFAC information, such as FAQs, executive orders, fact sheets, general licenses, statutes, and…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "TVA", "agency_name": "Tennessee Valley Authority", "name": "Land Management Records", "bureau": "Information Technology", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "TVA", "agency_name": "Tennessee Valley Authority", "name": "AWS Transcribe", "bureau": "Information Technology", "problem": "", "outputs": "", "stage": "Not reported", "high_impact": false, "technique": "", "categories": [ "translation" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Dam Inspection Report Document Processing", "bureau": "Farm Production and Conservation", "problem": "The AI use case allows our staff to identify the biggest issues and trends across 2,100+ dams.", "outputs": "The model outputs text and checkbox responses, including dam metadata, inspection issue responses (yes and no checkboxes), and further remarks on the issue regarding what has been or needs to be…", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "FSA FLP Chatbot", "bureau": "Farm Production and Conservation", "problem": "This AI use case is researching program handbooks and generating contextually and factually accurate responses from highly technical Farm Loan Program information.", "outputs": "The expected output is text answers to prompt questions.", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "HR Chatbot", "bureau": "Food Safety", "problem": "The HR chatbot provides support to Human Resources personnel by addressing benefits-related inquiries, particularly during the open season period.", "outputs": "Chat messages", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Chatbot for Meat and Poultry Hotline & OPACE Efficiency", "bureau": "Food Safety", "problem": "Live agents are overburdened trying to respond to a high volume of common food safety questions. Opportunity to streamline Office of Public Affairs and Consumer Education operations, freeing staff for higher-value tasks.", "outputs": "Chat messages", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Automated PDF Document Processing and Information Extraction", "bureau": "Marketing and Regulatory Programs", "problem": "Plant Protection and Quarantine (PPQ) has important program and workforce related information stored in thousands of PDFs, which cannot easily be used for analytics.", "outputs": "Machine-readable data tables", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Incident Invoice Document Understanding", "bureau": "Natural Resources and Environment", "problem": "Staff is overburdened by manual document handling, resulting in less time to focus on high-value strategic tasks. This tool automates data extraction from forms and invoices.", "outputs": "The Output is a CSV file with confidence level to be able to send the invoice data to the payment system", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Use of LLMs for data extraction", "bureau": "Natural Resources and Environment", "problem": "The purpose of this model is to quickly gather information from scientific papers to track the spread of plant diseases.", "outputs": "The AI extracts requested information from scientific papers (e.g., country name, pathogen name, host name, percent plants affected by disease, etc.)", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "ESRI Support Chat Bot", "bureau": "Natural Resources and Environment", "problem": "The purpose of this chatbot is to help handle requests for support with geospatial software between our team and the software vendor.", "outputs": "The chatbot outputs support ticket entries, code snippets for queries, and text and links for support ideas and answers.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "GIS Help Desk Chat Bot", "bureau": "Natural Resources and Environment", "problem": "Answer easier Geographic Information System (GIS) help desk questions based on published Knowledge Based Articles (KBAs) which are used to train the model.", "outputs": "Outputs text-based answers to user-submitted questions about geospatial applications and processes.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Forest Plan review and planning support", "bureau": "Natural Resources and Environment", "problem": "Opportunity to increase business productivity related to forest plan reviews by analyzing data and information at a rapid pace", "outputs": "Outputs data analysis and extracted information", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "TreeSearch", "bureau": "Natural Resources and Environment", "problem": "There is opportunity to streamline the usability of the US Forest Service (USFS) TreeSearch website, the primary database for Forest Service-sponsored research.", "outputs": "Provides conversational outputs through an AI Chatbot with a Human In The Loop (HITL) performing final data checks and quality assurance.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Infosec Chatbot", "bureau": "Natural Resources and Environment", "problem": "Information Security AI is a Chatbot that uses generative artificial intelligence (AI) to support users by responding to questions on information security.", "outputs": "Provides text outputs through retrieval-augmented generation (RAG) solution that uses data from National Institute of Standards and Technology (NIST) guidelines.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Recreation site chatbot", "bureau": "Natural Resources and Environment", "problem": "Estimating recreation use at recreation sites.", "outputs": "A dataset of text provided by recreation visitors.", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Forest Vegetation Simulator (FVS) Helpdesk Chatbot RAG", "bureau": "Natural Resources and Environment", "problem": "The Chatbot will respond to questions from users of the model (for both public and federal users), reducing staff time spent responding to questions.", "outputs": "Text-based output from a natural-language chat interface capable of answering common questions.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Alisha OCR Reader", "bureau": "Natural Resources and Environment", "problem": "Automates data extraction from OF-286 (Emergency Equipment Use) invoice forms", "outputs": "Outputs an excel document", "stage": "Pilot", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Public Comments Analysis", "bureau": "Office of the Chief Information Officer", "problem": "Public comments analysts are overburdened with processing large volumes of public comments. This delays regulatory review and burdens policy staff.", "outputs": "Outputs are CSV files containing extracted comment information necessary for analysts to address issues and recommendations", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Chatbot for IT Customer Help Desk", "bureau": "Office of the Chief Information Officer", "problem": "The chatbot assists USDA staff with IT issues by answering common questions and initiating a support request if additional help is needed.", "outputs": "The chabot provides customers with self-service knowledge articles. If the issue cannot be resovled by self-service, the chatbot will initiate a support request to USDA's IT service managment system.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Rapid Drafting of ARS Research Summaries", "bureau": "Research, Education and Economics", "problem": "The purpose of this tool is to summarize ongoing research from internal Agricultural Research Service (ARS) documents to allow program staff to efficiently understand ARS research outputs and create accurate and timely summary documents, such as briefing…", "outputs": "Natural language text summarizing relevant content from the document database.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "Axon+ Copilot Studio Chatbot", "bureau": "Research, Education and Economics", "problem": "To cut down the time staff spend searching our intranet for information to administer programs & projects and deliver answers that they would not be able to find otherwise.", "outputs": "The chatbot will provide text outputs with links and citations to documents housed on our intranet (Axon+).", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "USDA", "agency_name": "Department of Agriculture", "name": "GovChat", "bureau": "Research, Education and Economics", "problem": "AI assistant that helps with time consuming tasks such as getting answers to general inquiries using natural language", "outputs": "Text-based responses to a user’s input prompts", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Employee Transactions for HR Decision-Making", "bureau": "HRA: Human Resources Administration", "problem": "AI is intended to automate repeatable tasks, improve efficiency, measure employee performance, support onboarding, improve customer experience, improve user adoption, and improve analytics and reporting to further support decision-making and personalize…", "outputs": "- Built-in user aids: Intelligent search, personalized recommendations, and proactive guidance. - Configuration and maintenance: Automated tasks, predictive analytics, and streamlined processes. AI…", "stage": "Pre-deployment", "high_impact": true, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Automated Claims Processing", "bureau": "NCA: National Cemetery Administration", "problem": "The purpose of the AI is to automate aspects of NCA benefits and application processing to decrease the case processing time and increase the customer service delivered to Veterans and their next of kin. Currently, it is applicable to the Pre-Need and…", "outputs": "The Hyperscience platform (optical character recognition (OCR), natural language processing (NLP), and machine learning (ML)) extracts document data, uses application programming interfaces (APIs)…", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "intake", "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "SAC AI tools to automate application processing and data reporting", "bureau": "OALC: Office of Acquisitions Logistics, and Construction", "problem": "Longer response times and missed prioritization: Without automated NLP-driven sorting and predictive routing, incoming requests would sit in general queues or rely on slow manual triage, causing delays in getting issues to the right experts. Urgent or…", "outputs": "Contracting Audits & Reviews: Automating document analysis and classification in acquisition audit processes. Support Ticketing System: Classifying incoming customer requests by content, intent,…", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "review", "correspondence", "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Federal EHR End User Peer Support Chatbot", "bureau": "OEHRM: Office of Electronic Health Record Modernization", "problem": "Federal EHR end user support meets peak in the first three months after roll-out. The current model of super user support is not sustainable when EHRM-IO restarts deployment with an accelerated schedule (i.e., 13 sites in 2026). This tool will alleviate the…", "outputs": "Responses to the user questions related to the use of the Federal EHR.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Lexis+", "bureau": "OGC: Office of General Counsel", "problem": "Improve the quality and efficiency of legal research, legal document review, and legal document preparation. The Lexis+ Document Analysis tool will improve efficiency and accuracy of legal services by giving OGC legal professionals the ability to have…", "outputs": "Legal memoranda, legal pleadings (e.g. motions and briefs), and legal reviews.", "stage": "Pre-deployment", "high_impact": true, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VA VoiceBot for Call Center Modernization", "bureau": "OIT: Office of Information & Technology", "problem": "The VoiceBot can quickly handle common inquiries, offering Veterans immediate answers and reducing the frustration of long wait times. This allows Contact Centers to focus resources on more complex issues, ultimately improving response times for urgent…", "outputs": "Depends on call center. Output can include FAQ and authenticated information via APIs.", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Claims Processing Automation Quality Auditor POC", "bureau": "OIT: Office of Information & Technology", "problem": "In an automation-driven claims process, this tool will act as an entry point for quality reviews: Perform claims quality audits to identify and request NQA reviews or VCE corrections based on automated claims processing anomalies/errors. Function of the…", "outputs": "Within that automation workflow, the claim will be offramped (based on the criteria of the claim and resulting automated decision) to a human to be worked.", "stage": "Pre-deployment", "high_impact": true, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Disability Benefits Document Classifier", "bureau": "OIT: Office of Information & Technology", "problem": "As a Veteran submitting documents to support a claim, I want the VA to automatically classify my uploads so that I can avoid confusing dropdowns and feel confident my evidence will be reviewed and processed correctly. The burden currently rests with Veterans…", "outputs": "For each document uploaded, our system returns the corresponding VA.gov/VBMS-defined document type.", "stage": "Pre-deployment", "high_impact": true, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "ESD-Predictive Intelligence", "bureau": "OIT: Office of Information & Technology", "problem": "Predictive Intelligence, part of the ServiceNow platform, uses artificial intelligence and machine learning to improve the work experience. You can create and train models on the platform and integrate with other ServiceNow products and applications.…", "outputs": "Dashboard highlighting major incidents as they develop where OIT professionals from Major Incident Management and other OIT stakeholders can review the data for more immediate awareness and informed…", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "review" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Enterprise Precision Scanning and Indexing (EPSI) NEXT", "bureau": "OIT: Office of Information & Technology", "problem": "The integration of AI into EPSI aims to solve several key problems: reducing the time-consuming and error-prone manual processing and summarization of PDF medical records, and addressing inefficiencies in the current workflow for transferring and integrating…", "outputs": "The AI system integrated into EPSI produces several critical outputs that enhance the processing and management of medical records: Automated Document Handling: The AI system efficiently receives…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "intake", "summary" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Automated Incident Analysis", "bureau": "OIT: Office of Information & Technology", "problem": "The Enterprise Command Operations (ECO) Major Incident Management (MIM) team will develop an AI Agent to assist in Major Incident bridge calls. The agent will gather insights from the Major Incident bridge transcription, ServiceNow Incidents, and…", "outputs": "Documented insights identifying the probable cause of a Major Incident.", "stage": "Pre-deployment", "high_impact": false, "technique": "Agentic AI", "categories": [ "translation" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "OpenAI Embedding Generation for Future Vector Search of Banking Data", "bureau": "OIT: Office of Information & Technology", "problem": "The current design of a long term document for a banking partner consists of cloud blob storage which are registered into a database with metadata. A future phase will require full-text document search. We can OCR this information now, and implementing…", "outputs": "An API will be exposed to a secure VA-internal website where appropriate personelle will use the UI, which will make an API call which returns a list of semantic search matches.", "stage": "Pre-deployment", "high_impact": false, "technique": "Computer Vision", "categories": [ "intake" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Ask VA Inquiry Automated Category Classification System", "bureau": "OIT: Office of Information & Technology", "problem": "Primary Problem - Manual Categorization Burden: Veterans and their families currently manually navigate complex category, topic/subtopic selection fields when submitting inquiries through AskVA. This creates barriers to accessing VA services and can result…", "outputs": "The AI system produces a ranked list of the top 3 category predictions with their corresponding confidence scores for each processed AskVA inquiry. Rather than forcing a single classification…", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Power Platform Solutioning Chatbot", "bureau": "OIT: Office of Information & Technology", "problem": "The intention of the tool is to speed up the time taken between users identifying that help is needed for a solution and resolving what would be required to implement the solution as a citizen developer on the Microsoft Power Platform.", "outputs": "High level description of the technologies that would be required to implement the provided solution as well as any licensing needs.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Ansible Lightspeed", "bureau": "OIT: Office of Information & Technology", "problem": "Lack of adoption of automation, or expertise in coding knowledge to automate a remediation/code change. Ansible LightSpeed helps create automations faster, making teams more efficient and productive in a shorter amount of time than if they needed to write…", "outputs": "Automation playbooks. Ansible LightSpeed answers questions inside the Ansible Automation Platform application via a chatbot dialogue box in the application. Ansible LightSpeed can also be a plugin…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Pyramid Analytics Generative AI Options", "bureau": "OIT: Office of Information & Technology", "problem": "Pyramid Analytics is a scalable business intelligence application/platform that uses VA data sources for building and maintaining data reporting content. The application/platform has several tools, including dashboard and report creation tools and has…", "outputs": "AI collects and organizes information, then a person acts on it. The AI tools within Pyramid Analytics produce outputs such as interactive chatbot responses, text and audio prompts, generated…", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "BillieGPT POC", "bureau": "OIT: Office of Information & Technology", "problem": "It will exist to assist School employees (School certifying officials) in general use, policy, and procedures for handling Veterans cases from the school's perspective. Gives info directly to school officials rather than having them call in to the VA call…", "outputs": "Generative AI response to the user question", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "TERA Memorandum Automation", "bureau": "OIT: Office of Information & Technology", "problem": "The Toxic Exposure Risk Activity (TERA) Memo automation is intended to reduce the time that users spend manually researching and submitting answers for the TERA memo which is a required form.", "outputs": "The outputs of the TERA memo automation are pre-populated answers to questions on the TERA form, using data sourced from Veteran records and documents.", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VA.gov Chatbot: Summative Content", "bureau": "OIT: Office of Information & Technology", "problem": "AI can provide around-the-clock support without long wait times, allowing our human call center agents to help individuals with more complex concerns. The virtual agent will allow veterans to self-serve, ask questions at their own pace, complete tasks, and…", "outputs": "Benefits information based on VA.gov content.", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Concept Clustering", "bureau": "OIT: Office of Information & Technology", "problem": "Electronic discovery (e-discovery) refers to discovery in legal proceedings such as litigation, government investigations, or Freedom of Information Act (FOIA) requests, where the information sought is in electronic format. As data volumes and the number of…", "outputs": "Identified Cluster groups", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VA GPT", "bureau": "OIT: Office of Information & Technology", "problem": "VA GPT is intended to improve VA employee efficiency, ultimately improving their service to Veterans. Employees are using to assist with basic administrative tasks (drafting emails, summarizing documents, summarizing meeting notes, etc.)", "outputs": "Outputs include generative AI responses to users' prompts.", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "eDiscovery (VASI 2460): Predictive Coding", "bureau": "OIT: Office of Information & Technology", "problem": "Document review teams train an AI model to identify positive or negative content within a universe of documents.", "outputs": "Predicted ranking for remaining documents", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "review" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "CHAMPVA Automation", "bureau": "OIT: Office of Information & Technology", "problem": "The AI solution is designed to eliminate the manual data entry burden associated with processing CHAMPVA applications. Each application previously required up to 61 administrative steps, contributing to a backlog of over 70,000 unprocessed applications and…", "outputs": "The AI system outputs structured data extracted from VA Form 10-10d PDF applications. This data is formatted into text files suitable for ingestion into the CP&E system. The outputs include:…", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "intake", "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Potential Fraud or Waste", "bureau": "OM: Office of Management", "problem": "Quickly develop predictive analytics based on historical values captured and assessed within the Data Analytics Service (DAS) Dashboard. The dashboards allow various purchase card managers to monitor purchase card spending of employees. The dashboards help…", "outputs": "Predictive analytics based on historical values captured and assessed within the Data Analytics Service (DAS) Dashboard.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VA FSC AI ChatHelper", "bureau": "OM: Office of Management", "problem": "Customer Service Representatives (CSR’s) can ask their VA Financial Services Center (FSC) AI ChatHelper questions and receive policy and standards-based answers (e.g., Top Resolution Scripts, Historical Case Notes or Resolutions, Reference Links, Text, or…", "outputs": "Knowledge Base content, help desk resolutions, recommended solutions to customer inquiries", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "E2 HelpBot", "bureau": "Office of the Deputy Secretary of VA", "problem": "EEO Users browse through documents like the User Guides or knowledge articles to understand E2 functionality. Additionally, many Help Desk Tickets are entered for support on E2 functions as well. We plan to develop an internal HelpBot using an LLM model (GPT…", "outputs": "Chatbot The Help Bot AI use case will be expanded by developing two distinct products. The first product is a Portal Help Bot, intended for deployment on the EEO portal. This external bot will…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Compliance Made Easy", "bureau": "Office of the Executive Secretary", "problem": "AI is being added to a new version of Compliance Made Easy (CME) in development to assist with grammar and tone checks along with tracking Style Guide updates. This is intended to improve compliance with VA Style Guide and reduce staffing time for…", "outputs": "Once fully developed, the AI version of CME will provide style guide, grammar, and tone checks returned to the user on the text the user submitted to CME.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Smart Pension Automation", "bureau": "VBA: Veterans Benefits Administration", "problem": "Reduce the overall average of days to complete a pension related benefit claim. Currently, there is a batch claim processing system in place with Pension Automation that leverages Camunda and is currently only able to process 300 claims/night due to…", "outputs": "The AI solution will output reporting data on which claims were awarded or off ramped on a daily basis. The solution will also provide auditable information on why a decision was made for a human to…", "stage": "Pre-deployment", "high_impact": true, "technique": "Agentic AI", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Predictive Claims Processing Capability POC", "bureau": "VBA: Veterans Benefits Administration", "problem": "Faster claims decisions for Veterans by predicting/performing claims adjudication without the need for a rules engine", "outputs": "Proposed decision on Education Benefits", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "AICES", "bureau": "VBA: Veterans Benefits Administration", "problem": "1. Reduce the backlog and processing times, minimize unnecessary in-person exams, and address workflow inefficiencies. 2. Reduce the burden of travel on veterans and expedite the time required for claims processing. During the period of performance, this…", "outputs": "The system indexes structured, semi-structured, and unstructured Veteran health and service record data, including diagnosis, severity, and service connection evidence from eFolders, metadata, and…", "stage": "Pre-deployment", "high_impact": true, "technique": "Agentic AI", "categories": [ "intake", "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Smart Claim Check", "bureau": "VBA: Veterans Benefits Administration", "problem": "Smart Claim Check (SCC) reduces avoidable claims processing errors by addressing the problem of manually reviewing extensive documentation by Veterans Service Representatives (VSRs) to identify and resolve issues. Claims processing errors that can be avoided…", "outputs": "The output of the AI system is timely guidance with relevant M21-1 linkage, that will appear in the User Interface (UI) automatically for the claims processors to review and act upon. SCC is…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Loan Guaranty Lender’s Handbook Chatbot", "bureau": "VBA: Veterans Benefits Administration", "problem": "Loan Guaranty Service (LGY) has many policy and procedural documents that are difficult to know and retrieve information for. This AI tool is designed to make the knowledge retrieval process more efficient and improve consistency.", "outputs": "Curated information (answers) to user questions based on LGY policy and procedures.", "stage": "Pre-deployment", "high_impact": false, "technique": "Reinforcement Learning", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Education Call Center (ECC) Next Gen POC", "bureau": "VBA: Veterans Benefits Administration", "problem": "To give easier access to information like approved, current procedures or helpful tips to call center employees that engage directly with Veterans. This is a high turnover position within VA so this would lighten the training barrier and supply our Veterans…", "outputs": "• Function of the model: Generative AI Model that can provide real time transcription of calls, capture questions asked by caller, and provide recommended answers with source citation to ECC…", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence", "translation" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "AI Personal Assistance POC", "bureau": "VBA: Veterans Benefits Administration", "problem": "Help VBA Education Data Analysts create queries and interpret data which can be used for decision making.", "outputs": "Answers to questions. The tool would generate a mock SQL query and other useful outputs that would help refine the data inquiry.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Generative Annotations", "bureau": "VBA: Veterans Benefits Administration", "problem": "Veteran Service Representatives (VSRs) are required to annotate documents relevant to a Veteran claim so that subsequent reviewers are able to quickly reference information to make claim decisions. Currently in order to annotate documents, VSRs have to read…", "outputs": "AI Summarized text", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "summary" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "ASKA: Automated Support and Knowledge Assistance", "bureau": "VBA: Veterans Benefits Administration", "problem": "ASKA is an internal support tool for VBA Public Contact staff. It connects approved policy and procedural content from SharePoint to a searchable question and answer bot. Staff can ask common questions about public contact, FOIA, congressional inquiries, and…", "outputs": "We are currently looking into building or utilizing what is available to create this. We are looking at a space where all info is extracted and easy to find", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "correspondence", "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VBA Mail Management Services (MMS) - Modification", "bureau": "VBA: Veterans Benefits Administration", "problem": "We are shifting data capture from back-office processing to the point of first contact. The system validates VA forms in front of the user interactively as it is being submitted, allowing the submitter to verify and correct errors immediately (reduces…", "outputs": "Upgrade to QuickSubmit to include a forms validation engine.", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Call Center Knowledge Navigator", "bureau": "VBA: Veterans Benefits Administration", "problem": "1. Inaccurate transcription of phone calls by the current system. 2. Need for enhanced time-to-effectiveness for new employees of the Education Call Center. 3. Improvements to quality review of transcribed conversations.", "outputs": "- Version 1 outputs a string in response to a user query with cited reference materials - Version 2 will hyperlink these materials for user reference - Future versions will include transcription…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence", "translation" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Master Claims Assistance Tool (M-CAT)", "bureau": "VBA: Veterans Benefits Administration", "problem": "M-CAT is designed to automatically classify, extract, and validate critical information from diverse regulatory and/or policy references. This includes the automation of complex data extraction tasks, which are traditionally time-consuming and prone to human…", "outputs": "M-CAT provides quick access to regulatory and/or policy guidance and summarizes information within the prescribed RAG model. These retrieval results are maintained for each user and aggregated to…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "intake" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Automated Decision Support", "bureau": "VBA: Veterans Benefits Administration", "problem": "Identify medical evidence for claims processing. Automated Decision Support (ADS) is the utilization of technology to maximize operational efficiency by reducing administrative tasks to produce desired outcomes. These tools will improve the efficiency of the…", "outputs": "Depending on claim criteria, ADS may produce the following five documents: - Automated Review Summary Document (ARSD) - Summarizes relevant medical and service information from Veteran documents…", "stage": "Deployed", "high_impact": true, "technique": "Classical ML", "categories": [ "summary", "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Mail Automation Services", "bureau": "VBA: Veterans Benefits Administration", "problem": "Mail Automation Services (MAS) is an if-this-then that (ITTT) logic-based service which is utilized to process inbound mail across the VBA enterprise, including the receipt of claims materials from Veterans, Veteran Service Organizations, and other…", "outputs": "The most common outcomes are VBA End-Product establishment within the Veterans Benefits Management System (VBMS) and correct business line orientation for ingested submissions. However, MAS also…", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "intake", "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Pension Optimization Initiative (POI)", "bureau": "VBA: Veterans Benefits Administration", "problem": "VBA’s POI is a transformative effort that seeks a Managed Services Provider (MSP) to convert an existing manual business process into automated processing of Pension, Dependency Indemnity Compensation (DIC), and Burial claims. This effort is focused on…", "outputs": "1) updated Veteran/Beneficiary files, 2) completed Pension, DIC, and Burial-related claim actions to include stop and start of awards (delivery of benefits), 3) Notification Letters to Claimants.", "stage": "Deployed", "high_impact": true, "technique": "NLP", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Privacy Act Automation", "bureau": "VBA: Veterans Benefits Administration", "problem": "VBA receives over 160,000 Privacy Act requests each year requiring personnel to retrieve, review, and release these records to the Veteran. Prior to the D.AI solution, this process was highly manual, requiring significant labor resources that led to an…", "outputs": "The D.AI system retrieves documents based on the request type, as defined by VA, and then completes a preliminary processing review based on defined business rules for redaction. The request is then…", "stage": "Deployed", "high_impact": false, "technique": "Generative AI", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Auto Doc ID", "bureau": "VBA: Veterans Benefits Administration", "problem": "The purpose of the tool is to convert inbound images into a standard format that is required for downstream processing for auto document classification and OCR/Data Extraction. After all document processing is completed and we are preparing the final output…", "outputs": "Suggested doc ID. It provides document type suggestions in the ImageSort application to guide operators. D3P does not perform content-based analysis but makes technical image-quality determinations,…", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake", "review" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "electronic Virtual Assistant (e-VA)", "bureau": "VBA: Veterans Benefits Administration", "problem": "", "outputs": "", "stage": "Retired", "high_impact": true, "technique": "", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Summarize Progress Notes", "bureau": "VHA: Veterans Health Administration", "problem": "Reduce the amount of time required for a healthcare provider to review medical records during an active case within the Clinical Contact Center.", "outputs": "A summary of the patient medical record with the most important elements presented first.", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "summary", "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Natural Language Online Workflow (NOW)", "bureau": "VHA: Veterans Health Administration", "problem": "- Information Gaps and Misinformation: Veterans and staff often have trouble finding accurate, consistent answers about vasectomy procedures, eligibility, risks, and next steps. Policies and contacts change and not everyone knows where to look. - Inefficient…", "outputs": "- Personalized Responses: the chatbot gives tailored answers to user questions about vasectomy procedures, eligibility, risks, recovery, and next steps—based on the latest VA policies and user…", "stage": "Pre-deployment", "high_impact": true, "technique": "Agentic AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "JLV Booster", "bureau": "VHA: Veterans Health Administration", "problem": "It is difficult for physicians to figure out the details of care that occurred outside the VA. Even when the data exists and can be viewed in Joint Legacy Viewer (JLV), the process of viewing records is time consuming. AI can help summarize the existing…", "outputs": "Summary of external medical record", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VAHC CRM- Veteran Self-Service", "bureau": "VHA: Veterans Health Administration", "problem": "The Clinical Contact Center (CCC) receives 45 million calls per year. At this time the CCC has a queue abandonment rate of 9.4% which is significantly higher than the national average for contact centers. AI reduce the number of abandoned calls.", "outputs": "Refilled prescriptions; answers to commonly asked questions; decisions to escalate a contact to a triage nurse.", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "EEV/OHI Document Types", "bureau": "VHA: Veterans Health Administration", "problem": "Increase the speed and accuracy of identifying document types and routing thereof.", "outputs": "Correct document types and routing of those documents for further processing.", "stage": "Pre-deployment", "high_impact": true, "technique": "Agentic AI", "categories": [ "forms" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Electronic Mammogram Reporting (eMAMR)", "bureau": "VHA: Veterans Health Administration", "problem": "Clinical data extraction from diverse and unstructured sources remains a bottleneck for healthcare providers and agencies. For example, providers may need to re-enter the same patient information across different consults, which is inefficient and takes away…", "outputs": "- Extracted JSON Files: Clinical data extracted from incoming fax/PDF images using OpenAI GPT-4o. - TIU/SMART Notes: Automated note creation in CPRS/VistA with mirrored SMART notes and health…", "stage": "Pre-deployment", "high_impact": true, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Sentiment analysis for app feedback", "bureau": "VHA: Veterans Health Administration", "problem": "Analyzing and summarizing feedback provided by patients and providers for VA Mobile Apps. Identifies trends and makes inferences of \"App Feedback\" data to provide a scalable solution.", "outputs": "Major topic classifications of given output.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "summary" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Clinical Pathways for Cancer Patients", "bureau": "VHA: Veterans Health Administration", "problem": "The VA National Oncology Program (NOP) has invested in creating clinical pathways that guide providers to provide high quality evidence-based standard of care treatments to Veterans with cancer. However, it is currently unclear how many or how often…", "outputs": "Whether a patient is on a clinical pathway and where on the clinical pathway they are", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Pathology and Laboratory Medicine SharePoint Chatbot - Quincy", "bureau": "VHA: Veterans Health Administration", "problem": "The current SharePoint page has a tremendous amount of information. Using the Chatbot helps the field search for information without having to contact the office directly.", "outputs": "If AI is approved, it will allow the Chatbot to look outside the PLM SharePoint page for information on such sites as the Center for Medicare and Medicaid site.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "BESSistant", "bureau": "VHA: Veterans Health Administration", "problem": "This use case will cut down the amount of time it takes an individual to search manuals for preventative maintenance and troubleshooting instructions - the AI chatbot returns answers to queries from uploaded maintenance documentation.", "outputs": "The application will use retrieval augmented generation (RAG) to query a set of technical service manuals", "stage": "Pre-deployment", "high_impact": false, "technique": "Computer Vision", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Internal QMS Processes Revamp", "bureau": "VHA: Veterans Health Administration", "problem": "1. Planning & Restructuring Assistant: Utilize GPT-4x (or more advanced available models with reasoning capabilities) to analyze current QMS structures, identify optimization opportunities, and generate actionable restructuring plans. 2. Document…", "outputs": "Internal Documentations such as SOP (Standard Operating Procedures)", "stage": "Pre-deployment", "high_impact": false, "technique": "Generative AI", "categories": [ "review" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Using LLMs to assist with data extraction", "bureau": "VHA: Veterans Health Administration", "problem": "We are investigating the ability of Large Language Models (LLMs) to automate the extraction of discrete data elements from clinical notes. The National Radiation Oncology Program (NROP) office is using Quality Measures to evaluate the quality of care to…", "outputs": "The primary output of this system will be discrete data found in free text clinical notes.", "stage": "Pre-deployment", "high_impact": false, "technique": "NLP", "categories": [ "intake" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VA Health Connect (VAHC) Customer Relationship Management (CRM) - contact quality analysis", "bureau": "VHA: Veterans Health Administration", "problem": "There are very few tools available to evaluate the content of a Clinical Contact Center (CCC) Contact to provide insight into the purpose, tenor of the call, satisfaction of the Veteran, and identify Medical Support Assistant (MSA) training opportunities. AI…", "outputs": "Contact quality metrics, data for additional analysis to identify trends, training opportunities, and self-service options.", "stage": "Pre-deployment", "high_impact": false, "technique": "Classical ML", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "CITC Automated Fax Relabeling", "bureau": "VHA: Veterans Health Administration", "problem": "Speed intake of outside medical records. Currently the VA system as a whole gets millions of pages of faxes a day. The subset of these related to care in the community (CITC) contain results of care that the VA itself has paid for in non-VA facilities. As…", "outputs": "Renamed faxes (with patient identifiers and document classification). The software is run by care in the community (CITC) staff (on their local GFE). These staff 'point' the fax processer program to…", "stage": "Pilot", "high_impact": true, "technique": "NLP", "categories": [ "review" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Nurse Proficiency Writer (NPW)", "bureau": "VHA: Veterans Health Administration", "problem": "Many nurses lack knowledge of the standards and the requirements needed to meet their performance expectations. The GPT assists the nurse by evaluating their input against the standards. The dialogue between the GPT and the nurse results in improvements in…", "outputs": "GPT text that provides narrative for ePerformance integration, textual contribution for promotion (including plans), and skills recommendation to improve performance.", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Ask Sage – Generative AI Platform for VA Administrative Efficiency", "bureau": "VHA: Veterans Health Administration", "problem": "VA staff often spend significant time searching for information, drafting communications, and compiling documentation. These tasks can be repetitive, time-consuming, and prone to inconsistencies when performed manually. Ask Sage addresses this by providing a…", "outputs": "Draft correspondence, reports, and summaries for staff review. Condensed explanations of policies, regulations, and procedures. Suggested formats, outlines, and checklists for administrative…", "stage": "Pilot", "high_impact": false, "technique": "Generative AI", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "AI Transcription and Summary in Veteran Narrative Medicine Program", "bureau": "VHA: Veterans Health Administration", "problem": "1) The MyLife MyStory program tends to be time intensive, often requiring about four hours of the interviewers' time per patient, which limits the number of Veterans who can receive the program. 2) The narratives that are produced in MyLife MyStory are…", "outputs": "1) Transcriptions of patient interviews, generated using natural language processing. 2) An AI-generated summary of each patient's narrative.", "stage": "Pilot", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Nediser Reports QA", "bureau": "VHA: Veterans Health Administration", "problem": "Nediser is a continuously trained artificial intelligence \"radiology resident\" that assists radiologists in confirming features in radiology images, including x-ray. It helps with workflow efficiency and quality. The program fills in the comparison date,…", "outputs": "The AI system always requires human in the loop and sends outputs into the radiology report draft. No output is send to the EHRM. The output draft includes comparison dates, measurements, and…", "stage": "Deployed", "high_impact": true, "technique": "Computer Vision", "categories": [ "translation" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Continuous Glucose Monitoring Summary", "bureau": "VHA: Veterans Health Administration", "problem": "Reduce work hours associated with manually scouring Ambulatory Glucose Profile metrics and reduce transcription errors while copying and pasta data between medical tools and platforms.", "outputs": "Summarized Ambulatory Glucose Profile metrics over a 14-day period in a Power BI dashboard that is accessible from Virtual Care Manager, Clinical Data Services, and Electronic Health Record.", "stage": "Deployed", "high_impact": true, "technique": "Generative AI", "categories": [ "translation" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Axon Body Camera and DMS - OSSO", "bureau": "VHA: Veterans Health Administration", "problem": "AI is used to aid in Redaction of recorded Video footage and recorded Audio footage. Additionally, AI is used via transcribing recorded videos.", "outputs": "To have better outputs in searching the systems data repository for case files, evidence locker, and DMS; to have a better result when doing the redaction of evidence, files, videos and photos; and…", "stage": "Deployed", "high_impact": false, "technique": "Computer Vision", "categories": [ "forms", "translation" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VA Supply Chain Knowledge Management Dashboard", "bureau": "VHA: Veterans Health Administration", "problem": "Identify indictments of the VA’s enterprise supply chain in OIG, GAO and other public reports", "outputs": "Library of VA supply chain indictments in public reports; summary and entirety of reports are available for review", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "summary" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "Dragon Medical One", "bureau": "VHA: Veterans Health Administration", "problem": "Solution to decrease the provider administrative burdens of typing medical notes.", "outputs": "DMO is an AI-powered speech recognition solution that utilizes advanced AI algorithms to deliver accurate, real-time medical transcription and drive voice-activated documentation workflows.", "stage": "Deployed", "high_impact": false, "technique": "NLP", "categories": [ "translation" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "PII in Foreign Medical Program (FMP) Faxes", "bureau": "VHA: Veterans Health Administration", "problem": "Improve processing speed in identifying and processing multiple language claim forms and improve the speed and accuracy of such claims.", "outputs": "1. Identify claims submitted in a foreign language. 2. Identify which foreign language is being used. 3. Identify the specific claims pages in a foreign language within the document.", "stage": "Deployed", "high_impact": false, "technique": "Classical ML", "categories": [ "intake" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "VCA & PPMS Chatbot", "bureau": "VHA: Veterans Health Administration", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] }, { "agency": "VA", "agency_name": "Department of Veterans Affairs", "name": "AI-Enhanced Call Center", "bureau": "VHA: Veterans Health Administration", "problem": "", "outputs": "", "stage": "Retired", "high_impact": false, "technique": "", "categories": [ "correspondence" ] } ], "fetched_at": "2026-10-22T00:47:28Z" }