GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Large Language Model analysis for the Tips, Complaints and Referrals (TCR) system
Agency: Securities and Exchange Commission - Division of Enforcement (ENF)
Problem addressed: The SEC receives high volumes of Tips, Complaints, and Referrals (TCRs) and has limited staff to triage these TCRs effectively. Some TCRs contain large narratives for allegations; AI tools can help summarize submitted information and intelligence for more efficient review by triage staff.
Purpose and benefits: Large Language Model analysis will be leveraged to more efficiently review submissions to the SEC's Tips, Complaints and Referrals system.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Using AI Large Language Model chat for general tasks/questions
Agency: Securities and Exchange Commission - Division of Enforcement (ENF)
Problem addressed: Using an AI Large Language Model chat for general tasks and answer questions
Purpose and benefits: This AI service will provide a internal Large Language Model for general usage.
Topic area: Other
Development stage: Deployed
First used: 2024-04-01
US federal AI use case: Agentic Knowledge Base of Amazon Web Services Cloud Environment for the Securities and Exchange Commission (ACES)
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Problem addressed: Retrieving and Summarizing support FAQ information
Purpose and benefits: The AI system will look for relevant historical FAQs and Compliance Best Practices to present to Cloud Engineers using the ACES platform
Topic area: IT
Development stage: Pilot
US federal AI use case: Extracting data from FOIA request forms
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Problem addressed: 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.
Purpose and benefits: To capture and load data from a FOIA form into a SharePoint list for tracking through the eDiscovery Lifecycle.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-03-01
US federal AI use case: Knowledge base of federal securities law and chatbot
Agency: Securities and Exchange Commission - Division of Investment Management (IM)
Problem addressed: Using AI to carry out basic research on federal securities laws.
Purpose and benefits: Provide Staff with a simple internal chat bot to help find relevant documents referencing IM-specific curated content.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Knowledge base of crypto comments and chatbot
Agency: Securities and Exchange Commission - Division of Investment Management (IM)
Problem addressed: Using AI to carry out basic research on opinions and submitted comments related to crypto assets.
Purpose and benefits: Provide Staff with a simple internal chat bot to identify relevant documents referencing content curated by IM.  To be used as a supplement to research.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Create description of documents for records management in sharepoint
Agency: Securities and Exchange Commission - Office of the Chief Operating Officer (OCOO)
Problem addressed: Creating descriptions and other record keeping metadata on documents.
Purpose and benefits: Reduce the amount of time manually writing descriptions of documents.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Westlaw Co-Counsel
Agency: Securities and Exchange Commission - Division of Enforcement (ENF), Division of Examinations (EXAMS), Office of the Chief Data Officer (OCDO), Office of the General Counsel (OGC)
Problem addressed: The AI output will facilitate legal research, document analysis, and document production.
Purpose and benefits: More efficient legal research and analysis, document review, and document production.
Topic area: Other
Development stage: Pilot
First used: 2025-10-01
US federal AI use case: Lexis Protege
Agency: Securities and Exchange Commission - Division of Enforcement (ENF), Division of Examinations (EXAMS), Office of the Chief Data Officer (OCDO), Office of the General Counsel (OGC)
Problem addressed: The AI output will facilitate legal research, document analysis, and document production.
Purpose and benefits: More efficient legal research and analysis, document review, and document production.
Topic area: Other
Development stage: Pilot
First used: 2025-10-01
US federal AI use case: Semantic Layer Natural Language Query
Agency: Securities and Exchange Commission - Division of Economic and Risk Assessment (DERA), Office of the Chief Data Officer (OCDO)
Problem addressed: This use case prepares data for use by AI and then enables users to query data with AI without having to write code.
Purpose and benefits: Make agency enterprise data more accessible to business users for mission purposes.  The analytical power of insights drawn from enterprise data will be enhanced by AI.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Using AI to answer questions about a corpus of uploaded files
Agency: Securities and Exchange Commission - Division of Enforcement (ENF), Division of Economic and Risk Assessment (DERA)
Problem addressed: This AI service will provide a internal Large Language Model for general usage for chatting and answering questions about user uploaded files and internal knowledge bases.
Purpose and benefits: Cost and time savings by reducing Staff's time in reviewing a large number of documents.
Topic area: Other
Development stage: Deployed
First used: 2025-05-01
US federal AI use case: AI Chart Generation
Agency: Securities and Exchange Commission - Division of Economic and Risk Assessment (DERA), Office of the Chief Data Officer (OCDO)
Problem addressed: Our solution uses AI to generate chart code to automatically render interactive data visualizations for various chart types.
Purpose and benefits: This will give Staff new data visualization capabilities as well as save Staff time by not having to research how to create various chart types and features.  This could also enhance the quality of SEC published reports and data visualization content on sec.gov.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Creating AI generated images
Agency: Securities and Exchange Commission - Division of Enforcement (ENF), Division of Economic and Risk Assessment (DERA)
Problem addressed: Facilitate the use of models for AI generated image creation
Purpose and benefits: Cost savings, efficient use of time for creating stock images for presentations, keeping up with state-of-the-art technologies.
Topic area: Other
Development stage: Deployed
First used: 2025-03-01
US federal AI use case: 8-K Filing Solution
Agency: Securities and Exchange Commission - Division of Corporate Finance (CF)
Problem addressed: Business users now can quickly go through the details to make a informed decisions using AWS image recognition AI service
Purpose and benefits: Searches and extracts information from certain 8-K filings (HTML files and images).
Topic area: Other
Development stage: Deployed
First used: 2025-03-01
US federal AI use case: Disclosure Review Chatbot
Agency: Securities and Exchange Commission - Division of Corporation Finance (CF)
Problem addressed: Disclosure Review Automation (DREAM) Chatbot helps division staff search within review guides and generating detailed responses to disclosure review questions
Purpose and benefits: The DREAM chatbot is designed to support SEC staff in the Corporation Finance division by delivering fast, accurate, and context-aware responses to disclosure-related questions.  This chatbot delivers significant time savings for staff in the disclosure review program.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Note-Taking Assistant
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: The application assists staff with review and analysis of the examiner's notes taken during the examination process.
Purpose and benefits: This application will provide consistent and thorough review of examiner's notes to improve risk analysis during examinations.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Public Website Search
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: This application summarizes a public website and allows staff to ask natural language questions about the contents of the website.
Purpose and benefits: This application will streamline analysis of public websites which will improve efficiency and risk analysis during examinations.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Chatbot with SEC Documents
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS)
Problem addressed: This application includes a knowledge base of internal SEC guidelines and procedures.  It will help answer staff questions on those guidelines/procedures.
Purpose and benefits: This application will streamline responding to questions regarding internal SEC guidance.  It will improve efficiency and consistency of SEC work through near immediate response time to questions.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Enhancing Analysis of SEC Web Engagement Trends Using AI
Agency: Securities and Exchange Commission - Office of Information Technology (OIT), Office of the Chief Data Officer (OCDO), Office of the Advocate for Small Business Capital Formation (OASB)
Problem addressed: Manual analysis of Google Analytics data is time-consuming and limits the ability to identify trends across multiple SEC.gov pages.
Purpose and benefits: Faster, more accurate insights into how the public engages with SEC content, enabling more effective outreach and communication strategies.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Identifying Citations of SEC Publications to Measure Market Impact
Agency: Securities and Exchange Commission - Office of the Chief Data Officer (OCDO), Office of Information Technology (OIT), Office of the Advocate for Small Business Capital Formation (OASB)
Problem addressed: Lack of visibility into how SEC publications are cited in external research and industry materials.
Purpose and benefits: Improved understanding of the reach and impact of SEC publications, supporting transparency and accountability.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Analyzing Registered Offerings Data to Understand Capital Formation Trends
Agency: Securities and Exchange Commission - Office of Information Technology (OIT), Office of the Chief Data Officer (OCDO), Office of the Advocate for Small Business Capital Formation (OASB)
Problem addressed: Manual review and analysis of data filed with the SEC about registered offerings is time-consuming.
Purpose and benefits: Faster, more accurate insights into registrants are raising capital from investors through registered offerings.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Analyzing EDGAR Search Trends to Understand Public Interest in Filings
Agency: Securities and Exchange Commission - Office of the Chief Data Officer (OCDO), Office of Information Technology (OIT), Office of the Advocate for Small Business Capital Formation (OASB)
Problem addressed: Limited insight into how the public interacts with EDGAR and what types of filings are most viewed.
Purpose and benefits: Better understanding of public interest in SEC filings, supporting transparency and user-centered improvements.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Analyzing Form ADV Data to Identify Trends Among Investment Advisers
Agency: Securities and Exchange Commission - Office of the Chief Data Officer (OCDO), Office of Information Technology (OIT), Office of the Advocate for Small Business Capital Formation (OASB)
Problem addressed: Manual analysis of Form ADV data is inefficient and limits trend detection.
Purpose and benefits: Improved oversight and understanding of adviser trends, affiliations, and private fund reporting.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Rule and Comment Organization and Summarization
Agency: Securities and Exchange Commission - 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 addressed: Downloading, navigating, searching, and understanding comments and rule filings can be slow and burdensome using existing tools.
Purpose and benefits: The tool aims to ease these burdens of downloading, navigating, searching, and understanding comments and rule filing. In particular, AI is used to summarize individual comments, rule proposals, and overall subject matters to promote the understanding of vast quantities of unstructured data, i.e. text.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Training Conversation Tool
Agency: Securities and Exchange Commission - Office of Human Resources (OHR)
Problem addressed: Strictly a training tool for practicing conversations on a variety of topic areas.
Purpose and benefits: Improves communication and collaboration skills of the SEC workforce
Topic area: HR
Development stage: Deployed
First used: 2024-12-01
US federal AI use case: AI-enabled information collection and curation
Agency: Securities and Exchange Commission - Office of the Chief Data Officer (OCDO)
Problem addressed: Collecting information from a variety of sources, including documents, databases, and staff interviews, and curating into a knowledge base.
Purpose and benefits: Collecting and organizing information in support of various use cases.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AI-enabled information dissemination
Agency: Securities and Exchange Commission - Office of the Chief Data Officer (OCDO)
Problem addressed: Process, organize, and propagate new information to staff and AI agents when it arrives.
Purpose and benefits: Users and applications throughout the agency will benefit from better information dissemination processes.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Legal Research (Lexis+)
Agency: Securities and Exchange Commission - Office of the Chief Data Officer (OCDO)
Problem addressed: More efficient legal research.
Purpose and benefits: Improved search results and extracted information to assist in legal research activities.
Topic area: Other
Development stage: Deployed
First used: 2020-01-01
US federal AI use case: Legal Research (Westlaw Precision)
Agency: Securities and Exchange Commission - Office of the Chief Data Officer (OCDO)
Problem addressed: More efficient legal research.
Purpose and benefits: Improved search results and extracted information to assist in legal research activities.
Topic area: Other
Development stage: Deployed
First used: 2020-01-01
US federal AI use case: Natural language search on images through FlashPoint
Agency: Securities and Exchange Commission - Division of Examinations (EXAMS), Division of Enforcement (ENF)
Problem addressed: Using natural language to search through images posted to social media.
Purpose and benefits: Help the agency find and identify relevant images when reviewing social media posts.
Topic area: Other
Development stage: Deployed
First used: 2025-04-01
US federal AI use case: Streamlining initial research
Agency: Securities and Exchange Commission - Division of Enforcement (ENF), Division of Examinations (EXAMS)
Problem addressed: Initial research steps in examinations and enforcement matters requires marshalling many different data sets, for which AI can improve efficiency.
Purpose and benefits: Expected benefits may include improving efficiency by automating previously manual steps, improving risk identification, and optimizing use of resources.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Use natural language to query crypto market data and create on demand visualizations with prepopulated thresholds
Agency: Securities and Exchange Commission - Office of the Chief Data Officer (OCDO)
Problem addressed: The AI simplifies the process of creating data visualization for crypto market data.
Purpose and benefits: Simplifying crypto market data pulls and visualizations will help streamline the agency's review of crypto-related products.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Tracing digital asset transactions and identifying entities controlling wallet addresses
Agency: Securities and Exchange Commission - Division of Enforcement (ENF), Division of Examinations (EXAMS)
Problem addressed: The tool helps to identify clusters of related wallet addresses and to predict the sender’s change in transaction data.
Purpose and benefits: Staff has greater clarity of the wallet addresses controlled by certain entities and the movement of their digital assets.
Topic area: Other
Development stage: Deployed
First used: 2025-12-01
US federal AI use case: Improve efficiency of comment letter ingestion
Agency: Securities and Exchange Commission - Office of the Secretary (OS)
Problem addressed: Using AI/ML to improve the timeliness and accuracy of the ingestion of comment letters, such as by automating the identification of comment file numbers, and enhancing the identification of spam, form letters, PII, profanity, and copyrighted material. 2. Increase the quality and amount of structured data (including metadata) related to individual comment letters to directly support downstream uses, including use by GenAI tools
Purpose and benefits: Improve the timeliness and accuracy of the ingestion of comment letters, and increase the quality and amount of structured data (including metadata) related to individual comment letters to directly support downstream uses, including use by GenAI tools
Topic area: Service Delivery
Development stage: Deployed
First used: 2025-04-01
US federal AI use case: Comparing EDGAR system access request forms
Agency: Securities and Exchange Commission - EDGAR Business Office (EBO)
Problem addressed: The AI helps extract information from scanned documents and compares it with information provided on a digital form
Purpose and benefits: This application will streamline the EDGAR system's registration review process
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Mobile Phone Artificial Intelligence Features
Agency: Securities and Exchange Commission - Office of Information Technology (OIT)
Development stage: Retired
US federal AI use case: CF Staff Review Comments
Agency: Securities and Exchange Commission - Division of Corporate Finance (CF)
Problem addressed: The solution will help in classifying comments letters.
Purpose and benefits: Classifies staff comments and improve CF's review process.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Copilot for Appian
Agency: Surface Transportation Board - Surface Transportation Board
Problem addressed: Facilitate analysis of filings and data submitted by parties in proceedings
Purpose and benefits: Faster, more efficient analysis of data
Topic area: Other
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: AI may be integrated in decision or activity pipelines in high-impact categories without meeting the definition of high-impact because the Al's output does not actually "serve as a principal basis for" the relevant type of agency action or decision, as described in OMB Memo M-25-21 definition of "high-impact Al."
US federal AI use case: RelativityOne
Agency: Surface Transportation Board - Surface Transportation Board
Problem addressed: Facilitate analysis of content associated with proceedings
Purpose and benefits: Increasing speed and efficiency in processing of large data volumes, reducing timelines on proceedings.
Topic area: Other
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: AI may be integrated in decision or activity pipelines in high-impact categories without meeting the definition of high-impact because the Al's output does not actually "serve as a principal basis for" the relevant type of agency action or decision, as described in OMB Memo M-25-21 definition of "high-impact Al."
US federal AI use case: Public Chatbot
Agency: Department of the Treasury - Terrorism and Financial Intelligence (TFI)
Problem addressed: Public data is hard to sift through and results in calls to the helpline.
Purpose and benefits: Make public information more easily attainable and reduce demands for human call center
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: Does not affect civil rights, safety, or access to critical services, or non-public information.
US federal AI use case: SBSE - Payments Topics Chatbot
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Provide self service capabilities to taxpayers seeking information about certain tax topics.
Purpose and benefits: This chatbot provides means for the taxpayers to receive information without need to speak with an Agent.  The benefits include reduced taxpayer burden as well as increased internal IRS operational efficiencies.
Topic area: Service Delivery
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2021-12-10
US federal AI use case: Joint Committee on Taxation Review Research Aide
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: 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 case specific user interface (UI) that uses the Certara platform via Application Programming Interface (API) to perform the Retrieval-Augmented Generation (RAG) search and generation.
Purpose and benefits: Operational efficiency and reduced cost by providing the necessary information to agents to quickly fins answer to JCTR questions reducing the time and effort needed to research responses.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Local GenAI Pilot
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Local Large Language Models (LLMs) will help in local no software/hardware (SW/HW) cost. LLMs will position IRS for sustainable AI services and AI tool integrations and mitigate many risks [Several Risks Mitigated Include: vendor lock-in, licenses shortages or over purchasing, SW costs or per Application Programming Interface (API) call costs, contract gaps and access loss with outsourcing, and data privacy policies and interagency agreements restricting data leaving the IRS (Auditing/Compliance Algorithm's Source Code)]
Purpose and benefits: Reducing human workload by leveraging LLMs increasing development speed, automation, and operational efficiency.​​ Ensuring alignment with internal Information Technology (IT) policies and security protocols by operating entirely within IRS infrastructure.​​ Eliminates licensing costs reducing operational expenses.​​ Maintains full control over sensitive data, enhancing data governance and compliance with federal privacy mandates. Reducing maintenance costs and technical debt due to legacy code and technology.​ Aging/departing workforce and lack of expertise with legacy systems increases risk of system failures and operational bottlenecks. Resource optimization, efficiency, and sustainable solutions required to modernize critical IRS operations and services
Topic area: IT
Development stage: Pilot
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-03-25
US federal AI use case: Internal Revenue Manual Research Aid (IRMA)
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: 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 interface (UI) and utilizes a COTS platform via Application Programming Interface (API) to perform the RAG search and generate responses based on the full text of the IRM serving as a knowledge base. The generated responses also provide links back to the referenced sections of the IRM for further review.
Purpose and benefits: Operational efficiency and reduced cost by providing the necessary information to taxpayer-facing customer service agents to quickly answer taxpayers' questions. This enables the agents to increase their capacity and help a larger number of taxpayers by reducing the time and effort needed to research responses. The public, in turn, benefits from a reduction in wait time to receive a response to their inquiries.
Topic area: IT
Development stage: Pilot
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-04-01
US federal AI use case: Answering Employee Questions with Natural Language Processing in IRWorks
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The purpose of the Artificial Intelligence (AI) element of the IRWorks platform is to utilize natural language to respond to common user questions about IRWorks knowledge base (KB) articles and service catalogue items in the IRWorks Employee Center. This AI element is interpretive. At a high level, this is accomplished by using language models to train the agent to respond to user questions. The decision/judgement that this AI element uses is to search existing Knowledge Base (KBs)/service catalogue items, in conjunction with the user's defined access within IRWorks, to automate Tier 1 support by outputting (in natural language) answers and/or associated links that align to the customer's (natural language) query. Yes, humans are involved in reviewing the output - the AI element does not produce actionable items but produces information or links that are provided to the querying user for them to review and/or take manual actions. The population impacted by this decision is all IRWorks users, about 90K+ IRS employees.
Purpose and benefits: This functionality allows better understanding of user questions to direct them to better information allowing for quicker resolution of IT and HR queries and issues.
Topic area: IT
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2023-04-30
US federal AI use case: Form 990N Machine Learning Model
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The goal of this model is to predict non-eligible Form 990N filers as part of a data driven approach to identify non-compliance within the population of exempt organizations (EOs) filling the Form 990N (Issue Control Number (ICN) 121004).
Purpose and benefits: Form 990-N filings provide limited information to the IRS, making it difficult for Tax-Exempt Government Entities (TEGE) to detect non-compliance among that population of EOs. This use case provides improved ability to predict non-compliance.
Topic area: IT
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-01-22
US federal AI use case: Volunteer Income Tax Assistance (VITA) Generative AI Chatbot Proof of Concept
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Ask-CFO Research Aide
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: 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) search on a number of Chief Financial Officer (CFO) specific documents. In addition to the existing research aide capability and user interface (UI), extensions will be developed to incorporate AI Agents that can perform actions such as document summary comparison over fiscal year, or extract information from tables.
Purpose and benefits: This will help CFO users search and obtain information from their repository of relevant documents.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Sandbox for Large Language Model Research
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: This sandbox would enable IRS Criminal Investigation (CI) to research, test, and customize Large Language Model (LLM) AI tools to increase operational efficiencies within IRS-CI.  This will positively benefit IRS-CI by operationalizing functional LLM tools for our agency, reducing the administrative burden and increasing access to a shared agency knowledge base.
Purpose and benefits: The sandbox will benefit our agency by enabling IRS-CI to research, test, and customize LLM AI tools to increase operational efficiencies within IRS-CI.  This will positively benefit IRS-CI by operationalizing functional LLM tools for our agency, reducing the administrative burden and increasing access to a shared agency knowledge base.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Digital Forensics Chatbot for IRS Employees
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Generative AI for Form 1040X: Support Tool for Amended Returns (STAR)
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: DATA Act Bot for Procurement Data Matching
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: AI is used to validate whether public contract spending reporting matches the information in PDF contract documents. The Digital Accountability and Transparency Act requires consistent and reliable contract spending to be reported to the public in the Federal Procurement Data System (FPDS). AI powered bot  automates comparing FPDS data with contract documents to improve the quality of contract spending reporting.
Purpose and benefits: Policymakers and the taxpaying public can benefit by receiving higher quality, more reliable contract spending data that has been vetted by AI powered data validation. This contract spending data is available to the public on USASpending.gov.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2022-03-31
US federal AI use case: Taxpayer Services Chatbot
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: 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 Income Tax Credit (EITC), change of address, identity theft, extension to file topics. The Intent Engine, using Natural Language Processing (NLP) and AI algorithms classifies taxpayers' utterances (questions) into intents (requests). The answers provided to the taxpayers are not created by Generative AI. The responses of the chatbot and business logic is predetermined by content owners. This AI tool aims to classify/navigate to a correct predetermined response.
Purpose and benefits: This chatbot provides means for the taxpayers to receive information without need to speak with an Agent.  The benefits include reduced taxpayer burden as well as increased internal IRS operational efficiencies.
Topic area: Service Delivery
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2023-03-14
US federal AI use case: Machine Translation
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: 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 being assessed and evaluated by the primary users, the Linguistic Policy, Tools and Services (LPTS) organization, through integration with their existing processes. The MT application focuses on translation for existing text and labels into Spanish with the ultimate goal of becoming an enterprise solution for a variety of non-English translations.
Purpose and benefits: Machine translation is used to translate text and file-based content from and to English, Spanish, Chinese (Traditional, Simplified), Korean and Vietnamese used by MAS/LPTS to speed up responses to taxpayers.
Topic area: Service Delivery
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2023-09-25
US federal AI use case: Employee Resource Center (ERC) Chatbot
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Enable self-service for employees seeking information about various employee-related topics.
Purpose and benefits: This chatbot has proven to reduce the reliance on the ERC staff to answer employee inquiries greatly improving ERC operations.
Topic area: HR
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2021-06-01
US federal AI use case: Winnie Chatbot for Employee IT FAQs
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Development stage: Retired
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Synthetic Data Engine
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: 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 dimensions, aging people, households and businesses over time, and tightly correlating the synthetic data to actual familial and socioeconomic attributes of US taxpayers.
Purpose and benefits: Generate synthetic data for testing IRS tax processing systems and significantly reduce the risk of exposing taxpayer information while enabling more comprehensive testing of functional test cases. This system can now use to simulate fraud cases for testing negative and positive automated test cases.
Topic area: IT
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2022-03-28
US federal AI use case: AI Contract Document Toolbox
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: 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 procurement process and fail to catch some errors.
Purpose and benefits: Generative AI chat reduces the friction in the procurement process by providing automated assistance with drafting and reviewing documents. Procurement professionals can quickly generate first drafts of procurement documents and then refine according to specific agency needs. Further quality reviews occur more quickly when both AI and government staff vets documents. Further, AI can be instructed to incorporate lessons learned, new procurement policy goals, and detect common errors that have occurred in the past.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: AI Contract Document Toolbox drafts procurement narratives, supports vendor evaluations, and aids source selection. It offers recommendations but does not approve contracts. Final decisions are made by agency officials using multiple information sources.
First used: 2025-07-01
US federal AI use case: Tax Disclosure Text Clustering
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: This project takes written data from uncertain tax disclosures, cleans up errors in the text, incorporates additional attachment information not present in the Compliance Data Warehouse (CDW), and groups the disclosures into semantically similar topics. This creates a single source to find uncertain tax disclosure declarations and additional information to facilitate review. The process follows three steps: 1) extraction of text from Form 8275-R, Regulation Disclosure Statement, and Schedule UTP, Uncertain Tax Position Statement, from CDW and supplemental attachments, 2) embedding of text into numeric vectors using the E5-large model, and 3) performing hierarchical clustering on the text vectors to group the disclosures.
Purpose and benefits: The primary goal of this use case is to gain insights useful for improving future examinations and outcomes.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2024-09-30
US federal AI use case: Employee Retention Credit Text Clustering
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Application of Line-Item Consolidation algorithm to identify highly similar text and apply additional features to similar clusters to enable identification of suspicious texts.
Purpose and benefits: This enables the agents to increase their capacity and reduce time researching and identifying potential suspicious trends of topics used in claims.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2024-01-31
US federal AI use case: Safeguards
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The Office of Safeguards needs an AI assistant that can access its documents (e.g., IRM, Publication 1075, SOPs [PDFs/Word Docs], configuration checklists [excel], NIST documents [PDF] to generate responses and products. Ideally, the AI will be able to provide researched responses to questions employees may have. Additionally, it would also be able to review agency and IRS responses on documents (docx, xlsx) to ensure they're appropriate. The documents may be up to 400 pages. Currently all of this is a manual process.
Purpose and benefits: Quicker response time and better quality responses
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: IRS IaaP Accelerator ( AI-Powered Infrastructure as a Product Accelerator)
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: Federal agencies face slow, manual, and error-prone infrastructure delivery: Cloud adoption is blocked by governance bottlenecks (NIST, FedRAMP, IRS 1075). Compliance evidence is manual and audit-heavy. Developers struggle with multi-cloud complexity and vendor lock-in. Security drifts accumulate until quarterly reviews, leading to delayed taxpayer service delivery.
Purpose and benefits: The IRS IaaP Accelerator embeds AI Personas (Architect, Engineer, Product Manager, Security Generative Pre-trained Transformer (GPT)) into the delivery pipeline: Advisory (Level 1) – Personas draft architectures, Infrastructure as Code (IaC) modules, and guardrails. Semi-Autonomous (Level 2) – AI validates IaC pull requests, explains security findings, updates backlogs, and posts PR comments. Autonomous (Level 3) – Drift detection triggers cloud-based AI. Cloud-based AI proposes Terraform diffs and opens remediation PRs. Compliance-as-Code validates automatically. Human remains in loop for approvals. Model Context Protocol (MCP) servers allow these personas to work across multi-cloud environments without retraining or rewriting — AI sees one normalized contract, regardless of provider. 80% faster remediation of drift and policy violations. Always-compliant infrastructure with auditable AI-generated evidence. Secure-by-default modernization across multiple clouds, aligned to federal mandates. Human-in-the-loop AI ensures governance while accelerating delivery.
Topic area: IT
Development stage: Pre-deployment
Risk category: High-impact
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Qualitative Text Classification for Analysis
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The AI tool, leveraging internal large language models, is intended to increase the operational efficiency of qualitative research within Small Business / Self-Employed (SB/SE) Research, increase research scope, complexity, and decrease overall time to customer.
Purpose and benefits: Leveraging the generative AI applications within the large language model will allow analysts to benefit from an automated secondary analysis, improving overall reliability of analytical deductions and validating manually derived analytical outputs.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Forms Conversion
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The AFCS tool assists the team with working a higher volume of form conversion than we can sustain manually due to the loss of staff due to Deferred Resignation Program (DRP) and retirements.
Purpose and benefits: The AFCS tool saves significant time in the authoring process by providing an initial draft version of the form.
Topic area: Service Delivery
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Zero Paper AI Routing for Digitalization
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The Zero-Paper Initiative (ZPI) aims to eliminate the flow of paper into the IRS. As a result, all non-tax, mail submissions from taxpayers will be scanned through external vendors to enable the routing of digital documents and their associated data to the IRS for further processing/review. The digital documents and associated data will be routed downstream to various business units and processing systems based on the form type and other similar criteria. Leverages AI to make routing decisions at various levels of complexity as well as using AI to support routing at record processing time or as a tool to generate routing logic dictionaries.
Purpose and benefits: The expected benefits are: Automated routing: to instantly analyze form type or other characteristics to determine where files should be sent, removing delays from manual handling; Reduced bottlenecks: Files are less likely to get stuck in queues or misrouted, which speeds up workflows and improves efficiency; Pattern recognition: The system can be trained to recognize patterns in file characteristics and ensure they’re routed consistently according to rules or learned behaviors; Error reduction: the system will help reduce misfiling and simplify logic such as duplication prevention; Handling large volumes: The system will be able to scale, allowing it to handle larger workflows which would require additional efforts otherwise; Dynamic adaptation: The system will be able to easily adjust to the addition or removal of downstream systems or changes in routing processes without requiring significant system overhauls.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
US federal AI use case: Modular Code Assistant (Explain & Refactor Small Methods) for Automation Test Application Development
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: 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.
Purpose and benefits: The objective is to implement a secure, localized AI chatbot leveraging Retrieval-Augmented Generation (RAG) to enhance test automation productivity.
Topic area: IT
Development stage: Deployed
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions
First used: 2025-09-03
US federal AI use case: Digitalization AI Agent
Agency: Department of the Treasury - Internal Revenue Service (IRS)
Problem addressed: The AI will solve functions involved with the large inventory of disparate digitalization projects and systems that make them difficult to organize.
Purpose and benefits: The AI will assist with document organization, recall, and sorting. The AI will reduce errors and save time by automating form filling and recalling information. The AI can quickly analyze large datasets, revealing trends, risks, opportunities, and providing summaries. The AI can assist in managing tasks prioritizing workloads, suggest next actions, and helping employees focus on high-value tasks. The AI-powered search can quickly find relevant internal documents, policies, or best practices. In summary, the AI will save time, effort, and complete tasks more efficiently.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The output is not presumed to be high-impact and is not used as the principal basis for significant decisions/actions