GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Virtual Benefits Assistant
Agency: Federal Reserve Board - Division of Management
Problem addressed: Virtual Assistant is designed to answer questions on how to navigate the site and answer general questions
Purpose and benefits: Get general answers on benefits and links to reference materials for additional information or benefits contact information
Topic area: HR
Development stage: Deployed
First used: 2025-03-01
US federal AI use case: Data Quality Prioritization Dashboard
Agency: Federal Reserve Board - Division of Information Technology
Problem addressed: Edits are needed to call report data in a short period of time
Purpose and benefits: Quicker outreach and data revision to enhance data quality for end users
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Monitoring Earnings Conference Calls
Agency: Federal Reserve Board - Division of Monetary Affairs
Problem addressed: Need to identify mentions of using genAI alongside research and development
Purpose and benefits: Information on R&D at reported organizations
Topic area: Other – Economic & Financial
Development stage: Pre-deployment
US federal AI use case: Automatic PSC Classification
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: FTC receives uncategorized complaints from multiple channels and contents of comments varies widely by source.
Purpose and benefits: Enhances data quality by automatically classifying consumer complaints by PSC (Product and Sevice Code), minimizing the need for manual effort.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-06-01
US federal AI use case: Group Duplicate Complaints
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: Consumers submit multiple complaints for the same issue using various channels, which impacts the ability of Law Enforcement to quickly search and identify leads.
Purpose and benefits: Assists law enforcement with identifying leads in fraud investigations by efficiently grouping duplicate complaints.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-06-01
US federal AI use case: Chatbot
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: 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.
Purpose and benefits: Provide automated chatbots capabilities for ReportFraud.ftc.gov and IdentifyTheft.gov and replacing assisted chat services.
Topic area: Other
Development stage: Retired
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-06-01
US federal AI use case: Word Cloud
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: Assist Law Enforcement to reduce the time required to analyze search results
Purpose and benefits: Quickly grasp the essence of search results by visually exploring complaints through interactive word clouds.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-06-01
US federal AI use case: Graph Analytics
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: Assist with identifying connections between complaints as consumers may file same type of complaints against a company but with different names, phone numbers or when company use different names, aliases, phone numbers. It helps with grouping the complaints for further review and analysis.
Purpose and benefits: Helps law enforcement to explore and identify relationships and connections between complaints.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-06-01
US federal AI use case: Analytic Sandbox
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: 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 visualization and reports.
Purpose and benefits: Enable FTC Data Analysts to perform data analysis and respond to inquiries from various agency departments.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-06-01
US federal AI use case: Developer Productivity
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: Solve challenges and inefficiencies in development
Purpose and benefits: Enhance developer productivity by improving code quality
Topic area: Other
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-06-01
US federal AI use case: Relativity Active Learning
Agency: Federal Trade Commission - BC/BCP/OGC
Problem addressed: Manual document coding is very time consuming during the legal review process
Purpose and benefits: Enhance eDiscovery process efficiency for consumer protection and competition investigation and litigation
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2020-06-01
US federal AI use case: Relativity Integrated Veritone
Agency: Federal Trade Commission - BC/BCP/OGC
Problem addressed: Manual document translation and audio file transcription is time consuming and of inconsistent level of accuracy.
Purpose and benefits: Enhance eDiscovery process efficiency for consumer protection and competition investigation and litigation
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2022-06-01
US federal AI use case: Microsoft 365 Copilot
Agency: Federal Trade Commission - OCIO
Problem addressed: Staff performing manual, time-consuming activities, taking away focus from higher-value tasks.
Purpose and benefits: Enhance general productivity of FTC staff to achieve agency mission more efficiently
Topic area: Other
Development stage: Pilot
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-06-01
US federal AI use case: CRC Call Summarization
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: 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.
Purpose and benefits: Improve data quality and customer experience
Topic area: Other
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-06-01
US federal AI use case: Azure Databricks Assistance
Agency: Federal Trade Commission - BE/BC/OCIO
Problem addressed: Address the challenges of manual data analysis and coding inefficiencies
Purpose and benefits: Enhance data analysts productivity and support accurate, efficient decision-making.
Topic area: Other
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-06-01
US federal AI use case: IVR Automated Voice Assistant
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: Reduce cost of call volume for requests for information which are low value contacts for law enforcements while maintaining positive customer experience.
Purpose and benefits: Enhances user experience by providing RFIs (Request for Information) accessible 24x7 rather than limited to working hours.
Topic area: Other
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-06-01
US federal AI use case: GenAI Mail Scan
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: The difficulties of interpreting handwritten and poorly formatted documents by staff leads to increased manual labor required to accurately read and enter data into all fields.
Purpose and benefits: Automatic processing of handwritten mail received by the agency enhances accuracy, reduces manual labor, accelerates processing times, and improves data quality.
Topic area: Other
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-06-01
US federal AI use case: Azure ML
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: Ad-hoc Machine learning tasks are taking longer time to train and review results
Purpose and benefits: Assist data analysts with training and testing machine learning models .
Topic area: Other
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-06-01
US federal AI use case: DNC IVR Audio File Transcription
Agency: Federal Trade Commission - BCP/DCRO
Problem addressed: 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.
Purpose and benefits: Enables faster, more efficient complaint review by allowing to quickly search and read comments instead of listening to audio, improving accessibility, productivity, and response times.
Topic area: Other
Development stage: Retired
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-06-01
US federal AI use case: Counterparty Risk Anomaly Detection
Agency: Department of Housing and Urban Development - Ginnie Mae
Problem addressed: Ginnie Mae is responsible for analyzing counterparty risk profiles of mortgage issuers who participate in Ginnie Mae’s program. Ginnie Mae analyzes data from multiple sources to identify potential risks and areas of focus.
Purpose and benefits: To enhance the identification of data patterns, Ginnie Mae uses machine learning algorithms, specifically clustering and genetic techniques. These algorithms detect potential risk areas, enabling a focused approach to subsequent analysis by Ginnie Mae staff.
Topic area: Other
Development stage: Deployed
First used: 2022-05-01
US federal AI use case: Subledger Data Quality Machine Learning
Agency: Department of Housing and Urban Development - Ginnie Mae
Problem addressed: Ginnie Mae analyzes Master Sub-Servicer (MSS) transaction data on a monthly cadence.   The AI solution allows Ginnie Mae to detect anomalies in this data that would not be detected via traditional methods.
Purpose and benefits: Through its use of machine learning models, Ginnie Mae has enhanced its ability to identify data inconsistencies and exceptions associated with its MSS transaction data. Through the early detection of these anomalies, Ginnie Mae is able to reduce manual adjustments to financial reporting, which yields cost and time savings.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2021-04-21
US federal AI use case: Automated Draft Narrative Reports

Previously "Automating Draft Counterparty Credit Narrative Reports"; have since expanded to general enterprise use.
Agency: Department of Housing and Urban Development - Ginnie Mae
Problem addressed: Ginnie Mae uses Natural Language Generation (NLG) to implement coded rules and generate draft narratives/memos. This application of AI enables processing efficiency and reduces errors that can occur in a manual process.
Purpose and benefits: The AI solution ensures greater accuracy in reporting, time savings, and eliminates manual steps that are prone to error.
Topic area: Other
Development stage: Deployed
First used: 2022-04-01
US federal AI use case: Voice of the Customer
Agency: Department of Housing and Urban Development - Office of the Chief Financial Officer (OCFO)
Problem addressed: 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.
Purpose and benefits: The Voice of the Customer application brings the best-in-class voice transcription, speech, and text analytics to customer feedback surveys,  contact center calls and chats. It allows HUD to unlock critical insights that provide deeper understanding of how to support and manage our program/service delivery and contact center providers, and ultimately to improve customers’ experiences.
Topic area: Service Delivery
Development stage: Deployed
First used: 2024-08-30
US federal AI use case: Quantitative Text Analysis
Agency: Department of Housing and Urban Development - Office of the Chief Financial Officer (OCFO)
Development stage: Retired
US federal AI use case: Translation of Digital Media
Agency: Department of Housing and Urban Development - Office of Public Affairs (OPA)
Development stage: Retired
US federal AI use case: Microsoft Copilot
Agency: Department of Housing and Urban Development - Office of the Chief Information Officer
Problem addressed: To support the HUD Secretary's directive to improve efficiency, HUD has a need to offer office productivity tools and capabilities. This would include the ability to automate repetitive, manual, and time-consuming tasks, streamline processes, and analyze data and produce insights and recommendations.
Purpose and benefits: Microsoft Copilot will help with employee productivity by helping users draft documents, create summaries, generate data insights, and automate routine tasks. HUD will use Copilot to streamline workflows and automate repetitive activities within Microsoft apps.
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-03-01
US federal AI use case: Amazon Textract for automatic signature identification
Agency: Department of Housing and Urban Development - Office of Housing
Problem addressed: The Homeownership Center (HOC) staff is required to review and validate each Case Binder.
Purpose and benefits: Amazon Textract automatically analyzes PDF documents to detect if the document has a signature panel and if the document is signed or not. This tool will help to automate and improve the overall efficiency of the case binder business process within Federal Housing Administration (FHA) Catalyst and potentially save about 30 minutes time.
Topic area: Admin Functions
Development stage: Pre-deployment
First used: 2025-05-01
US federal AI use case: Email Assistant
Agency: Department of Housing and Urban Development - Office of Housing
Problem addressed: 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.
Purpose and benefits: This proposed AI solution will deliver assistance to the FHA Resource Center customer service representative through email processing guided response using AI-powered classifications, templates, and checklists. The GenAI acts like a virtual subject matter expert in providing decision support in the processing of customers emails being submitted into the FHA Resource Center.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: CAISY - Workforce Training Conversation Simulator
Agency: Department of Housing and Urban Development - Office of the Chief Human Capital Officer
Problem addressed: CAISY is designed to solve the challenge of helping employees practice and improve difficult workplace conversations in a safe, realistic environment.
Purpose and benefits: CAISY helps employees practice and receive real-time feedback on workplace conversations.  It offers several real-world scenarios, allowing users to practice skills like resolving customer concerns, sharing constructive feedback, and leading difficult conversations, within a safe learning environment.
Topic area: HR
Development stage: Deployed
First used: 2023-09-01
US federal AI use case: FHA Resource Center Chatbot
Agency: Department of Housing and Urban Development - Office of Housing
Problem addressed: 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.
Purpose and benefits: The FHA Resource Center Virtual Assistance Chatbot will assist HUD clients utilizing searching for public policies through the 2,200+ FAQs in the FAQ Knowledge base.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: AI-Enabled Automated Visual Inspection for Mint Product Quality Assurance in Production Lines
Agency: Department of the Treasury - United States Mint (USM)
Problem addressed: The system is intended to automate the visual inspection of product (e.g., coins) on production lines to identify defects, inconsistencies, or anomalies that affect product quality. Currently, operators leverage systems that use comparable references to identify differences and, in some cases, conduct manual, visual quality checks using traditional inspection tools, which are labor-intensive, subject to human variability, and limited in speed. The AI solution aims to address these challenges by providing consistent, high-speed, high-accuracy inspection capabilities that can operate continuously across multiple shifts.
Purpose and benefits: Implementation of automated visual inspection is expected to improve production efficiency, reduce operator workload, enhance coin quality, and decrease waste due to undetected defects. By reducing the incidence of defective products entering circulation or collector channels, the Mint intends to strengthen public trust and maintain brand integrity. Increased throughput and more stable quality control processes also support mission-critical delivery timelines and ensure the Mint can meet the nation’s coinage demand reliably.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The AI system's outcome does not affect civil rights, safety, or access to critical services, therefore, it doesn't meet the criteria for a high-impact AI use case.
US federal AI use case: AI-Driven Predictive Maintenance for Mint Manufacturing Equipment and Production Assets
Agency: Department of the Treasury - United States Mint (USM)
Problem addressed: The system is intended to identify patterns in equipment performance and lifecycle data to predict mechanical failures before they occur. Currently, maintenance relies on fixed schedules or reactive responses after failures, resulting in unplanned downtime, potential safety issues, and increased maintenance costs. The solution shall analyze machine data to anticipate component degradation and recommend optimal maintenance intervals.
Purpose and benefits: Predictive maintenance capabilities will improve equipment uptime, support safer manufacturing operations, extend the usable life of key assets, and reduce costs associated with emergency repairs and production stoppages. Increased operational reliability directly supports the Mint's mission to deliver circulating and numismatic products on schedule for the nation and for collectors. Better maintenance forecasting also supports resource optimization and reduces waste.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The AI system's outcome does not affect civil rights, safety, or access to critical services, therefore, it doesn't meet the criteria for a high-impact AI use case.
US federal AI use case: Generative AI Solutions for Workplace Productivity (aka Gemini)
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to improve employee productivity by providing smarter tools for employees to summarize, draft, and navigate operational records.
Purpose and benefits: NARA is exploring how Generative AI tools can help employees work more efficiently and creatively. These AI tools can automate tasks, enhance communication and collaboration, and improve decision-making.
Topic area: Other
Development stage: Deployed
First used: 2025-09-01
US federal AI use case: A1 Museum AI Project
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: 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.
Purpose and benefits: NARA will use AI generated tags and topics to recommend and make records available to visitors from a pool of approximately 2 million digital records, freeing up staff time and enhancing and personalizing the user experience for A1 museum visitors.
Topic area: Other
Development stage: Deployed
First used: 2025-10-01
US federal AI use case: NARA@WORK Kendra Search
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to solve the problem of inefficient employee information discovery by replacing outdated keyword search with a natural language system that provides instant, context-aware answers from disparate agency resources.
Purpose and benefits: NARA is implementing an AI-driven contextual information retrieval tool (e.g., Amazon Kendra) to modernize search functionality on the NARA@Work internal site. This will enable staff to use Natural Language Processing (NLP) to query a wide range of agency information (Work Life, Benefits, Policies, etc.) and receive "Suggested Answers."
Topic area: Other
Development stage: Deployed
First used: 2025-09-01
US federal AI use case: Amelia Earhart AI Search
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to solve the manual search limitations when processing massive historical datasets by using Natural Language Processing to accurately locate and declassify specific records required for public release
Purpose and benefits: The intended purpose of this AI Proof of Concept (POC) is to efficiently retrieve and prepare for release all government records related to Amelia Earhart and her final trip, following the Presidential directive of September 26, 2025.
The expected benefit is to leverage Natural Language Processing (NLP) search capabilities to overcome the limitations of traditional search methods, enabling the National Archives to effectively and accurately locate the necessary results from vast holdings and fulfill its legal mandate to make these specific historical records accessible to the American public.
Topic area: Other
Development stage: Deployed
First used: 2025-12-01
US federal AI use case: AI Pilot Project to Screen and Flag for Personally Identifiable Information (PII) in Digitized Archival Records
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: 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.
Purpose and benefits: NARA is using AI to automatically find and remove sensitive personal information from its digitized historical records. This will make the process faster, more accurate, and better protect privacy while allowing for greater public access to these records. The AI will also help NARA manage its vast collection and adapt to future privacy needs.
Topic area: Other
Development stage: Pilot
First used: 2024-07-01
US federal AI use case: AI based Semantic Search for National Archives Catalog (aka ArchiAI)
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to solve the problem of "unsophisticated" keyword-based search limitations by implementing semantic search that understands user intent and historical context, allowing researchers to find relevant records even when they don't match exact search terms.
Purpose and benefits: NARA is using AI to create a smarter search engine for its online catalog. This new search understands the meaning behind your search, not just the keywords, and can even find hidden connections between documents. This will make research faster, easier, and more insightful.
Topic area: Other
Development stage: Pilot
First used: 2024-09-01
US federal AI use case: Auto-fill of Descriptive Metadata for Archival Descriptions
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: 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 instantly searchable for the public.
Purpose and benefits: NARA is using AI to automatically create descriptions (metadata) for its digital archives. This will save archivists time, make records easier to find, and help the public better understand the information in the National Archives Catalog.
Topic area: Other
Development stage: Pre-deployment
First used: 2024-09-01
US federal AI use case: Topic Summarizer and Entity Extraction using AI
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to solve the problem of unsearchable digital collections caused by massive descriptive backlogs, automating the creation of metadata for billions of digital objects to ensure they are immediately discoverable and understandable for the public without years of manual archival processing.
Purpose and benefits: NARA plans to use AI to automatically create descriptions for its digital objects. This will free up archivists' time and make it easier for people to find and understand the digital materials in NARA's system.
Topic area: Other
Development stage: Pre-deployment
First used: 2026-08-01
US federal AI use case: Create an AI based knowledge articles user interface for working with CRG documents
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to improve the case processing by providing staff with an automated retrieval tool that navigates the complex Case Reference Guide to deliver instant, accurate answers for veteran and personnel record requests.
Purpose and benefits: NARA is developing an AI-powered tool to help its National Personnel Records Center (NPRC) staff quickly find information in their Case Reference Guide (CRG). This will improve the speed and accuracy of their work, leading to better customer service and less time spent training new employees.
Topic area: Other
Development stage: Pre-deployment
First used: 2024-10-01
US federal AI use case: EOP 42 Search PoC using AI Based Semantic Search
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to solve the problem of inefficient information retrieval within Presidential email archives by testing whether AI-driven semantic search can understand context and intent more effectively than traditional keyword matching to improve record accessibility.
Purpose and benefits: The purpose of this Proof of Concept (POC) is to evaluate whether AI-driven semantic search provides a demonstrable improvement over the current keyword-based search experience for accessing EOP 42 digital records, including email archives.
Topic area: Other
Development stage: Pre-deployment
First used: 2026-12-01
US federal AI use case: Freedom of Information Act (FOIA) Discovery AI Pilot
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: 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.
Purpose and benefits: NARA is piloting the use of AI to improve its responses to Freedom of Information Act requests. This includes AI tools for finding relevant records and automatically redacting sensitive information, which will make the process faster, more accurate, and compliant with privacy laws.
Topic area: Other
Development stage: Pre-deployment
First used: 2026-12-01
US federal AI use case: Archives.gov AI search
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to solve the problem of "unstructured data challenges" in a vast, complex catalog by replacing traditional literal-match search with semantic technology that understands researcher intent, connects disparate records, and surfaces relevant results that would otherwise remain hidden behind inconsistent terminology.
Purpose and benefits: NARA is using AI to create a smarter search engine for its online Archives.gov public site. This new search understands the meaning behind your search, not just the keywords, and can even find hidden connections between documents. This will make research faster, easier, and more insightful.
Topic area: Other
Development stage: Pre-deployment
First used: 2026-08-01
US federal AI use case: Develop a Natural Language Based Chat Interface (like ChatGPT) to Interact With the Archival Documents
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to solve the problem of high barriers to entry for historical research by replacing complex database queries with a natural language interface that allows users of all skill levels to discover records and gain insights through simple conversation.
Purpose and benefits: NARA plans to develop an AI-powered chat interface, similar to ChatGPT, that will allow users to easily explore its digital archives by asking questions in everyday language. This will make historical documents more accessible and engaging, leading to more efficient research and a deeper understanding of history.
Topic area: Other
Development stage: Pre-deployment
First used: 2027-09-01
US federal AI use case: Automated Data Discovery and Classification Pilot
Agency: National Archives and Records Administration - National Archives & Records Administration (NARA)
Problem addressed: The AI is intended to solve the problem of inefficient manual data governance and risk assessment by testing automated data classification techniques to organize unstructured datasets, accelerate information discovery, and more accurately identify potential security or privacy vulnerabilities.
Purpose and benefits: NARA plans to test how well AI can automatically organize and categorize its data. This pilot project will help NARA understand its data better, find information faster, improve efficiency, and manage risks more effectively.
Topic area: Other
Development stage: Pre-deployment
First used: 2027-09-01
US federal AI use case: Anomaly Detection and Precursor Identification in UAV flight data
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: This project is using past algorithms developed by the NASA ARC (Ames Research Center) Data Sciences Group and modifying them with application to identifying previously-unknown anomalies and precursors to known issues in UAV (Unmanned Aerial Vehicle) test flights.
Purpose and benefits: This project is using past algorithms developed by the NASA ARC (Ames Research Center) Data Sciences Group and modifying them with application to identifying previously-unknown anomalies and precursors to known issues in UAV (Unmanned Aerial Vehicle) test flights.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: ExoMiner discovery of ExoPlanets via data from the Kepler and TESS space telescopes
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: ExoMiner is being used to statistically validate exoplanets detected by the Kepler space telescope and to identify promising exoplanet candidates for the TESS space telescope
Purpose and benefits: ExoMiner has been used to validated 370 new exoplanets identified in data from the Kepler space telescope. It is now being used to idenitify promising exoplanet candidates in TESS mission data. It promises to significantly reduce the manual effort required while improving the accuracy of identify promising TESS exoplanet candidates and to reject astrophysical false positives and instrumental false alarms.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Earth Information System Fire Pilot
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: EIS-Fire developed a visualization and analysis data portal providing access to a variety of fire data products in a cloud-optimized, analysis-ready format. The team also developed an algorithm for mapping active fire perimeters from satellite fire detections and updated the representation of fire emissions in the Goddard Earth Observing System (GEOS) model to use improved Visible Infrared Imaging Radiometer Suite (VIIRS) fire data. Finally, EIS-Fire engaged stakeholders in identifying use cases to inform data portal development.
Purpose and benefits: Earth Information System Fire Pilot
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Enabling Autonomous Differential Drag Control and Attitude Maneuvering Using Onboard Artificial Intelligence (AI) for SmallSat Distributed Space Missions (DSMs)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work is in enabling Autonomous Differential Drag Control and Attitude Maneuvering Using onboard Artificial Intelligence (AI) for SmallSat Distributed Space Missions (DSMs). AI systems include autonomous navigation & control, with inputs including location, state, and systems data.
Purpose and benefits: Enabling Autonomous Differential Drag Control and Attitude Maneuvering Using Onboard Artificial Intelligence (AI) for SmallSat Distributed Space Missions (DSMs)
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Explainable and robust deep semi-supervised model for multi-class anomaly detection in flight data
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: This model is a semi-supervised deep learning based anomaly detection for aircraft flight data. It is designed to work when a small subset of data is reviewed and labeled by experts. The most useful realm is where the size of labeled data is small, so that any supervised learning approach won't reach optimum performance.
Purpose and benefits: This model is a semi-supervised deep learning based anomaly detection for aircraft flight data. It is designed to work when a small subset of data is reviewed and labeled by experts. The most useful realm is where the size of labeled data is small, so that any supervised learning approach won't reach optimum performance.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Forest responses to climate change
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work explores a variety of Physics-Guided ML techniques to contribute to terrestrial ecosystem and carbon cycle modeling and related fields.
Purpose and benefits: Forest responses to climate change
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: From Single Spacecraft to Synchronized Swarms: Fault Diagnosis for Distributed Spacecraft Mission Resilience
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: From Single Spacecraft to Synchronized Swarms: Fault Diagnosis for Distributed Spacecraft Mission Resilience. This project is in AI/ML-assisted fault detection and diagnosis to complement human capability for diagnosing issues.
Purpose and benefits: From Single Spacecraft to Synchronized Swarms: Fault Diagnosis for Distributed Spacecraft Mission Resilience
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: HEASARC X-ray Spectra and Light Curve data sets
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work creates and makes available X-ray Spectra and Light Curve data sets for ML analysis from the High Energy Astrophysics Science Archive Research Center (HEASARC), which includes data sets from multiple space-based observatories.
Purpose and benefits: HEASARC X-ray Spectra and Light Curve data sets
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: High Rate Digital Spectrometer Enhancement with Neural Network AI
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: As spaceborne spectrometer increases in spectral resolution, the growth in spectral data volume and the limited space to Earth communication bandwidth are prone to be a problem for achieving higher fidelity science measurement. To effectively utilize the limited communication bandwidth while reducing the loss of science data, a smart on-board neural network processing that had been successful in the field of machine learning and artificial intelligence (AI) is proposed to potentially reduce the spectral data volume, retaining the essential spectral information and mitigating the effect of human introduced radio signal interference.
Purpose and benefits: High Rate Digital Spectrometer Enhancement with Neural Network AI
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: High Resolution Earth and Planetary Atmospheric Predictions using Machine Learning
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: We are developing a machine learning (ML) tool that can predict high-resolution in situ atmospheric conditions using relatively lower resolution remote data e.g., from an orbiting spacecraft.
Purpose and benefits: Our ML tool could be used to map and track atmospheric cycling on Earth and planetary bodies, not only as a fundamental science tool, but also as a mechanism for tracking planetary weather from orbit.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model
Agency: National Aeronautics and Space Administration - ARC: Ames Research Center
Problem addressed: Our team developed high-performance unsupervised deep machine-learning models for the detection of flight-operations anomalies. The models’ engineered-feature (latent) spaces are composed of discrete variables, which allows an integration with quantum computing because (part of) the latent-space variables can be populated by quantum-state measurements, which are discrete in nature.
Purpose and benefits: This project enabled the additional development of two quantum-capable unsupervised deep-learning models with discrete latent space (Bernoulli and Boltzmann priors). The models exhibit state-of-the-art anomaly-detection performance and robustness. Future versions of our models will be deployed on in-time flight-operations data streams. They will also be used to assess the performance and resource requirements of quantum and other physical computing devices.
Topic area: Science
Development stage: Pilot
US federal AI use case: HyperMapping with Hyperspectral Precise Pointing Optical Sensor (HYPPOS) for Decadal Survey Mission Pathfinder Activities
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work uses HyperMapping techniques on Hyperspectral Precise Pointing Optical Sensor (HYPPOS) data for Decadal Survey Mission Pathfinder Activities. The effort is developing AI tools using Google GPUs and NVIDIA TPUs with thermal imagery to enable curiosity-driven remote sensing of hyperspectral features using an optical pointing hyperspectral spectrometer component.
Purpose and benefits: Twin experiments with archived thermal and hyperspectral imagery are being used to develop and test novel AI pointing algorithms.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Knowledge Capture: helionauts.org
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work uses natural language processing to perform text-based knowledge capture for the helionauts.org community, which is a cloud-based community of practice for Heliophysics experts.
Purpose and benefits: Knowledge Capture: helionauts.org
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: M4OPT: Multi-Mission Multi-Messenger Observation Planning Toolkit
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: M4OPT: Multi-Mission Multi-Messenger Observation Planning Toolkit. This work includes considerations for data integration and use across multiple Science missions and leveraging multiple sensors, suitable for traditional human or AIML-assisted analysis.
Purpose and benefits: M4OPT: Multi-Mission Multi-Messenger Observation Planning Toolkit
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Intelligence for Small Satellites
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: The MAtISSE project seeks to develop a new approach to rapid, real-time extraction and classification of photometric light curves using a modern differencing technique and advanced DL integrated onto a compact graphics processing unit.
Purpose and benefits: MAtISSE will develop this technique, which has the potential to greatly reduce the amount of data transmitted by an observatory, for implementation on a future CubeSat-based science payload with a thorough assessment of power requirements vs. processing and communications bandwidth. This technology will be especially applicable to small, power-limited spacecraft and may enable observations and science return that would be challenging or even impossible otherwise.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Machine Learning of Ocean Worlds Laboratory Analog Seawater Volatiles
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: This work focuses on algorithm-development for software that can predict the composition of an ocean using mass spectra of volatile gases analyzed.
Purpose and benefits: Development specific for ocean worlds exploration (e.g., Europa / Enceladus), but applicable for Earth.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: MERRAMax Automated feature selection
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: There is growing interest in using Intergovernmental Panel on Climate Change (IPCC)-class climate model outputs in ecological research. These models provide realistic, global representations of the climate system, projections for hundreds of variables (including Essential Climate Variables), and combine observations from an array of satellite, airborne, and in-situ sensors. Unfortunately, direct use of this important class of data has been limited due to the large size and complexity of model output collections, internal file complexity, and limited means for dynamically creating derived products of interest. To address these limitations, we have developed an AI-based stochastic convergence technology, called MERRA/Max, that combines HPC and Princeton's Maximum Entropy (MaxEnt) software to rapidly subset and identify potential drivers of change among the hundreds of variables in a climate model output collection. MERRA/Max reduces dimensionality by iteratively drawing on MaxEnt's capacity for feature selection to winnow randomly selected climate variables until a stable set of predictors is found. Preliminary work focuses on the MERRA reanalysis, a product of NASA's GEOS-5 modeling framework. At 1 petabyte in size, MERRA comprises over 700 climate variables and spans 1970 to the present at high temporal resolution. We evaluated MERRA/Max by modeling the bioclimatic envelope of Cassin's Sparrow using MERRA and BioClim variables.
Purpose and benefits: MERRAMax Automated feature selection
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Multi-temporal analytic center framework
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Multi-temporal analytic center framework. This is an integrating mechanism rather than a specific AI or ML technology.
Purpose and benefits: Multi-temporal analytic center framework
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Optimization of Machine Learning Algorithms for Lidar to Detect Aerosols, Clouds, and the PBL (Planetary Boundary Layer)
Agency: National Aeronautics and Space Administration - GSFC: Goddard Space Flight Center
Problem addressed: Optimization of Machine Learning Algorithms for Lidar to Detect Aerosols, Clouds, and the PBL (Planetary Boundary Layer)
Purpose and benefits: Understanding aerosol and cloud conditions can enhance analysis of the Earth's climate system and this work uses ML to enhance signal to noise ratios, detect atmospheric features, and more.
Topic area: Science
Development stage: Pre-deployment