AI Inventory
For inquiries about these AI use cases only, please contact the Responsible AI Official at raio@gsa.gov. Unrelated inquiries sent to this address will not receive a response.
| Use Case Name | Summary of Use Case | Stage of System Development Life Cycle |
|---|---|---|
| Solicitation Review Tool (SRT) | The SRT intakes SAM.gov data for all Information and Communications Technology (ICT) solicitations. The system then compiles the data into a database to be used by machine learning algorithms. The first of these is a Natural Language Processing model that determines if a solicitation contains compliance language. If a solicitation does not have compliance language, then it is marked as non-compliant. Each agency is asked to review their data and validate the SRT predictions. GSA also conducts random manual reviews monthly. | Operation and Maintenance |
| Acquisition Analytics | Takes Detailed Data on transactions and classifies each transaction within the Government-wide Category Management Taxonomy | Operation and Maintenance |
| City Pairs Program Ticket Forecast and Scenario Analysis Tools | Takes segment-level City Pair Program air travel purchase data and creates near-term forecasts for the current and upcoming fiscal year by month and at various levels of granularity including DOD vs Civilian, Agency, and Region. | Development and Acquisition |
| Category Taxonomy Refinement Using NLP | Uses token extraction from product descriptions more accurately shape intended markets for Product Service Codes (PSCs). | Operation and Maintenance |
| Key KPI Forecasts for GWCM | Takes monthly historical data for underlying components used to calculate KPIs and creates near-term forecasts for the upcoming fiscal year. Pilot effort focuses on total agency/category spend (the denominator in multiple KPIs). If the pilot program is successful, the same methodology can be extended to other KPIs. | Implementation |
| Service Desk Generic Ticket Classification | We are building a model to take generic Service Desk tickets and classify them so that they can be automatically re-routed to the correct team that handles these types of tickets. The process of re-routing generic tickets is currently done manually, so the model will allow us to automate it. The initial model will target the top 5 most common ticket types. | Implementation |
| Service Desk Virtual Agent (Curie) | Virtual agent that uses ML to provide predictive results for chat entries. A natural language chatbot (virtual assistant), we named Curie, as part of a multi-model customer service experience for employee's IT service requests leveraging knowledge-based articles. | Operation and Maintenance |
| Contract Acquisition Lifecycle Intelligence (CALI) | CALI tool is an automated machine learning evaluation tool built to streamline the evaluation of vendor proposals against the solicitation requirements to support the Source Selection process. Once the Contracting Officer (CO) has received vendor proposals for a solicitation and is ready to perform the evaluation process, the CO will initiate evaluation by sending solicitation documents along with all associated vendor proposal documents to the Source Selection module, which will pass all documents to CALI. CALI will process the documents, associated metadata and begin analyzing the proposals in four key areas: format compliance, forms validation, reps & certs compliance, and requirements compliance. The designated evaluation members can review the evaluation results in CALI and submit finalized evaluation results back to the Source Selection module. CALI is currently being trained with sample data from the EULAs under the Multiple Award Schedule (MAS) program. | Implementation |
| Classifying Qualitative Data | USAGov and USAGov en Español collect large amounts of qualitative data from survey comments, web searches and call center chat transcripts. Comments are grouped together by topic to determine where we need to make product updates/enhancements | Operation and Maintenance |
| Chatbot for Federal Acquisition Community | The introduction of a chatbot will enable the GSA FAS NCSC (National Customer Support Center) to streamline the customer experience process, and automate providing answers to documented commonly asked questions through public facing knowledge articles. The end goal is this will reduce staffing requirements for NCSC’s live chat programs and allow the NCSC resources to be dedicated to other proactive customer services initiatives. Customers will still have the option to connect to a live agent if they choose by requesting an agent. | Operation and Maintenance |
| Document Workflow / Intelligent Data Capture and Extraction | GSA is driving towards a more accurate and scalable document workflow platform. GSA seeks to intelligently capture, classify, and transfer critical data from unstructured and structured documents, namely PDF files, to the right process, workflow, or decision engine. | Operation and Maintenance |
| IAE FSD CCAI Virtual Agent | The virtual agent uses manual learning to understand customer needs and provide a response appropriately. Our AI is named SAM and uses natural language. | Operation and Maintenance |