PHIS Predictive Analytics

Description

{"description":" 1. Purpose \n\n The Food Safety and Inspection Service (FSIS) is seeking information on AI-powered predictive analytics solutions to enhance its ability to prioritize inspections, allocate resources, and oversee food safety operations using inspection data, large language models (LLMs), and external risk indicators. This RFI aims to gather insights from industry experts and technology providers to understand the current state of predictive analytics and its potential applications in food safety. \n\n 2. Background \n\n FSIS currently relies on numeric risk scores and manual analysis for prioritization, which limits agility and predictive capabilities. By leveraging advanced AI technologies, FSIS seeks to modernize its decision-making processes, improve food safety outcomes, and optimize operational efficiency. \n\n 3. Objectives \n\n The primary objective of this RFI is to explore solutions that: \n- Incorporate AI-driven risk scoring integrating structured data and narrative-based signals. \n- Enable predictive resource planning, scenario simulation, and real-time alerts. \n- Provide role-specific dashboards and conversational AI tools for supervisory, analytical, and operational users. \n- Ensure transparency and governance through explainable AI and audit mechanisms. \n\n 4. Scope of Interest \n\n Respondents should address as many of the following areas as possible. You may include additional information beyond what is requested if it is material to the RFI. \n\n FSIS is not asking for the development of AI software from scratch. The ideal solution will take a vendor's existing commercial software platform, preferably on the Azure Government Cloud, and have data path, enhancements and customization that can be done to the existing software platform. \n\n Risk Scoring & Analytics: \n- Describe capabilities for generating dynamic risk scores using structured and unstructured data. \n- Explain integration of external data sources (e.g., weather, illness trends, recall history). \n- Provide details on transparency features (e.g., explainable AI, confidence indicators). \n\n Advanced Analytical Features: \n- Sentiment/contextual analysis on inspection narratives and complaints. \n- Pattern detection for recurring issues across establishments. \n- Predictive resource planning and scenario simulation. \n\n User Interfaces & Tools: \n- Role-specific dashboards (supervisory, analytical, operational). \n- Conversational AI assistants for natural language queries. \n- Mobile and field applications for inspectors. \n\n Governance & Security: \n- Audit logs, user permissions, and feedback loops. \n- Data security measures (encryption, access control, compliance). \n\n Cost Estimates: \n- Provide a detailed breakdown of costs (development, deployment, maintenance). \n- Discuss ROI and cost-saving benefits. \n\n Implementation Timeline: \n- Outline proposed timeline for deployment, including milestones for assessment, testing, and full implementation. \n\n Training & Support: \n- Describe training programs for FSIS personnel. \n- Include ongoing support and technical assistance. \n\n Scalability & Integration: \n- Explain scalability for varying volumes of data and establishments. \n- Discuss integration with FSIS systems and Azure Government Cloud (preferred environment). \n\n 5. Requested Information \n\n Respondents are encouraged to provide: \n- Detailed information on proposed predictive analytics solutions. \n- Case studies and past performance. \n- Cost models or pricing structures. \n- Government FTE time estimates for support and feedback. \n- Recommendations for Key Performance Indicators. \n- Potential implementation barriers. \n\n 6. Submission Instructions \n\n Responses should be submitted electronically in PDF format to: \nGeorge.Baptist@usda.gov and Monika.Masei@usda.gov \nDue Date: March 14, 2026 \nEmail Subject: RFI Number FSIS-FY26-0003 – PHIS Predictive Analytics \nInclude: \n- Company name and point of contact. \n- Executive summary (1 page max). \n- Detailed responses (10 pages max). \n- Optional: White papers, case studies, product brochures. \n- Relevant experience. \n\n 7. Disclaimer \n\n This RFI is for planning purposes only and does not constitute a solicitation or obligation. No compensation will be provided for responses. \n"} Solicitation Number: FSIS-FY26-0003 Type: Sources Sought Base Type: Sources Sought NAICS: 541511 Classification Code: DA10 Response Deadline: 2026-03-14T17:00:00-04:00 Office Address: BELTSVILLE, MD Place of Performance: Beltsville, Maryland POC: Monika Masei, monika.masei@usda.gov POC: George Baptist, george.baptist@usda.gov {"description":" 1. Purpose \n\n The Food Safety and Inspection Service (FSIS) is seeking information on AI-powered predictive analytics solutions to enhance its ability to prioritize inspections, allocate resources, and oversee food safety operations using inspection data, large language models (LLMs), and external risk indicators. This RFI aims to gather insights from industry experts and technology providers to understand the current state of predictive analytics and its potential applications in food safety. \n\n 2. Background \n\n FSIS currently relies on numeric risk scores and manual analysis for prioritization, which limits agility and predictive capabilities. By leveraging advanced AI technologies, FSIS seeks to modernize its decision-making processes, improve food safety outcomes, and optimize operational efficiency. \n\n 3. Objectives \n\n The primary objective of this RFI is to explore solutions that: \n- Incorporate AI-driven risk scoring integrating structured data and narrative-based signals. \n- Enable predictive resource planning, scenario simulation, and real-time alerts. \n- Provide role-specific dashboards and conversational AI tools for supervisory, analytical, and operational users. \n- Ensure transparency and governance through explainable AI and audit mechanisms. \n\n 4. Scope of Interest \n\n Respondents should address as many of the following areas as possible. You may include additional information beyond what is requested if it is material to the RFI. \n\n FSIS is not asking for the development of AI software from scratch. The ideal solution will take a vendor's existing commercial software platform, preferably on the Azure Government Cloud, and have data path, enhancements and customization that can be done to the existing software platform. \n\n Risk Scoring & Analytics: \n- Describe capabilities for generating dynamic risk scores using structured and unstructured data. \n- Explain integration of external data sources (e.g., weather, illness trends, recall history). \n- Provide details on transparency features (e.g., explainable AI, confidence indicators). \n\n Advanced Analytical Features: \n- Sentiment/contextual analysis on inspection narratives and complaints. \n- Pattern detection for recurring issues across establishments. \n- Predictive resource planning and scenario simulation. \n\n User Interfaces & Tools: \n- Role-specific dashboards (supervisory, analytical, operational). \n- Conversational AI assistants for natural language queries. \n- Mobile and field applications for inspectors. \n\n Governance & Security: \n- Audit logs, user permissions, and feedback loops. \n- Data security measures (encryption, access control, compliance). \n\n Cost Estimates: \n- Provide a detailed breakdown of costs (development, deployment, maintenance). \n- Discuss ROI and cost-saving benefits. \n\n Implementation Timeline: \n- Outline proposed timeline for deployment, including milestones for assessment, testing, and full implementation. \n\n Training & Support: \n- Describe training programs for FSIS personnel. \n- Include ongoing support and technical assistance. \n\n Scalability & Integration: \n- Explain scalability for varying volumes of data and establishments. \n- Discuss integration with FSIS systems and Azure Government Cloud (preferred environment). \n\n 5. Requested Information \n\n Respondents are encouraged to provide: \n- Detailed information on proposed predictive analytics solutions. \n- Case studies and past performance. \n- Cost models or pricing structures. \n- Government FTE time estimates for support and feedback. \n- Recommendations for Key Performance Indicators. \n- Potential implementation barriers. \n\n 6. Submission Instructions \n\n Responses should be submitted electronically in PDF format to: \nGeorge.Baptist@usda.gov and Monika.Masei@usda.gov \nDue Date: March 14, 2026 \nEmail Subject: RFI Number FSIS-FY26-0003 – PHIS Predictive Analytics \nInclude: \n- Company name and point of contact. \n- Executive summary (1 page max). \n- Detailed responses (10 pages max). \n- Optional: White papers, case studies, product brochures. \n- Relevant experience. \n\n 7. Disclaimer \n\n This RFI is for planning purposes only and does not constitute a solicitation or obligation. No compensation will be provided for responses. \n"}