PHIS_Predictive_Analytics_RFI.pdf
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- PHIS Predictive Analytics
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- FSIS-FY26-0003
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This is a Request for Information (RFI) issued by the Food Safety and Inspection Service (FSIS) seeking information on AI-powered predictive analytics solutions for food safety operations. The RFI Number is FSIS-FY26-0003, issued February 12, 2026, with responses due by March 14, 2026. FSIS seeks to modernize its current numeric risk scoring and manual analysis processes by incorporating AI-driven risk scoring, large language models (LLMs), and external risk indicators to enhance inspection prioritization and resource allocation. The ideal solution should leverage a vendor's existing commercial software platform, preferably on Azure Government Cloud, with customization and data pathway enhancements rather than custom development from scratch.
Respondents should address risk scoring and analytics capabilities for dynamic scoring using structured and unstructured data; integration of external data sources such as weather, illness trends, and recall history; transparency features including explainable AI and confidence indicators; sentiment and contextual analysis of inspection narratives; pattern detection across establishments; predictive resource planning and scenario simulation; role-specific dashboards for supervisory, analytical, and operational users; conversational AI assistants; mobile field applications; governance and security measures; detailed cost breakdowns; implementation timelines with milestones; training programs; and scalability for varying data volumes. Responses must be submitted electronically in PDF format to George.Baptist@usda.gov and Monika.Masei@usda.gov and should include company name and point of contact, a one-page executive summary, detailed responses not exceeding ten pages, optional white papers and case studies, and relevant experience documentation. The RFI is for planning purposes only and does not constitute a solicitation or obligation; no compensation will be provided for responses.
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PHIS Predictive Analytics Request for Information (RFI)
RFI Number: FSIS-FY26-0003
Issue Date: February 12, 2026
Response Due Date: March 14, 2026
1. Purpose 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.
2. Background 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.
3. Objectives The primary objective of this RFI is to explore solutions that:
- Incorporate AI-driven risk scoring integrating structured data and narrative-based signals.
- Enable predictive resource planning, scenario simulation, and real-time alerts.
- Provide role-specific dashboards and conversational AI tools for supervisory, analytical, and operational users.
- Ensure transparency and governance through explainable AI and audit mechanisms.
4. Scope of Interest 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.
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.
Risk Scoring & Analytics:
- Describe capabilities for generating dynamic risk scores using structured and unstructured data.
- Explain integration of external data sources (e.g., weather, illness trends, recall history).
- Provide details on transparency features (e.g., explainable AI, confidence indicators).
Advanced Analytical Features:
- Sentiment/contextual analysis on inspection narratives and complaints.
- Pattern detection for recurring issues across establishments.
- Predictive resource planning and scenario simulation.
User Interfaces & Tools:
- Role-specific dashboards (supervisory, analytical, operational).
- Conversational AI assistants for natural language queries.
- Mobile and field applications for inspectors.
Governance & Security:
- Audit logs, user permissions, and feedback loops.
- Data security measures (encryption, access control, compliance).
Cost Estimates:
- Provide a detailed breakdown of costs (development, deployment, maintenance).
- Discuss ROI and cost-saving benefits.
Implementation Timeline:
- Outline proposed timeline for deployment, including milestones for assessment, testing, and full implementation.
Training & Support:
- Describe training programs for FSIS personnel.
- Include ongoing support and technical assistance.
Scalability & Integration:
- Explain scalability for varying volumes of data and establishments.
- Discuss integration with FSIS systems and Azure Government Cloud (preferred environment).
5. Requested Information Respondents are encouraged to provide:
- Detailed information on proposed predictive analytics solutions.
- Case studies and past performance.
- Cost models or pricing structures.
- Government FTE time estimates for support and feedback.
- Recommendations for Key Performance Indicators.
- Potential implementation barriers.
6. Submission Instructions Responses should be submitted electronically in PDF format to:
George.Baptist@usda.gov and Monika.Masei@usda.gov
Due Date: March 14, 2026
Email Subject: RFI Number FSIS-FY26-0003 – PHIS Predictive Analytics
Include:
- Company name and point of contact.
- Executive summary (1 page max).
- Detailed responses (10 pages max).
- Optional: White papers, case studies, product brochures.
- Relevant experience.
7. Disclaimer This RFI is for planning purposes only and does not constitute a solicitation or obligation. No compensation will be provided for responses.
File details come from the government source that posted it. Updated .