PHIS Predictive Analytics

Request for Information (RFI)

This RFI seeks information on AI-powered predictive analytics solutions to enhance the Food Safety and Inspection Service (FSIS) ability to prioritize inspections, allocate resources, and oversee food safety operations. The goal is to gather insights from industry experts on the current state of predictive analytics and its applications in food safety, moving beyond FSIS's current reliance on numeric risk scores and manual analysis.

FSIS is interested in solutions that can:

• Incorporate AI-driven risk scoring using structured and unstructured data.
• Integrate external data sources (e.g., weather, illness trends, recall history).
• Provide transparency features (e.g., explainable AI).
• Perform sentiment/contextual analysis on inspection narratives and complaints.
• Detect patterns for recurring issues across establishments.
• Enable predictive resource planning and scenario simulation.
• Offer role-specific dashboards and conversational AI tools.
• Provide mobile and field applications for inspectors.
• Ensure governance and security through audit logs, user permissions, and data security measures.

FSIS is not seeking development from scratch but rather an existing commercial software platform, preferably on Azure Government Cloud, with data path, enhancements, and customization capabilities.

• Submission Deadline: March 14, 2026
• Format: Electronic PDF
• Submission Email: George.Baptist@usda.gov and Monika.Masei@usda.gov
• Email Subject: RFI Number FSIS-FY26-0003 – PHIS Predictive Analytics
• Required Content: Company name/POC, Executive summary (1 page max), Detailed responses (10 pages max).
• Optional Content: White papers, case studies, product brochures, relevant experience.

This RFI is for planning purposes only and does not constitute a solicitation or obligation. No compensation will be provided for responses.

Statement of Objectives (SOO)

To design and implement an AI-powered decision-support platform that transforms the Food Safety and Inspection Service's (FSIS) ability to prioritize inspections, allocate resources, and oversee food safety operations using inspection data, Large Language Models (LLMs), and external risk indicators.

The platform will incorporate AI-driven risk scoring integrating structured and narrative-based signals, and external data sources (weather, illness trends, recall history). It will provide role-specific dashboards and conversational AI tools for users, enable predictive resource planning and scenario simulation, and ensure transparency through explainable AI and audit mechanisms. Feedback from business SMEs will be incorporated to refine scoring decisions.

• Develop an AI-Driven Risk Prioritization Engine with LLM-based narrative analysis.

• Integrate external data sources to dynamically adjust risk scores.

• Provide transparent rationale and data provenance for AI recommendations.

• Implement sentiment and contextual analysis on inspection narratives.

• Deploy pattern detection algorithms.

• Incorporate predictive resource planning and scenario simulation.

• Create role-specific user interfaces and tools, including dashboards and conversational AI assistants.

• Enhance operational efficiency and collaboration through AI-powered chatbots and cross-role collaboration features.

• Support mobile and field applications for inspectors.

• Ensure trust, transparency, and governance through model confidence indicators, explainable AI panels, and audit logs.

• AI Risk Prioritization Engine: Within 5 business days after contract award.

• Role-Specific Dashboards: Within 10 business days after contract award.

• Conversational AI Assistant: Within 10 business days after contract award.

• Scenario Simulation Tools: Within 10 business days after contract award.

• Governance Framework: Within 10 business days after contract award.

TBD

The preferred deployment environment is Azure Government Cloud. Proposals using different environments must include additional hosting cost estimates.