PHIS Predictive Analytics
Closed Pre-Solicitation Posted
- Solicitation number
- FSIS-FY26-0003
- Agency
-
Food Safety and Inspection Service
Department of Agriculture
- Responses due
- Set-aside
- No set-aside
Opportunity facts
- NAICS code
- 541511 Custom Computer Programming Services
- Place of performance
- Beltsville, Maryland, United States
- Points of contact
Notice details come from SAM.gov. Updated .
About this opportunity
The Food Safety and Inspection Service (FSIS), a division of the U.S. Department of Agriculture, is seeking information on AI-powered predictive analytics solutions to modernize its food safety inspection operations. The procured solution must incorporate AI-driven risk scoring that integrates both structured and unstructured data, leverage large language models and external risk indicators, and provide role-specific dashboards with conversational AI tools for supervisory, analytical, and operational users. Key objectives include enabling dynamic risk scoring, integrating external data sources such as weather and illness trends, ensuring transparency through explainable AI mechanisms, and supporting predictive resource planning and scenario simulation capabilities. The ideal solution should build upon an existing commercial software platform, preferably deployed on Azure Government Cloud, rather than requiring custom development from scratch. Responses to this Request for Information (RFI Number FSIS-FY26-0003) are due by March 14, 2026, and should be submitted electronically in PDF format to George.Baptist@usda.gov and Monika.Masei@usda.gov.
This is a pre-solicitation notice with no set-aside designation, and no incumbent contractor or award value has been identified. The RFI is issued for planning and market research purposes only and does not constitute a formal solicitation or obligation to award a contract; respondents will not receive compensation for their submissions. Interested parties should submit responses including company information, a one-page executive summary, detailed responses not exceeding ten pages, and optional white papers or case studies demonstrating relevant experience. The submission should address risk scoring capabilities, external data integration, transparency features, sentiment analysis of inspection narratives, pattern detection, resource planning tools, mobile applications, governance and security measures, detailed cost breakdowns, implementation timelines, and scalability considerations. Upon completion of this RFI phase, FSIS anticipates moving toward a formal solicitation with specific contract deliverables due five to ten business days after contract award.
Notice text
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.
Files
| File | Type | Posted |
|---|---|---|
| SOO_PHIS_AI.pdf | ||
| PHIS_Predictive_Analytics_RFI.pdf |
On GovTribe
Work this opportunity
- Track it in your pipeline
- Find teaming partners
- Similar opportunities
- Ask GovTribe AI about this opportunity