PHIS Predictive Analytics

2026-11-03

1 . PurposeThe 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 . BackgroundFSIS 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 . ObjectivesThe 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 InterestRespondents 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 InformationRespondents 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 InstructionsResponses should be submitted electronically in PDF format to : George . Baptist @ usda . gov and Monika . Masei @ usda . govDue Date : March 14 , 2026Email Subject : RFI Number FSIS - FY26 - 0003 – PHIS Predictive AnalyticsInclude :- 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 . DisclaimerThis RFI is for planning purposes only and does not constitute a solicitation or obligation . No compensation will be provided for responses .

Maryland

2026-05-02