AI Camera Technology

This is being updated to reflect that the Reverse Industry Day is going to be held on 26 January 2026. Interested vendors submission date has been extended through 18 February 2026. In addition, this can be held virtually via teams for some portions. 

A tentative agenda will be submitted once finalized.

Title: AI Camera Technology
RFI Number: FSIS-FY26-0002
Issue Date:29 January 2026
Response Due Date: 20 February 2026

1. Purpose

The Food Safety and Inspection Service (FSIS) is seeking information on AI Camera Technology to enhance its capabilities in detecting pathogens, contaminants, and conducting visual inspections. This RFI aims to gather insights from industry experts, technology providers, and other stakeholders to understand the current state of AI Camera Technology and its potential applications in food safety.

Responses should address as many of the areas below as possible. You may include additional information beyond was requested if it is material to the RFI.

2. Background

The Poultry Products Inspection Act requires that “the Secretary of Agriculture, whenever processing operations are being conducted, shall cause to be made by inspectors’ postmortem inspection of the carcass of each bird processed . . .” (21 U.S.C. 455(b)). FSIS employs trained inspectors who perform online carcass-by-carcass inspection to meet this requirement.

FSIS is considering using technology, such as cameras and associated software to assist FSIS inspectors in identifying contamination or disease conditions on poultry carcasses, provided the use of such technology is effective and does not compromise food safety.

3. Objectives

The primary objective of this RFI is to explore AI Camera Technology solutions that can assist FSIS in improving food safety by accurately detecting pathogens, identifying contaminants, and performing visual inspections in real-time. The information gathered will help FSIS in developing future procurement strategies and technology implementations.

4. Scope of Interest

Requirements:

1. Pathogen Detection:

• Describe the AI Camera Technology's capability to detect various pathogens in food products.
• Provide details on the accuracy, sensitivity, and specificity of the technology in identifying pathogens.
• Include information on the types of pathogens that can be detected and any limitations.

1. Contaminant Identification:

• Explain how the AI Camera Technology can identify different types of contaminants in food products.
• Discuss the technology's effectiveness in detecting physical, chemical, and biological contaminants.
• Provide examples of contaminants that can be identified and any known challenges.

1. Visual Inspection:

• Detail the AI Camera Technology's ability to perform visual inspections of food products.
• Describe the technology's capability to identify defects, foreign objects, and other visual anomalies.
• Include information on the resolution, speed, and accuracy of the visual inspection process.

1. Cost Estimates:

• Provide a detailed breakdown of the costs associated with implementing the AI Camera Technology.
• Include equipment purchase, initial setup costs, ongoing maintenance costs, and any additional expenses.
• Discuss any cost-saving benefits or return on investment (ROI) that technology may offer.

1. Implementation Timeline:

• Outline a proposed timeline for the deployment/implementation of the AI Camera Technology in a slaughter plant.
• Include key milestones such as initial assessment, testing, full deployment, and ongoing evaluation.
• Provide an estimated duration for each phase of the implementation process.

1. Implementation Models:

• FSIS is interested in different implementation models. Describe how camera settings and results could be implemented both by a FSIS implemented model and a plant implementation model (similar to the cameras used for grading).
• Describe measure to train and calibrate the cameras. Describe how FSIS could set and monitor sensitivity limits and findings.
• Describe how FSIS inspectors can receive/interact with the results to perform inspection.

1. Data Security:

• Describe the measures in place to ensure the security and privacy of data collected by AI Camera Technology.
• Include information on data encryption, access controls, and compliance with relevant data protection regulations.
• Discuss how data integrity and confidentiality will be maintained throughout the data lifecycle.

1. Regulatory Compliance:

• Provide information on how AI Camera Technology complies with relevant food safety regulations and standards.
• Include details on any certifications or approvals the technology has received from regulatory bodies.
• Discuss how technology ensures ongoing compliance with evolving regulatory requirements.

1. Training and Support:

• Describe the training programs to be made available for FSIS personnel to effectively use AI Camera Technology.
• Include details on initial training, ongoing support, and any available resources such as manuals or online tutorials.
• Discuss the availability of technical support and maintenance services to ensure the technology operates smoothly.

1. User Feedback:

• Explain how user feedback will be collected and utilized to improve the AI Camera Technology.
• Include details on feedback mechanisms such as surveys, user interviews, and feedback forms.
• Discuss how feedback will be analyzed and integrated into future updates and enhancements of the technology.

1. Scalability:

• Describe the scalability of the AI Camera Technology to accommodate varying volumes of food products.
• Include information on how technology can be scaled up or down based on the needs of FSIS.
• Discuss any limitations or challenges associated with scaling the technology and potential solutions.

1. Integration with Existing Systems:

• Explain how the AI Camera Technology can be integrated with FSIS' existing systems and infrastructure.
• Include details on compatibility with current software, hardware, and data management systems.
• Discuss any potential challenges and solutions for seamless integration, including below:

• Provide temperature parameters camera can operate under
• Provide condensation and fog environment parameters, the camera can operate under
• Keeping the camera clean in rugged working environments solution
• Number of cameras needed to avoid blind spots and provide 3600 views
• Lighting necessary for effective operation of the camera and imaging
• Storage necessary for storing the footage
• Image quality and processing speed and throughput

 5. Requested Information

Respondents are encouraged to provide:

• Detailed information on their AI Camera Technology solutions
• Case studies
• Past performance for the proposed capabilities or technologies
• Additional relevant supporting materials
• Cost models or pricing structures
• Government FTE time to support the contract, including skill sets and availability of the FTE that is needed to provide feedback to the contractor
• Recommendations for Key Performance Indicators
• Potential implementation barrier

6. Reverse Industry Day

Purpose:
The Food Safety and Inspection Service (FSIS) is exploring advanced AI Camera Technology to enhance its ability to detect pathogens, identify contaminants, and perform real-time visual inspections in food processing environments. In alignment with FAR 15.201, which encourages early exchanges of information to improve acquisition outcomes, FSIS will host a Reverse Industry Day to gain a deeper understanding of industry capabilities, challenges, and best practices related to AI-driven imaging solutions. This event will allow technology providers and subject matter experts to present their perspectives directly to FSIS acquisition and technical teams, helping the agency shape requirements and procurement strategies that reflect current market realities and technological advancements.

Objectives:

• Understand the state of AI Camera Technology, including capabilities for pathogen detection, contaminant identification, and automated visual inspection.
• Learn about industry approaches to integrating AI with imaging hardware and software, including data processing, machine learning models, and real-time analytics.
• Identify potential barriers to adoption, such as cost drivers, infrastructure requirements, cybersecurity considerations, and regulatory compliance.
• Gather insights on performance metrics, scalability, and interoperability with existing FSIS systems.
• Use industry feedback to inform future solicitations, evaluation criteria, and acquisition strategies that encourage innovation and competition.

Format:
The Reverse Industry Day will feature presentations from industry participants to FSIS personnel, focusing on:

• Technical capabilities and limitations of AI Camera Technology.
• Implementation challenges and lessons learned from similar deployments.
• Recommendations for structuring requirements and timelines to enable successful adoption.
  A moderated Q&A session will follow to allow FSIS to clarify technical and acquisition-related questions.

Participation:
FSIS invites technology providers, AI solution developers, and other stakeholders to respond to this RFI indicating their interest in participating. FSIS intends on having an in person Reverse Industry Day. The Reverse Industry Format is intended to be engaging with FSIS Personnel and industry participants. FSIS is open to a variety of formats. Responses should include suggested topics for discussion and any unique insights that would help FSIS better understand the commercial landscape. The information gathered will be used solely for planning purposes and will not result in a contract award.

It is anticipated that the Reverse Industry Day will take place during the last week of February. More than one day may occur depending on participation. Once a date is finalized, it will be posted to this RFI. The Reverse Industry Day will take place in Beltsville, Maryland. Specific details will be sent to those participating.

7. Submission Instructions

Responses should be submitted to George.Baptist@usda.gov and monika.masei@usda.gov by 20 February 2026 electronically in PDF format. If industry participants are interested in attending the in person Reverse Industry Day, responses should be submitted to George.Baptist@usda.gov and monika.masei@usda.gov by 13 February 2026 in order to facilitate and plan for the Reverse Industry Day. Questions to this Reverse Industry Day shall be directed to monika.masei@usda.gov by 9 February 2026.

For the email subject please include: (RFI number: FSIS-FY26-0002 and name)

Include:

• Company name and POC
• Executive summary (1 page max)
• Detailed responses to sections A–E (10 pages max) including technical approach
• Optional: White papers, case studies, or product brochures
• Relevant experience
• Interest in attending the Reverse Industry Day

8. Disclaimer

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