SAM #47QACA26K0718

AI Native Complaint Intake and Case Analysis Solution for OCR

This procurement opportunity is for the U.S. Department of Education's Office for Civil Rights (OCR), seeking a commercially available AI-native complaint intake and case analysis solution. - The solution will automate intake, deduplication, and categorization of over 40,000 pending civil rights complaints, with an ongoing influx of about 2,000 new complaints monthly. - Kaizen Laboratories Inc. is identified as the OEM and provider of the solution. - Deployment must occur within the Department's cloud security perimeter and be fully operational within 90 days of award. - The solution must meet FISMA Moderate, FERPA, and Privacy Act compliance requirements. - Human-in-the-loop AI classification is required, with high precision and recall standards. - The contract period of performance is 150 calendar days, with a 30-day transition-out option at the Government's discretion. - No competing OEMs are named, but other AI software vendors with similar capabilities could potentially compete. - The procurement focuses on replacing manual complaint processing with AI-driven automation to improve efficiency and accuracy.

Description

OCR enforces federal civil rights laws in the nation's schools, including Title VI, Title IX, Section 504, and the Age Discrimination Act. OCR began calendar year 2025 with approximately 19,000 pending complaints and received a record 23,879 new complaints during 2025, while resolving approximately one percent of its pending inventory. The pending inventory now exceeds 40,000 cases and grows by approximately 2,000 complaints per month. Intake, deduplication, and categorization are performed manually, contributing to processing times that substantially exceed the case processing benchmarks established in the OCR Case Processing Manual. The Government requires a commercially available, AI-native complaint intake and case analysis solution, configured and deployed into production, that automates intake, deduplication, and categorization of the existing backlog and of future public complaints.

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