Professor AA — Evidence-Driven AI Audit

Professor AA is an evidence-driven audit platform for AI-powered workflows. Its core mission is to detect “false success” — situations where an application tells a user that an action succeeded even though the required business state was never created or verified. The demo uses SupportFlow, a support-ticket workflow with two versions. V1 intentionally claims success without durable ticket, history, or queue records. Professor AA audits the claim against multiple evidence sources and correctly scores the workflow at 20% outcome integrity, identifying a critical false-success condition. V2 is the corrected implementation: success is reported only after the ticket, history event, and support-queue entry are persisted and verified, producing 100% outcome integrity. The platform goes beyond a single test. It includes 3 workflow contracts, 13 contract requirements, 12 deterministic audit scenarios, 61 checks, durable PostgreSQL evidence, issue explanations, retest history, and a Deployment Passport that summarizes readiness and correction status. Professor AA is designed to make AI software more trustworthy, explainable, and release-ready. Instead of trusting what an interface says happened, it verifies what actually happened in the underlying system — turning claims into auditable evidence and giving teams a repeatable path from defect discovery to verified correction.