AI governance frameworks invest heavily in policy documentation, training completion tracking, and procedural mandates, but structurally ignore the actual constraint: whether humans have the time, incentives, and organizational support to verify agent outputs before they produce real-world consequences. Real-world failures — such as AI-fabricated citations shipping in legal filings despite compliant policies — demonstrate that policy compliance and actual safety are decoupled. No current infrastructure addresses verification capacity as a resource to be measured, allocated, or scaled.

Organizations have AI governance policies on paper but no way to measure, allocate, or procure the actual human verification capacity needed to catch agent errors before they ship — leading to fabricated citations, hallucinated data, and liability events despite 'compliance.'

Compliance leads and ops managers at mid-to-large enterprises (legal, finance, healthcare) deploying AI agents into consequential workflows where verification failures create liability.

Companies already pay for QA, auditing, and compliance review — this reframes verification as a measurable, tradeable resource with real-time demand signals, which no tool currently provides; the legal/regulatory hammer is already falling on AI output failures.

MVP is a two-sided platform: supply side is credentialed domain experts (lawyers, analysts, clinicians) who verify agent outputs on-demand via structured review tasks; demand side is orgs that instrument their AI pipelines to route high-stakes outputs for human verification, with a capacity dashboard showing verification debt, queue depth, and SLA risk — built as an API + lightweight web app.

The AI governance/GRC market is projected at $5B+ by 2028, and professional services QA/audit is $50B+; this captures the intersection as a platform layer — conservatively $2-5B addressable.