Independent per-regulation testing of how AI models hallucinate on regulatory questions. Each white paper pairs the model's answer with the authenticated regulator text that contradicts it, and verifies every source the model cited — separating real citations from fabricated, pretextual, or contradictory ones.
Per-regulation diagnostics on how specific AI models hallucinate on each rule — grounded in authenticated primary sources by the RLB Specialist Panel.
Four partner tracks, each with its own deep-dive playbook: AI Labs (evaluate your model), Banks & Financial Institutions (board adoption), Regulators (right of reply at scale), Licensed Practitioners (verify AI before client work). Plus the Beyond regulation methodology extension.
Public findings carry executive summaries, per-question contrasts, and cited-source verifications. Partner engagements add the Panel's per-finding root-cause analysis, dominant-mode profile of the model's failures, and full substrate context behind each finding — surfaced through Panel-led review of the partner's own AI model.
The methodology generalises to any critical-accuracy domain with an authoritative primary source: medical guidelines, tax authority guidance, court precedent, building codes, aviation safety, cybersecurity frameworks, drug interaction databases, clinical trial protocols.