The RLB Specialist Panel partners with four audiences whose work depends on AI getting regulatory questions right. Every finding is bound to a permanent, human-verified Citation ID that does not decay and cannot hallucinate. We engage on a services-led basis, applying the Panel's expertise to your specific need.
Last updated 14 Jun 2026
Probe your model against the verified 94-finding dataset before shipping. Each finding is failure-mode classified and bound verbatim to the primary regulator source. Collaborate on remediation. Pre-publication review of findings affecting your model. Every finding ships with a permanent RLB-H- Citation ID your evals and safety work can cite immutably.
The methodology generalises beyond regulation: medical guidelines, tax authorities, investment research, and other critical-accuracy domains your model serves. See cross-domain playbook →
AI Labs deep-dive playbook below. See the playbook ↓
Engage as AI Lab partner →Industry compliance with your administered rules is increasingly mediated by AI. And AI is structurally degrading. RegLegBrief surfaces where regulated entities are reading your rules wrong. You get a right of reply on every published finding affecting your framework, optional industry sensitisation collaboration, and cross-regulator intelligence on the rule constructions that most consistently break AI.
Regulators deep-dive playbook below. See the playbook ↓
Engage as Regulator partner →Court rulings have made AI verification a personal professional duty. RegLegBrief sits between your AI draft and the client file, verifying every AI-asserted claim against the actual primary source the regulator, court, or standards body published. Safe-AI adoption consultancy tailored to your profession. Continuous awareness of new regulations affecting your practice areas. RAG-augmented query against the RLB-curated substrate, on roadmap.
Practitioners deep-dive playbook below. See the playbook ↓
Engage as Practitioner partner →Verify AI-generated content from your own teams, AI-mediated drafts from external consultants, and outputs from the AI tools your firm relies on, all against the primary regulator source. Consultancy and training tailored to your sub-sector, anchored in the RLB Hallucination Register.
Banks & FI deep-dive playbook below (the lead case for regulated firms). See the playbook ↓
Engage as Regulated Firm partner →Each partner track has a board-ready playbook. Read your track, then engage.
The methodology — verifying AI outputs against authenticated primary sources, classifying failures across four failure modes (inference drift, misstated rule, misattributed, outdated), tagging by audience — applies to any critical-accuracy domain where authoritative sources exist and the consequences of AI misinformation are material.
| Substrate domain | Target audience for AI products |
|---|---|
| Regulatory rules | Lawyers, compliance, regulators, regulated firms (what we do now) |
| Medical guidelines (WHO, FDA, NICE) | Doctors, nurses, patients, pharma |
| Tax authorities (IRS, HMRC) | Accountants, tax advisers, individuals |
| Investment research (prospectuses, fund factsheets, SEC filings) | Advisers, retail investors, asset managers |
| Banking product T&Cs, rate sheets | Retail bankers, financial advisers |
| Drug interaction databases | Pharmacists, prescribers |
| Court precedent / case law | Lawyers, paralegals |
| Building codes, safety standards | Engineers, architects, contractors |
| Cybersecurity standards (NIST, ISO 27001) | Security teams, CISOs, IT auditors |
| Aviation safety (FAA, EASA, ICAO) | Pilots, airlines, maintenance technicians |
| Clinical trial protocols (ICH-GCP, FDA IND) | Clinical researchers, regulatory affairs |
AI products serving these audiences need to be right. We make sure they are.
Three ways to start a conversation: