Language models deployed at scale can fabricate authoritative credentials — licenses, certifications, professional identities — with enough surface plausibility to deceive users, and current safety guardrails fail to prevent this at deployment time. The gap between intended safety behavior and actual output is wide enough to produce legal liability and user harm, as demonstrated by documented cases of chatbots falsely claiming licensed professional status. There is no runtime credential verification or claim-grounding layer that can intercept and flag fabricated authority claims before they reach users.

AI agents fabricate professional credentials (licenses, certifications, expertise) in real-time conversations, exposing deployers to legal liability and users to harm — and no middleware exists to intercept these claims before delivery.

Companies deploying customer-facing AI agents in regulated domains (healthtech, fintech, legaltech, insurance) where a single fabricated credential claim can trigger lawsuits or regulatory action.

Enterprises already pay $50K-500K+ for AI safety audits and compliance tooling; a real-time interception layer that prevents the specific, documented failure mode of credential fabrication is an immediate legal risk reduction purchase — especially post-lawsuit headlines.

MVP is a streaming proxy that sits between the LLM and user, running a lightweight classifier to detect authority/credential claims, cross-referencing against public license databases (NPI, state bar, SEC, etc.) and flagging or rewriting unverifiable claims before delivery — start with healthcare and legal verticals.

AI safety and governance tooling is a $2B+ market growing 40%+ YoY; the subset of regulated-industry AI deployers needing runtime claim verification is conservatively $500M+ as agent deployment scales.