AI is on the examination table.
What evidence will you hand the examiner?
An exam doesn't ask whether you have an AI policy. It asks: show us. Insurance already verifies everything consequential through an outside party — reserves, models, financials. We produce that class of evidence for AI outcomes.
The scrutiny is already scheduled
Multistate exam
A coordinated, multistate regulatory examination of insurers' AI practices is underway now, running through September 2026.
NAIC bulletin
The NAIC model bulletin on insurers' use of AI — adopted across roughly two dozen states — expects governance, vendor oversight, and validation of AI-driven decisions.
Old doctrine
Independent validation is already insurance doctrine: actuarial opinions on reserves, model validation, audited financials. AI is the newest entry on that list, not an exception to it.
"The vendor's dashboard says it works" has never satisfied an examiner in any of those domains. The question isn't whether AI-touched outcomes get verified — it's who is structurally allowed to verify them.
Evidence produced by a party with no stake in the answer
An independent, reproducible verdict on whether AI-touched outcomes were actually right and what they truly cost — with fault attributed to a specific party: data, model-vendor, operator, or handoff. Same inputs, same method, same answer, re-runnable by anyone who doesn't trust us.
Lane 1 · Your own AI
Examiner-ready evidence for the AI you deploy
Claims triage, fraud scoring, customer resolution — wherever AI touches an outcome a regulator can question. The verdict shows the outcome was measured by an outside party, the measurement is reproducible, and failure is attributed to a nameable cause. That's the shape of evidence exams are built to accept.
Lane 2 · AI you underwrite
Underwriting evidence for AI performance risk
Tech E&O and AI-performance exposure are being priced today largely on the insured's own representations. An independent verdict replaces self-reported performance with verified performance — and fault attribution maps a loss to the party that actually caused it: the data, the model-vendor, the operator, or the handoff between them. That's the boundary a liability question turns on.
Engagement outputs are produced from your data under written terms. Illustrative examples on this site are labeled and conditional until validated. See what a verdict looks like →
Why our evidence holds up where a vendor's can't
Insurance learned this lesson before anyone: the 2008 ratings failed because the rated party paid for — and could shop — its own rating. We built the opposite structure, and made it one you can hold us to.
Buyer-paid, not issuer-paid
The party that consumes the verdict pays for it. Our fee is the same whether the answer is yes or no.
No build, no operate
We hold no equity, board seat, or advisory fee from any party we measure. There is nothing for us to defend by softening a result.
Reproducible verdict
A method, not an opinion — re-runnable by your team, your auditor, or your examiner.
The same independence test regulators apply elsewhere — EU AI Act Art. 31 for conformity bodies, SEC/PCAOB auditor rules — is the test we built to. The full independence structure →
Start with one book, one use case
A fixed-fee First-Verdict Diagnostic on a single AI-touched process — an independent, reproducible verdict in 30–60 days, credited in full toward annual verification if you continue.
A findings call is 30 minutes: what the exam-shaped evidence looks like on your data, and what a diagnostic would scope. No deck, no drip campaign.
Core methods patent-pending. De-identified inputs; no policyholder PII required to start.