You bought a legal AI. Now prove it.
Verify-Your-Legal-AI sits behind your drafter, re-checks every citation and every clause reading it produced against the actual source, and hands you a signed disagreement report before anything reaches a client or a court. Not a green checkmark you have to trust. Proof you can hand a judge.
Book a pilot See a sample receipt
THE PROBLEM
A firm adopts a legal AI, and then quietly discovers the hard part: it cannot trust the output. A cited case might not exist, it might have been reversed or overruled, it might be real but cited for a proposition it never stood for, and a clause reading might rest on words that are not actually in the contract. The AI will tell you all of it with the same confident tone. Someone still has to prove each item before it ships, and courts have stopped taking your word for it. More than 113 standing orders now require per-citation certification of AI-assisted filings, judges are issuing sanctions (observed to about $95,000) for hallucinated cites that got filed, and the FTC's ban on DoNotPay made clear that unverified legal AI is an enforcement target, not a gray area. Eighty-seven percent of general counsel use AI, but only thirty-one percent have scaled it, and when you ask them why, the answer is always trust.
HOW IT WORKS
Verify-Your-Legal-AI is an independent auditor that sits behind your legal AI, it does not replace it and it does not care which AI produced the output. For every citation, it runs the actual research legs and grounds each item against the source: it parses the cite on Westlaw, extracts the real cite set from the brief on CourtListener, confirms the case exists on Fastcase, and Shepardizes it on Lexis Advance to prove it is still good law. For every clause reading, it reads the source contract text and checks that the AI's supporting quote is actually in it. Then a deterministic, model-free judge grades the AI's output against that ground truth. No second AI grading the first one, because grading an AI with an AI is just theater with more steps. Where a check is decisive, you get a verdict. Where an item cannot be resolved, it fails closed and flags it rather than passing it, because the item it could not verify is exactly the one you do not want it to wave through.
WHAT YOU GET (the receipt)
For every citation and every clause reading, one line an opposing counsel and a judge cannot wave away: - VERIFIED, with the sources checked, or - the failure kind (invented citation, dead law, wrong reporter or pincite, miscited holding, ungrounded clause reading, unverifiable), - the correct value, where one exists, and - the exposure, the sanction risk that item would have carried if it had shipped.
A per-item disagreement report you can certify from, not a dashboard you have to believe.
WHO IT'S FOR
Litigation and legal-ops teams running an AI drafter they do not fully trust, in-house counsel piloting legal AI under a no-hallucination mandate, and legal-AI vendors who need to certify their own output to sell into risk-averse firms. If an invented citation or an ungrounded clause reading from a tool on your desk would end up in front of a judge who is now sanctioning for exactly that, this is for you.
PRICING (the ladder)
- Pilot, a fixed-price paid proof, we audit your legal AI's output on your own briefs and you see the receipts before you commit to anything. - Value-metered, per brief or per verified item, once the pilot proves the catch rate on your AI's output. - Platform, Verify-Your-Legal-AI wired behind the legal AI in your filing workflow, so an uncertified citation or an ungrounded clause reading cannot ship. - Vendor attestation, a legal-AI vendor embeds the auditor to certify its own output as a trust badge (later, once the wedge is proven).
IP is licensed, never assigned. The auditor stays ours, the certainty is yours.
THE PROOF (dogfood)
We run this discipline on ourselves. Verify-Your-Legal-AI is the same verifier-first architecture that already powers AYA's regulatory pilot, an independent checker that sits behind a producer and reports where it is wrong with the correct answer as a receipt, retargeted from regulatory walks onto legal citations and clause readings. The regulatory version is backed by a deterministic judge (regulatory_walk_verifier.py), and this one is backed by its sibling (legal_output_verifier.py), the same idea in the same shape.
Honest status, today: the judge is built and it runs. In this build we pointed it at a synthetic case, an AI that cited Roe as good law (caught, dead law), Brown cited with a quote that is not in the opinion (caught, miscited holding), a citation to a reporter that does not exist (caught, unverifiable), a contract-clause reading resting on language that is not in the contract (caught, ungrounded), a genuinely grounded cite and clause (passed), and empty or malformed input (failed closed, never a silent pass). Deterministic, model-free, repeatable. What is not done yet: live runs on your briefs need your own Westlaw, Fastcase, and Lexis credentials wired in (you keep your keys) and the auditor connected on a running mesh, which is the pilot setup, not a rebuild. No live legal API was called to write this page.
First design partner slot open, a litigation or legal-ops team running an AI drafter and feeling the per-citation-certification pressure. case study to follow
HONEST NOTE
We would rather under-promise. Where the research legs give a decisive answer, the auditor certifies it against the source. Where they do not, it tells you the item is unverifiable and stops, it does not manufacture a green check to look complete, and it never grades one AI's work with another AI's opinion. That refusal to fake certainty on something a court will read is the entire product.
Book a pilot
*This page is a specification. The capability it describes is not built yet, and nothing here is a claim that it runs today.*