Your payer's AI denied the claim. Prove it broke its own guideline.
Denial Defense re-walks the payer's own medical-necessity guideline as a decision tree, judges the payer AI's denial against it, and hands you a per-claim verdict. Either the denial follows the guideline, or it does not, and when it does not you get the specific failure and the correct answer as a receipt you can put in front of an appeals reviewer.
Verify a denial See a worked receipt
THE PROBLEM
A payer's AI denies a claim in seconds, and you are left arguing with a black box. You suspect the denial did not actually follow the plan's own medical-necessity criteria (the AI skipped a step, or leaned on a chart value that is not in the chart, or cited a criterion the guideline never carried), but suspicion is not evidence, and the appeal clock is running. This is not a rare edge case anymore. The nH Predict disclosure order in March 2026 put a named payer AI denial engine on the public record, and the independent reporting around these systems is blunt, more than ninety percent of AI-driven denials get overturned on appeal, which is another way of saying the AI is wrong most of the time it says no. The trouble is that overturning a denial today takes a human reviewer, hours, and a fight. The evidence exists inside the guideline the payer already published, nobody has just been checking the denial against it, deterministically, at the speed the denial was issued.
HOW IT WORKS
Denial Defense is the same producer-agnostic verifier AYA already uses to grade regulatory walks, pointed at the payer instead of a regulator:
- The guideline becomes a decision tree. The plan's medical-necessity criteria (an MCG or InterQual style criterion, or the plan's own coverage policy) are authored as a decision tree, one node per criterion, edges for met and not-met, leaves for covered and not covered. This is data, not code, and it is exactly how the guideline already reads. - The chart is the profile, the denial is the claim. The patient chart plus the claim is the input the tree walks over. The payer AI's denial (its determination, its criteria path, its cited criteria, its rationale) is the claim we judge, and it is never trusted, it is re-derived. - Five checks, deterministic, no model at verification time. We re-walk the guideline ourselves and compare, on the verdict, the path, the evidence each step rested on, whether an abstention was honest, and whether every cited criterion is one the guideline node actually carries. A denial that clears all five agrees with the guideline. A denial that fails one comes back with the specific failure. - The failure speaks the payer's own language. A generic verifier failure is mapped onto the insurance bad-faith vocabulary, an unreasonable denial, a failure to follow guidelines, inconsistent criteria, and so on, so the finding lands where an appeal or a complaint can use it. - Across a batch, the pattern surfaces. Single-claim verdicts feed the claims-intelligence layer, so denials that cluster on one criterion or one cohort at a rate the guideline does not justify show up as a pattern, not just a stack of individual fights.
WHAT YOU GET
Per claim, a verdict, and when the denial broke the guideline, the exact failure plus the correct guideline-grounded answer as an auditable receipt. Across a batch, the pattern of denials, where the payer AI is systematically breaking its own rules. And an appeal-ready evidence bundle, so the verdict does not just tell you the denial was wrong, it hands you what you need to say so. The verdict and the evidence travel together, which is the whole point, a conclusion without the receipt does not survive an appeal, and the receipt without the conclusion is just paper.
WHO IT'S FOR
Provider revenue-cycle and denial-management teams drowning in AI denials, appeals shops that live or die on turnaround, patient-advocacy groups fighting coverage refusals, and plaintiff-side healthcare counsel who need independent, auditable proof a denial did not follow the guideline. If you are on the receiving end of a payer AI and you need evidence rather than indignation, this is built for you.
PRICING (the ladder)
- Single-case. Verify one AI denial against the guideline, with the correct-answer receipt. - Batch-audit. A batch of denials verified, with the across-cases pattern-of-denials report. - Subscription. Continuous verification as denials arrive, with a running pattern dashboard. - Platform. The verifier wired behind your denial-management workflow, every AI denial checked before you accept it.
(Prices are hypotheses we are validating, not commitments carved in stone.)
THE PROOF (the dogfood)
This is not a new engine we are hoping works. It is the exact re-walk judge AYA already runs to verify its own agents' determinations, and the same one behind our regulatory verification wedge with a live design partner. Pointing it at a payer took no new code, we authored the guideline as a decision tree and added a small data map from the verifier's failures to the bad-faith vocabulary. So the pitch is not a claim about a clever new thing, it is the verification capability we already trust on ourselves, turned outward at the payer's AI.
HONEST NOTE
Two straight things. First, Denial Defense checks a denial against a guideline authored as a decision tree, so it is only as good as that tree, and where a plan's criteria are genuinely open-ended clinical judgment rather than a structured rule, the honest output is needs-review, not a false verdict, and we would rather abstain than guess. Second, the engine and the domain build are real and the pattern passes our quality gate today, but a live paid run waits on a real plan guideline authored as a tree and a real denial export mapped in, plus a booted mesh, so this is capability-ready, not one-click-live for a stranger yet. And to be clear about scope, Denial Defense produces the verdict and the evidence, it does not file your appeal or send anything to a payer or a regulator, that stays your decision and your action.
Verify a denial
*This page is a specification. The capability it describes is not built yet, and nothing here is a claim that it runs today.*