AI agents that auto-appeal healthcare denials — at the scale your team never could.

Most denials go unappealed because the labor never pencils out. We build a production agent that reads the denial, assembles the evidence, and drafts the payer-specific appeal — for the entire queue.

What the agent actually does.

A production agent that works the whole denial queue with evidence-backed appeals.

Workflow

How the agent runs

Denial to a filed, payer-specific appeal — at queue scale.

1
Classify denial
2
Map evidence
3
Draft appeal
4
Work the queue
Evaluation

Proven, not promised

Measured on overturn outcomes and recovered cash.

96%
ILLUSTRATIVE — NOT CLIENT DATA96% eval target.
clean-appeal rate
Inputs

Reads every source

Reads the denial, claim, and clinical record together.

DenialOriginal claimChartCodingPayer policy
Output

Structures the data

Classifies the denial and maps rebutting evidence.

ReasonMed. necessity
Recoverable$18,400
EvidenceMapped
Decision

Decides with evidence

Evidence mapped to the payer's own criteria.

Appeal
Filed
Reason classified
Criteria matched
Payer-specific letter

Most denials are recoverable revenue you write off.

Many denials would overturn on appeal. But appeals take time, records, and payer expertise — so teams work a fraction and write off the rest. The agent changes the math.

$260B
lost to denials across U.S. hospitals every year
At scale
evidence-backed appeals, automatically
Higher
overturn rates and recovered revenue
1
workflow owned end-to-end

When the labor cost goes to zero, the write-off list shrinks.

The appeal you don't file is revenue you concede. The agent reads every denial in the queue, matches it to evidence, and appeals against the payer's stated criteria. Your revenue-cycle staff review the highest-value exceptions instead of grinding through volume.

  • Appeals that were written off now get worked at scale
  • Higher overturn rates from evidence mapped to payer criteria
  • Denial-prevention insights from patterns across the queue
  • Revenue-cycle staff focus on the highest-value exceptions
The ROI

When an agent works the whole queue, written-off revenue becomes recovered cash. At hospital scale, that changes the P&L.

Common questions, answered.

01

Why do so many denials go unappealed?

Appeals are labor-intensive — pulling records, mapping evidence to payer criteria, and drafting payer-specific letters. Most teams can only work a fraction, so recoverable revenue is written off. An AI agent works the entire queue.

02

How does AI improve overturn rates?

It maps clinical and coding evidence to the payer's own medical-necessity and policy criteria and drafts payer-specific appeals — the structured, evidence-backed appeals most likely to be overturned.

03

Who buys AI denials management?

Hospital systems and revenue-cycle teams where denial volume is high and write-offs are material. ACVs are large because the recoverable revenue is large.

04

Is it compliant and auditable?

Yes. Appeals are cited to clinical and coding evidence, and an evaluation framework with human oversight is in place from day one for compliance and payer defensibility.

Related reading

Go deeper: AI Denials & Appeals: Recovering the $260B Hospitals Lose

Why denials go unappealed, how AI changes the unit economics, and what to expect from a production deployment.

Ask about the 2-week audit

Tell us if AI denials & appeals is worth owning.

In a two-week audit we map your denial volume, the recoverable revenue you're writing off, and exactly where an agent pays for itself. You get back a build plan, not a pitch.

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