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Now onboarding customers

Get the claim right
at the record.

Healthcare already wrote everything down. The chart note, the procedure performed, the code submitted, the payer's answer. Antecedent Health checks insurance billing against the treatment that was actually provided, with coding rules applied as of the date of service and the source behind every finding shown.

Every finding cites the document it came from.

Revenue Integrity How the platform works
Retrieval-groundedFindings are anchored to a source document and the passage is shown, not summarized
De-identified firstPHI is tokenized in our middle layer before any model sees a record
Service-date scopedRules are versioned and applied as of the date of service, not the date of the run
No API requiredWorks from an export. Most billing operations do not have an EHR API and never will
The premise

The hard part was never the model.

An enterprise AI system in a regulated setting has three layers. The language model is the least differentiated of them — everyone can rent the same one. The data underneath is the customer's, and it is theirs alone. What determines whether the system is trustworthy is the layer in the middle.

Governance and escalation

What the system is permitted to conclude on its own, what it must route to a human, and what happens when it is uncertain. Written down as rules, not left to a prompt.

PHI handling

Records are de-identified and tokenized before inference. The token keeps a patient's claims linked without carrying their identity. Access is logged; authentication is multi-factor.

Provenance

A finding that cannot be traced to a source document is not a finding. Every output carries the citation and the version of the rule set that produced it.

Why this matters commercially, not just technically

A coding finding you cannot defend to a payer is worthless, and a finding a reviewer cannot check is a liability. The deliverable is not an answer — it is an answer with its evidence attached.

Revenue Integrity

Insurance billing, checked against the treatment actually provided.

For medical billing companies, physician groups, and health systems — the people responsible for getting a correct claim paid the first time.

What it checks

  • Procedure-to-diagnosis coding consistency at the service-line level
  • Denial risk scored before submission, with the reason cited
  • Rules applied as of the date of service, not the date of the run
  • Documentation-to-code support: does the note carry the code?

Where it fits

Alongside the billing platform and the clearinghouse, not in place of either. Two insertion points are practical today: a pre-submission pass over a batch of charges, and a post-denial pass that reads the denial and the record together to find what caused it.

Neither needs an EHR integration. An export is enough.

Revenue Integrity in detail →

Where we start

Start at the note, not the claim.

Everything that goes wrong downstream in a claim was already decided upstream in the record. The code is derived from what was documented; the denial is derived from the code. So the useful place to stand is not at the claim — it is at the note.

1 · The recordChart notes, encounter data, medication and procedure detail — structured and unstructured
2 · ExtractionNormalize, de-identify, tokenize, before any model sees the record
3 · RetrievalGround the question in a versioned corpus of authoritative source documents
4 · ReasoningModel inference under explicit governance and escalation rules
5 · FindingAn answer, the citation behind it, and the rule version that produced it

Steps 3 through 5 run against a versioned corpus of coding and billing authority, so every finding names the rule and the rule version behind it.

Where this actually stands

Built, live, and ready for customers.

We would rather be believed than impressive. The platform is a working build, one industry design partner is engaged and validating output against real workflow, and we are actively onboarding new customers now.

Today

Working software: ingest, de-identification, retrieval-grounded per-claim analysis with cited findings, and an authenticated interface. One design partner from the medical billing industry is reviewing output against real-world workflow, and we are onboarding additional customers.

In build

Service-date-scoped rule versioning. Payer-specific rule sourcing. Documentation-to-code derivation from the note.

Designed

Denial-history feedback: findings scored against what payers actually did, so the system learns from real adjudication rather than from rules alone.

What we are not claiming

  • No completed pilot yet, and no published accuracy or denial-reduction figure
  • Not a medical device, not FDA-cleared, and not clinically validated
  • Not a diagnostic, and not a substitute for clinical judgment or a certified coder's review
  • No claim to discover biomarkers, predict drug response, or do any laboratory or pre-clinical science
  • Nothing here replaces a billing system, a clearinghouse, or an EHR — it is a layer above records those systems already hold
Why we built this

Built by operators who run regulated data pipelines for a living.

Antecedent Health is built on Amazon Web Services and runs on Amazon Bedrock. We built it because the engineering underneath it is the engineering healthcare organizations keep needing: PHI boundaries, retrieval you can audit, and governance that holds up when someone asks how the system reached a conclusion.

About Antecedent Health Platform architecture