AI that shows its work.
Every output carries its evidence. People make the decisions that change care or money.
The coder makes the call. No code is applied automatically.
Axiom Code on encounter 4471. 1. Each reviewer reads the signed note, and only the signed note. 2. Three reviewers propose a level and cite the lines behind it. Every vote is shown. 3. Official edits gate the result. A failed edit holds the code. 4. The coder makes the call. No code is applied automatically. Illustrative, synthetic data.
Six capabilities. One rule.
Show the source.
Notes drafted from the visit.
Each statement links to the recording. Pre-visit briefs, and drafts that learn each provider’s style.
Medical speech recognition.
Speaker-labeled transcripts in about 30 languages, with the note in English.
Findings traced to text.
Every finding points to its source.
Three reviewers, every vote shown.
Edits gate the result. The coder decides.
Claim support, sourced.
Pricing, edits and denial risk, each with its source.
Images and documents, read.
Extracted into fields a person reviews.
Calibrated to each payer.
A 10% risk means about 10 in 100 such claims are denied. Submit, or hold for review.
- 0.4% calibration error, out-of-fold
- The edits behind each score
- Thin history means rules only
Receipts, not a black box.
Audit any estimate down to the fee schedule, edit, payer history or chart note behind it.
- CMS fee schedule matched
- NCCI / MUE edit status
- An estimate, never payer approval
Predicted payment
Denial risk
Measured before it’s trusted.
No accuracy claim without a held-out evaluation. Axiom runs leading models on AWS, orchestrated and checked by VAERION.
AI questions.
Is it a black box?
Whose models are these?
How does Axiom Code work?
Does it improve over time?
See AI that shows its work.
Watch a claim checked before submission, evidence included.