AI strategy, transformation, and fluency, with governance and evals built in.See the full operating read →
Healthcare · The evidence door
Clear the coding queue without breaking the audit trail.
For the VP of Revenue Cycle and the CMIO who have to clear more charts without shipping a code an auditor or a payer can question.
Codestraced
Reviewermodel
Gatehuman
Every code traced to the chart. Every output reviewed.
Every claim in the read traces back to source evidence, ownership, and the workflow decision it supports.
Valuefund next
Riskcontain now
Fluencytrain where work changed
At a glanceBuyer: VP Revenue Cycle / CMIO / Health-System COOProduction coder workspace: assign, code, review, auditAutonomous coding pipeline: POC with a design partnerEvery code evidence-linked to chart textThree-model pipeline, human accept/edit gate
What the assist puts on every code
The evidence behind every code, before anyone bills it.
Illustrative shape of the design, not a single customer. Accuracy and throughput figures are held qualitative until measured in your environment.
Four levers
Where the leverage actually lands.
01
Coder throughput
The AI runs the first pass and shows its work. Coders review and sign off instead of typing every code from a blank chart, so more charts clear per coder per day.
02
Evidence on every code
Every suggested code links back to the exact chart text that justifies it. An unexplained code never reaches a claim, because an unexplained code is unbillable.
03
Independent review
A separate reviewer model checks the coder model before a human sees the output. Confidence scoring and hallucination detection flag the weak calls for a closer look.
04
Audit-ready by design
Role-based assign, code, review and audit with full logging. A payer or an auditor can reconstruct who decided what, and why, on any chart.
The coding pipeline
From chart text to a code you can defend.
A chart goes in. A billable, evidence-linked code comes out, with the reasoning attached at every step. The trail a coder uses to accept a code is the same trail an auditor uses to defend it.
Ingest & extract
OCRClinical NLPEntity extraction
Pre-validation model
Chart completenessCodeable encounter
Coding model
ICD-10 / CPTUMLS / SNOMED groundedConfidence scoreEvidence link
Reviewer model
Independent adversaryHallucination detectionLow-confidence flag
Human accept/edit gate
Coder signs offEdits logged
Billable coded chart
Evidence-linkedAudit-ready
Audit trail
Who, what, whyRole-based access
What changes on the floor
Same charts, with the evidence attached.
One assist, read four ways by the four people who have to live with the code.
TodayWith the assist
The coderTypes every code from a blank chart, a few dozen a dayReviews an AI first pass with the evidence already attached
The auditor"The AI said so" is not an answerEvery code traces to the chart text that justifies it
The denialUnexplained codes get kicked back, unbillableEach code ships with its evidence, ready for the payer
The CMIOAI in the clinical workflow is a black-box riskAn independent reviewer and a human gate on every output
Frequently asked
Direct answers.
Not yet. The coder workspace is in production today. The autonomous coding pipeline is a POC running with a launch design partner. We hold accuracy and throughput figures qualitative until they are measured in your environment, rather than quoting numbers from a deck.
AI built across the board
Start with the AI work that moves the number. Keep the proof built in.
Start with Strategy, Transformation, or Fluency; use Quick Audit when the first need is an independent read on what is already running.