Autonomous coding. The 92 percent honest number.
Every vendor demo of autonomous coding hits 99 percent. Every honest production number is lower. On tuned specialties, our auto-accept rate measured on a rolling 90-day window across the production book is 92 percent. On day one before tuning, it is 78 percent. We publish both because the second one is what a new client should plan on for the first 60 days, and pretending otherwise is the reason most autonomous coding rollouts collapse at month three.
Three rates the vendor demo will not show you.
Every vendor leads with the highest number on their happiest day. We publish three because the gap between them is the real conversation. The auto-accept rate is a function of how long the model has seen your documentation, your payer mix, and your specialty edge cases. Numbers below are measured on our own production book.
Auto-accept rate on a new engagement before any tuning to your documentation patterns. This is the number a coder team should plan on for the first 60 days. Anyone showing you a higher day-one number is either cherry-picking the specialty or showing you their best client.
Auto-accept rate after 60 days of tuning on a specialty we know well (ABA, common MA HCC, primary-care office E&M). Measured across the production book on a rolling 90-day window. The remaining 8 percent goes to a CRC, CPC, or CCS-certified coder for accept, modify, or reject.
Clean-code rate measured by first-pass acceptance at the clearinghouse on auto-accepted suggestions, before any payer touches them. The auto-accept rate is the upstream metric. The clean-code rate is what determines whether you get paid first pass. Both matter; only one shows up on the SLA.
The coder-in-the-loop architecture.
The architecture has five steps. Each step is operationally tested, audited, and visible to the client through the same console our coders see. The 92 percent number is the output of all five steps working as designed. When the rate drops, we know which step is the cause because each one is independently measured.
The coder is not coding from scratch on the cases that route to them. They are reviewing a high-quality suggestion with MEAT evidence already extracted and the confidence score visible. Their time is spent on the cases where their judgment is the difference between captured and missed revenue, not on the cases where the answer was obvious.
Five things vendors say about autonomous coding. The truth on each one.
The autonomous coding category has its share of theater. Five of the most common claims we hear from prospects who have been pitched by other vendors, and what is actually true on each one. The truth is shorter and more boring than the myth.
99 percent of charts auto-coded, no coder needed.
Pitched as the future of coding. Demoed on a curated sample of straightforward office visits. Sold against a license-fee P&L.
Tuned book hits 92 percent. The 8 percent that escalates is where the revenue and risk live.
Coding is bimodal. The middle is easy. The edges are where the dollars and the audit exposure are. A coder on the edges is the right architecture; not having one is the wrong architecture.
The model learns your patterns in days, not months.
Sold as the reason rollout will be faster than the competition. Means the vendor is overfitting on a small sample.
60 days minimum to tune a specialty. The model gets there fast on the common cases and slowly on the long tail.
Days-not-months on common documentation patterns. Months on edge cases, unusual payer rules, and specialty-specific modifier patterns. The tuning rate is asymptotic, not linear.
RADV defense is automatic from the AI audit trail.
Implied to mean the auditor will accept the AI evidence without further review.
RADV defense is the MEAT documentation plus the certified-coder validation event. The AI is the upstream evidence pipeline.
The model extracts the MEAT and surfaces it; the certified coder validates and signs off; the audit trail captures both. The auditor reviews the chart, the MEAT, and the validation. The AI does not get the auditor to skip review.
A horizontal LLM with the right prompt is enough.
Said by every vendor who wrapped a foundation model in a CSS skin and called it a coding product.
The V28 condition map, the MEAT extraction rules, the three-way ABA match are domain primitives. The LLM is a layer in the stack, not the stack.
A horizontal model with no domain primitives will hit 60 percent and stop. The domain primitives plus the model plus the certified-coder loop are what get you to 92 percent and clean code.
Auto-accept rate is the metric that matters.
Heavily advertised because it is the easiest number to make look big in a slide deck.
Captured revenue, clean-code rate, and RAF lift are the metrics on the SLA. Auto-accept is upstream of them, not a substitute.
A 98 percent auto-accept with a 70 percent clean-code rate is worse than a 90 percent auto-accept with a 99 percent clean-code rate. The SLA measures the downstream outcome, not the upstream theater.
Frequently asked questions: autonomous coding.
What does autonomous coding mean in 2026?
What is the honest 92 percent number?
What is coder-in-the-loop?
How do you handle a case the model has never seen?
What about RADV and risk-adjustment audit?
Are you replacing the coder?
How is your autonomous coding different from competitor offerings?
When should I expect to see the 92 percent number?
Run the model on your charts. Get the honest number.
Free 30-day coding audit on a 90-day encounter sample under a same-day BAA. We run the model on your real documentation, measure the day-one auto-accept rate, project the post-tuning rate, and deliver a four-page written report with sample evidence and the implementation roadmap.