Every code, with the rule and record evidence behind it.
Healthcare reimbursement is largely decided by evidence: what a clinician documents, and what a coder builds from the evidence.
Miss the evidence, compromise the reimbursement.
What if you could capture all the evidence, for every encounter, every time?
The nine dimensions of evidence that coding demands.
The work has to be exact, complete, and defensible. Getting it right is cognitively demanding work, for both clinician and coder.
Capturing the full clinical picture:
across a high volume of encounters.
Capturing the full encounter picture:
inside a fixed and shrinking window of time.
Surviving the interrogation:
Even a claim that clears every automated check can be pulled for medical record review at the first doubt about its evidence.
Praxevo is built to carry that cognitive load at scale with Prescriptive AI — evidenced, governed, and consistent: defensible coding determinations with the evidence attached.
Generative AI aimed at precision, not independence.
Prescriptive AI is generative AI exercised systematically, purposefully and pragmatically, within a prescribed architecture that directs how it reasons, what it knows, and what its output must prove.
Every decision applies published industry rules, not whatever the model learned in training.
Instructions, rules and changes are controlled and versioned. Nothing about an agent or its resources changes without review.
Each agent is pointed at the specific rules and reference resources for one job. It doesn't roam, and it never sets its own goals.
Praxevo runs alongside your systems, never inside them. It takes what your systems already produce, a medical record or an EDI claim, and returns a determination.
Every determination shows the record text and the rule behind it, so a person can check it.
AI recommends and a person decides. The coder is the final authority.
The same generative AI and the same engineering disciplines as autonomous AI, aimed at precision instead of independence.
The problems standing in the way, and the solutions leading the way.
The calculations, rulebook lookups, criteria and edits. No time, no surface, no mechanism to keep them.
Coding automation mostly learns what coders decided, not the record and rule evidence behind the decisions.
115+ checks in every chart, against 74,719 diagnosis codes, 20,319 CPT and HCPCS codes, 406 modifiers and 4.5M edits.
About $12 lost per encounter: an 8% denial rate, about 9% of E/Ms undercoded, 67% of denials never resubmitted.
A record quote, a rule quote, a reason, what was ruled out, and a flag where the evidence was soft.
Instructed by a prescribed architecture: deterministic, governed, directed, bounded, evidenced. A person decides.
The same coding criteria, guidelines and rules, applied at greater scale and with more depth of instruction.
A coder edits a table term or a sentence of rule text. The change is visible, reviewed and versioned.
Bring a chart. See the codes, the rule behind each, and the record language each step rests on.
Request a demo →The problem and the solution at full depth: how the agents, rules and evidence work. Access is by request.
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No claim goes unreviewed. Every claim runs a real gauntlet — four layers deep, each looking for a different kind of imperfection — before it's ever paid.
Every claim is checked against hundreds and thousands of EDI compliance edits and federal and state coding rules — NCCI bundling edits, mutually exclusive code pairs, unit caps — before a payer has considered anything clinical at all.
Layered on top, each payer writes its own proprietary criteria beyond the federal and state baseline. AMA: payer-specific edits alone drive 61% of all denials, many considered a black box inaccessible to clinicians and billing staff.
A second, separate mechanical pass — not a human decision — scores and correlates findings into a payment, denial, or downcode determination. Payers automate downcoding algorithms to work directly from claims data.
Whatever survives the first three layers gets additional review through analytics and generative AI to find anomalies, inconsistencies, and suspicions. These trigger a medical record request while the claim payment is pended.
The Problem and The Solution go under the hood. Enter the access phrase to continue.
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