Meet your AI Agent for Medical Bill Extraction.

Drug codes validated against national databases

When NDC, J-codes, or other drug codes are extracted, they're verified against national references for clinical coherence — so wrong codes, retired codes, and codes that don't match the diagnosis get flagged before they hit your bill review queue.

NDC Crosscheck | Verified

Every National Drug Code is matched against the FDA's NDC directory at extraction time. Retired, repackaged, and non-existent codes surface as exceptions instead of slipping through as billable.

J-Code Mapping | Clinical

HCPCS J-codes for injectables and infusions are checked for unit math, dosage plausibility, and route of administration — the math a pharmacist would do, applied to every line.

Diagnosis Coherence | Cross-Validated

Drug codes are reconciled against the ICD-10 diagnoses on the same bill. A muscle relaxant billed against a fracture-only diagnosis gets flagged for review rather than auto-paid.

Healthcare-specific terminology

CPT, ICD-10, HCPCS Level I and II, NDC, ASA, DRG, place of service codes, revenue codes, units, modifiers, EOB reason codes — all extracted natively. The model knows what a 99213 means versus a 99214 and treats the modifiers accordingly.

Code Set Fluency | Native

CPT, ICD-10, HCPCS I/II, NDC, ASA, DRG, revenue codes, and place-of-service codes are extracted as first-class fields — not parsed out of free text after the fact.

Modifier Awareness | Contextual

Modifiers like -25, -59, -LT, and -RT change reimbursement materially. The model reads them in context with the parent code, so a 99214-25 is treated as the distinct billable event it is.

EOB Reason Decoding | Translated

EOB and remittance reason codes are mapped to their plain-language meaning at ingest, so denial reasons, adjustments, and patient responsibility splits are queryable instead of buried in alphanumeric strings.

Insurance math, not just extraction

Bill subtotals reconcile to line totals. Date-of-service ranges check against policy effective dates. Duplicate billing — same provider, same DOS, same code — gets surfaced. The AI Agent does the math a bill reviewer would have to do manually.

Line-to-Total Reconciliation | Balanced

Every line charge is summed and reconciled against the bill's stated subtotal and total. Off-by-one rounding errors, dropped lines, and inflated totals are flagged at extraction, not at audit.

Coverage Window Check | Eligible

Each date of service is checked against the policy's effective and termination dates. Out-of-coverage services are surfaced before they're scheduled for payment.

Duplicate Detection | Deduplicated

Same provider, same patient, same date of service, same code combinations are detected across bills and across submissions — catching duplicates that span statements humans would have to compare side-by-side.

Continuous learning from your reviewers

Every correction your team makes in the Review experience feeds back into the model. The AI Agent gets sharper with every bill, on your specific intake patterns — so the work routed to humans shrinks over time, automatically.

Reviewer Feedback Loop | Closed-Loop

Every override, correction, and approval in the Review queue becomes a labeled training signal. The next bill of the same shape is more likely to clear straight-through without a human touching it.

Carrier-Specific Tuning | Tailored

The model adapts to the provider mix, bill formats, and exception patterns specific to your book — not a generic average across the industry — so accuracy climbs fastest on the bills you actually see.

Shrinking Manual Queue | Compounding

As the model learns, the volume routed to human review trends down month over month. Specialist time gets redeployed onto medical judgment work instead of repetitive data entry.