Catching $200K in Underpayments with Document AI
A comparison engine that OCRs submitted claims against received EOBs.
Build Your Document Comparison EngineAbout the Project
OCR and AI compare claims against EOBs, surfacing underpayments, denials, and silent code changes.
Healthcare & Insurance Billing
Claim-to-EOB reconciliation for practices that bill insurance every month.
Dental and medical practices with insurance billing operations
Practices receiving hundreds of EOBs a month with no way to check them.
AI-powered claim vs. EOB comparison with automated discrepancy detection
OCR extracts both documents, then AI highlights every field that does not match.
From detection to appeal in one pass
Appeal-ready reports and pre-filled letters lifted appeal success from 30% to 72%.
$200K Recovered in Year One
95% of discrepancies caught automatically versus 20% under manual review.
Talk to our experts
Scope your own claim-to-EOB comparison engine with our healthcare billing team.
The Problem: Silent Underpayments
Underpayments went unnoticed and unrecovered.
From undetected underpayments
to automated discrepancy catching
and $200K in recovered revenue
Recover Your Lost Revenue
Why It Recovers Lost Revenue
A comparison engine covering the claim-to-EOB workflow: OCR, discrepancy detection, appeal reports.
Intelligent OCR Extraction
OCR extracts and maps fields across every carrier format.
Automated Discrepancy Detection
Every field compared, catching 95% of discrepancies manual review misses.
Appeal-Ready Documentation
Comparison reports and pre-filled appeals lifted success from 30% to 72%.
Batch Monthly Reconciliation
A month of claims reconciled in one batch: 3 days to 2 hours.
The Impact: Revenue Recovered
Every number below was measured in production after launch — not projected in a pitch deck.
$200K
Underpayments Found — In the First Year
95%
Discrepancies Caught — Automatically vs. 20% Manual
72%
Appeal Success Rate — Up from 30%
93%
Time Reduction — Monthly Reconciliation