Undetected Underpayments at Scale
With hundreds of EOBs received monthly, practices had no practical way to compare each one against the original claim — letting silent underpayments accumulate into significant revenue loss.
An AI-powered document comparison engine that OCRs submitted claims against received EOBs — catching 95% of discrepancies automatically and recovering $200K in underpayments in the first year.
Build Your Document Comparison EngineAn automated document comparison platform that uses OCR and AI to extract, compare, and highlight discrepancies between submitted insurance claims and received Explanations of Benefits (EOBs).
The system identifies underpayments, incorrect denials, and code changes that would otherwise go unnoticed — generating appeal-ready reports and turning silent revenue loss into recovered income.
Practices receive Explanation of Benefits (EOBs) from insurance companies but rarely compare them against submitted claims — leading to silent underpayments, incorrect denials, and missed revenue that accumulates month after month.
A practice manager in Florida had a nagging feeling they were being underpaid but couldn't prove it. Manually comparing hundreds of EOBs against claims each month was impossible with existing staff, so discrepancies went undetected and unrecovered.
The objective was to build an automated comparison engine that could OCR both documents, extract key fields, highlight every discrepancy, and generate appeal-ready reports — turning a 3-day manual process into a 2-hour automated one.
With hundreds of EOBs received monthly, practices had no practical way to compare each one against the original claim — letting silent underpayments accumulate into significant revenue loss.
Manually reviewing and comparing claim documents against EOBs took 3+ days per month and still only caught roughly 20% of discrepancies due to human oversight.
When underpayments were caught, practices lacked the detailed comparison documentation needed to file successful appeals, resulting in only a 30% appeal success rate.
Without aggregate analysis, practices couldn't identify carriers that systematically underpaid specific procedure codes — missing opportunities for proactive intervention.
We built an AI-powered comparison engine that automates the entire claim-to-EOB reconciliation workflow — from OCR extraction to discrepancy detection to appeal-ready reporting.
Advanced OCR accurately extracts and maps fields from both claims and EOBs across multiple insurance carrier formats.
AI compares every field and highlights underpayments, code changes, and denials — catching 95% of discrepancies that manual review misses.
Generate detailed comparison reports and pre-populated appeal letters that increased appeal success rates from 30% to 72%.
Process an entire month's claims vs. EOBs in one batch, reducing reconciliation time from 3 days to 2 hours.
