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Catching $200K in Underpayments with Document AI

A comparison engine that OCRs submitted claims against received EOBs.

Build Your Document Comparison Engine

About the Project

OCR and AI compare claims against EOBs, surfacing underpayments, denials, and silent code changes.

Industry

Healthcare & Insurance Billing

Claim-to-EOB reconciliation for practices that bill insurance every month.

  • Insurance Billing
  • Revenue Recovery
  • Payer Appeals
Business Type

Dental and medical practices with insurance billing operations

Practices receiving hundreds of EOBs a month with no way to check them.

  • Private Practice
  • In-House Billing
  • Multi-Carrier Mix
Core Offering

AI-powered claim vs. EOB comparison with automated discrepancy detection

OCR extracts both documents, then AI highlights every field that does not match.

  • OCR Extraction
  • Field Mapping
  • Discrepancy Flags
  • Batch Comparison
Recovery Workflow

From detection to appeal in one pass

Appeal-ready reports and pre-filled letters lifted appeal success from 30% to 72%.

  • Appeal Templates
  • Underpayment Alerts
  • Carrier Patterns
  • Status Tracking
Results

$200K Recovered in Year One

95% of discrepancies caught automatically versus 20% under manual review.

  • 95% Caught
  • 72% Appeal Success
  • 3 Days to 2 Hours
Build your idea

Talk to our experts

Scope your own claim-to-EOB comparison engine with our healthcare billing team.

  • Free Consultation

The Problem: Silent Underpayments

Underpayments went unnoticed and unrecovered.

Silent Loss
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.
Unprovable
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.
3d to 2hr
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.

From undetected underpayments
to automated discrepancy catching
and $200K in recovered revenue

Recover Your Lost Revenue

Our Solution

  • OCR-Powered Document Extraction

    OCR-Powered Document Extraction

    OCR-Powered Document Extraction

    • Upload claim and EOB for side-by-side comparison
    • OCR extracts amounts, codes, dates, and patient info
    • Field mapping across every carrier's EOB format
  • AI-Highlighted Discrepancy Detection

    AI-Highlighted Discrepancy Detection

    AI-Highlighted Discrepancy Detection

    • Flags billed vs paid, changed codes, denied items
    • Underpayments flagged above your set threshold
    • Detects systematic underpayment across carriers
  • Batch Reconciliation & Audit Trail

    Batch Reconciliation & Audit Trail

    Batch Reconciliation & Audit Trail

    • Batch reconciliation of a full month of claims
    • Comparison history and audit trail for compliance
    • Dashboard of underpayments, recovery, and appeals
  • Appeal-Ready Report Generation

    Appeal-Ready Report Generation

    Appeal-Ready Report Generation

    • Comparison reports exported for appeals
    • Appeal letters populated with the discrepancies
    • Tracks appeal status and outcomes
  • Recovery Analytics & Payer Trends

    Recovery Analytics & Payer Trends

    Recovery Analytics & Payer Trends

    • Underpayment totals tracked by carrier, code, and location
    • Repeat patterns escalated as contract-level disputes
    • Recovery rate and appeal outcomes on one dashboard

Challenges: Invisible Revenue Loss

Hover a row to see what changed.

Why It Recovers Lost Revenue

A comparison engine covering the claim-to-EOB workflow: OCR, discrepancy detection, appeal reports.

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

Global presence

Three offices. One team.

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