Single-model OCR breaks on real documents
Vendor invoices aren't clean PDFs — they're forwarded screenshots, skewed phone scans, faxes, and PDFs with three different currencies on one page. A single model's accuracy collapses exactly where it matters most.
Every field gets read twice, independently
Claude and GPT-4o each read the raw Textract output and extract the same fields without seeing the other's answer. Where they agree, confidence is high. Where they don't, that's the signal a single model could never give you.
Four fields agree and clear automatically. The due date doesn't — a smudged digit on the scan — so that single field routes to a reviewer instead of the whole document silently going through wrong.
The confidence bar to skip a human entirely
Agreement alone isn't enough — critical fields need both models to agree and clear a 95% confidence threshold before a document auto-submits to the ERP with zero human involvement.
A single model can only ever tell you what it thinks. Two models tell you what they agree on — and that's the difference between automation you can trust and automation you have to babysit.