03 · AI · BACKEND

Rebuilt invoice OCR with 94% accuracy

Replacing a brittle Tesseract chain with a hybrid Textract plus transformer pipeline, gated by a 4,000-invoice regression eval that runs every release.

Procurement SaaS

Industry

Procurement

Engagement

5 months

Team

2 engineers + 1 ML lead

Stack

Python, AWS Textract, custom transformer, Postgres

The challenge

The legacy OCR pipeline was a brittle Tesseract chain hitting 71% accuracy on real-world invoices. Misreads cascaded into AP automation errors that customers had to reconcile by hand.

Our approach

We built a hybrid pipeline — Textract for layout, a fine-tuned transformer for field extraction, with confidence-aware fallbacks to human-in-the-loop. Every release went through a regression eval against a 4,000-invoice gold set.

The outcome

94% accuracy in production. Customer support tickets on AP errors dropped by more than half. Tooling is now testable and the team can iterate weekly.

94%

Field accuracy

−55%

AP error tickets

4,000

Gold-set invoices

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