Case study
End-to-End Document Intelligence Pipeline
An eight-stage production platform that turns messy scans into structured data: 84% end-to-end, later 88% with an LLM layer.
Problem
Thousands of medical and insurance documents arrived as poor-quality scans mixing print, handwriting, checkboxes, and multi-page records. No off-the-shelf OCR could handle the full journey from noisy image to validated structured data.
Each failure mode needed a dedicated solution, working together as one reliable production system, not disconnected scripts.
Solution
We designed eight specialized stages: noise classification, U-Net denoising, handwriting localization, handcheck detection, OCR recognition, language-model post-processing, value codification, and interpage correlation.
Every stage is independently deployable, so each one can be retrained and scaled without touching the rest of the pipeline.
Outcomes
Reached 84% end-to-end extraction accuracy in production.
Modular design allowed an LLM extraction layer to raise accuracy to 88%.
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