Claims Document Extraction

Claims documents arrive as scanned PDFs. Manual data extraction is slow and error-prone; automated extraction without confidence checks is risky in a regulated environment where a wrong field value is a compliance problem.

Claude vision extracts structured data from claims documents. Output is validated through Pydantic schemas — not free-form JSON. Each field carries a confidence score. Fields below threshold trigger a needs-review flag that routes the document to a human reviewer before any downstream processing. Built and ready for production at Zororo-Phumulani.

1. 01

   Pydantic validation at the extraction boundary means any field the model cannot confidently extract fails explicitly rather than silently propagating a wrong value.

2. 02

   Per-field confidence (not a single document-level score) lets the human reviewer see exactly which fields are uncertain rather than re-reviewing the whole document.

3. 03

   Needs-review flag routes low-confidence documents to human review before any downstream processing — the model's uncertainty is part of the output contract.