Document Intelligence

Reliable structured data is the foundation everything downstream sits on. Treat extraction as a data-quality problem, not a prompting trick.

Approach

• Normalize before you reason. Run OCR and layout parsing (Azure Document Intelligence) and reduce every format to a common representation before an LLM sees the page. Garbage in poisons everything after it.
• Extract against a schema. Drive extraction with an explicit Pydantic schema. Never accept free-form JSON from the model; validate types, enums, and formats on the way out and reject what does not fit.
• Score confidence and route. Attach a per-field confidence signal and send only the low-confidence fields to a human. Reviewing everything defeats the point of automating.
• Assume heterogeneity. No two vendors format things the same. Prefer a few robust, general extractors over one brittle template per document type.

Defaults

• A tight schema removes more ambiguity than a longer prompt ever will.
• Measure accuracy per field, not per document — one wrong total sinks the whole record.

Evidence

• High-throughput contract extraction turning heterogeneous documents into structured data.
• A veterinary platform parsing invoices and reports into a queryable schema.

Stack

Azure Document Intelligence · OCR · LLMs · Pydantic · Python