Documents into evidence.
Separate recognition from interpretation and build evaluation, validation, and human review into document extraction.
AI & LLM
Automate the reading.
Keep the evidence.
Use models for defined interpretation tasks, then validate the result against source evidence. Separate recognition, extraction, normalization, and review instead of treating confidence as proof.
A valid shape is not a verified fact.
Keep unsupported fields explicit, evaluate reviewed examples, and require evidence for consequential values. A model may produce a well-formed response while selecting the wrong item or inferring an unstated fact. Separate extraction from actions that need authorization.
Go deeper
Read the complete workflow.
Extract meaning. Keep proof.
Design narrow text tasks, constrain untrusted content, and accept fields because the source supports them.