Document extraction needs reconciliation, not just valid JSON
A practical guide to schemas, provenance, confidence, business rules, reconciliation, and exception queues for document data extraction.
SHORT ANSWER
Validate document extraction at multiple levels: syntax and schema, field provenance, format and domain constraints, cross-field relationships, document totals, confidence, and downstream acceptance. Route unresolved records to review rather than silently converting uncertainty into data.
A schema catches shape, not truth
A model can produce a valid date, amount, or account identifier that does not match the document. Schema validation is necessary for integration safety, but it is only the first layer.
Keep values connected to their source
Store the page, region, source text, extraction method, and transformations needed to produce a value where the use case warrants it. Provenance makes review faster and helps distinguish model errors from ambiguous documents.
Add domain relationships
Check formats, ranges, chronology, identifiers, totals, balances, repeated records, and relationships between sections. These checks turn domain knowledge into deterministic evidence that can challenge plausible-looking output.
Treat confidence as routing information
A confidence score is useful only when calibrated against real cases and connected to an action. Define which records pass, which fields require review, and which documents should be rejected or reprocessed.
Design the exception queue as a product
Show the uncertain field, relevant source context, reason for the flag, allowed correction, and downstream consequence. Record the final reviewed value so evaluation can learn from actual operating errors.
- Do not hide low confidence
- Avoid asking operators to reread an entire document
- Separate extraction correction from business approval
- Measure review time and recurring error classes