AI Extract API
Combine recognition and interpretation with field-level evidence and a review path.
A focused use case
Process an approved family of purchase documents to identify supplier names, issue dates, and totals, while flagging ambiguous or unsupported values.
A practical workflow
Define the document family and field dictionary. Separate recognition from mapping, evaluate reviewed examples, and apply structural and business-rule checks before the result enters a downstream workflow.
An example record contract
These fields illustrate the decisions to make before implementation. Adapt the names and required values to the receiving system; the table is not a promise of a live endpoint or a provider-specific response.
| Example field | Meaning in this contract |
|---|---|
| document_id | Input identity |
| field_value | Extracted or normalized value |
| evidence_ref | Supporting source location |
| review_status | Acceptance decision |
Limits worth keeping visible
A confidence score is not a universal accuracy guarantee. Evaluate it on your inputs and separate direct extraction from inference or calculation.
Before increasing the workload
Run a small, authorized test set through the whole path, including the receiving application. Keep accepted, rejected, and incomplete outcomes distinguishable. A result should retain the context needed to explain its meaning after it leaves the extractor.
Continue with AI Extract API: A Reviewable Document Workflow for the full implementation discussion, and use the sample contract documentation to review the shape of a handoff.
Consult the source documentation for implementation details and current access conditions. The workflow above is an editorial planning pattern.