An assistant extracts invoice fields and reports high confidence. The document number is correct, but the currency comes from a note rather than the total. In this illustrative case, successful extraction does not equal a correct outcome. The record needs separate meaning checks before entering the next workflow.
Evaluate fields by the consequences of error
A description error may make search harder. An incorrect counterparty identifier, amount or currency may send a record into the wrong workflow. List critical fields and define their mandatory checks.
Extraction, reference-data validation, document approval and operation execution are different actions. Even perfectly extracted text does not authorize a payment or establish an accounting decision.
Evaluate on documents unseen during tuning
The test set should represent documents expected in operation: different layouts, scan quality, page structures and exceptions. Expected values are prepared and checked separately. A set used for tuning is no longer sufficient for independent acceptance.
Google Document AI documentation distinguishes precision, recall and F1 and explains the effect of confidence thresholds. A higher threshold can reduce incorrectly accepted predictions while increasing omissions. It does not establish a universal threshold for every document.
Define a path for uncertain results
In this example, currency must be associated with the correct document total. Conflicting sources should send the record for review rather than trigger a default value. The reviewer needs the relevant original fragment and the reason for the stop.
Test repeat receipt of the same document separately. Correcting a field should update the intended record rather than create another operation. Agree document identity rules before launch; the document number alone may be insufficient.
| Result | Next action | Responsible role |
|---|---|---|
| Field matches source and rules | Send to the agreed next stage | Process owner defines the rule |
| Value conflicts with another fragment | Show sources for review | Authorized reviewer |
| Document was processed before | Compare identity and state | Register steward |
Accept the workflow, not an attractive demonstration
Keep results by critical field, document type and reason for manual review. An average must not hide a problematic type. The process owner agrees thresholds and acceptable consequences based on the tests.
E-Data Consulting considers Scriptum.DMS and other Intecracy Group solutions after defining these requirements. Supported formats, source presentation and integration are verified in the actual configuration. A practical first step is to assemble a representative sample and define an error for each critical field.
Questions and answers
Is high model confidence sufficient?
No. Compare it with correct answers on an independent sample and examine error consequences in the actual process.
Must every field be reviewed manually?
The process owner decides after testing. Some checks can be formalized, while ambiguous and critical cases need a defined human-review path.
Source and method
Google Document AI: Evaluate performance. The worked example and acceptance procedure above are original editorial guidance, not a quoted standard or a real client case.