THE KEY ANSWER
Divide the process into reading, field extraction, validation, and saving. Maintain the association of values with their source and a queue for exceptions. Verify data affecting settlements or liabilities using rules and appropriate approval.
What documents actually reach the company?
Collect samples from various sources: text files, scans, photos, attachments, and different versions of forms. Check languages, image quality, and table layouts. A system refined on one clean PDF may behave differently with a photo from a phone or a multi-page attachment.
Choose one document type and define the required data. “Document processing” is too broad a scope. “Reading order line items and the counterparty number” allows for establishing acceptance criteria. Record which fields can remain empty and which block the next step. It is also worth recognizing files outside the supported scope.
Context and references: AWS Builders' Library: Making retries safe with idempotent APIs
Separate reading from interpretation
Text reading answers the question of what is in the document. Data extraction assigns fragments to fields, and validation checks their logic. A model can help interpret variable layouts, but sums, formats, and identifier consistency should be controlled independently.
Demonstration example: a document contains a net price, tax, and final amount. The system should check the relationship between them and indicate any inconsistency, rather than choosing the most probable number. Show the user the source fragment next to the field. This shortens verification and makes it easier to distinguish a reading error from unusual document content.
Exceptions need a convenient place for handling
Prepare a queue for cases requiring attention with a clear reason: illegible fragment, missing field, unknown counterparty, or inconsistent sums. The user should be able to correct a single value and return to the process without re-uploading the file. Retain information about manual corrections.
Do not base decisions solely on the confidence percentage generated by the model. Verifiable rules and consistency with sources are more useful signals. Mandatory control may be required for critical fields. Set the automation threshold based on the consequences of errors and pilot results, not the expected percentage of unattended cases.
Saving to the system is a separate stage
After accepting the data, check whether the document has already been processed. Plan behavior for interrupted connections and partial saves. The operation should not create a duplicate simply because the user clicked again or the system did not receive a response in time.
Measure the accuracy of required fields, manual review time, the share of exceptions, and the save result. Monitor new document variants separately. When a supplier changes a template, you need to quickly detect degradation. Maintaining such a solution covers data and process rules, not just model availability.
WHERE TO START
Bring this into your project.
- Collect various formats and material quality.
- Define required fields and consistency rules.
- Show the source and allow correction of a single field.
- Control duplicates and partially completed saves.
Choose one thing your process is missing today. It's a useful topic for your first conversation with the team.
QUESTIONS AND ANSWERS
Frequently asked questions.
Are OCR and AI the same thing?
No. OCR is used for recognizing text in images. A model can additionally interpret layout or assign information to fields. The entire process still requires validation and integration.
Can manual control be completely removed?
For some limited classes of documents, this may be justified after testing. It should not be assumed for all data. The scope of control depends on the quality of materials and the consequences of errors.
Sources and context
- AWS Builders' Library: Making retries safe with idempotent APIs ↗
The source explains safe operation retrying, which is important when saving data from documents. The design for reading and field control is an original scenario.
Prepared by the ALGOV team. Current as of September 8, 2026. Examples describe possible scenarios, not results from client projects. How we create our guides.
