AI document processing checklist
Separate recognition, field extraction, validation and approval when specifying document automation.
Author：WeTech technical team · Updated： (Hong Kong time (GMT+8))
Specify document types, languages, scan quality, required fields, validation rules and human review. Recognising text is different from extracting correct fields or approving an action. Low confidence, conflicting values and missing pages need explicit exception handling.
When to use this guide
For invoice, application, supplier-document or contract workflows that extract drafts for staff confirmation.
Document processing boundaries
| Stage | Specify | Exception |
|---|---|---|
| Intake | Formats, pages, duplicate detection | Corrupt, missing or repeated files |
| Extraction | Field definitions and source location | Blur, handwriting, split tables |
| Validation | Format, totals and reference records | Conflicting or unmatched values |
| Delivery | Human confirmation, write and audit trail | Rejection, corrections and retries |
Define fields rather than “understanding”
Define each field, type, permitted blank and source location. Keep extracted, normalised and corrected values distinct with correction reasons. Test multiple dates, similarly named fields and tax-inclusive versus exclusive amounts rather than accepting only clean demonstration files.
Connect confidence to review rules
Tool confidence is not a measure of business risk. Payment details or high-impact fields may require review even when well formed. Calibrate thresholds with representative samples and report errors by field, rather than one average document accuracy number.
Separate extraction from approval
Start with reviewable drafts and write to production only after the agreed approval. Retain document and processing versions, reviewer and write status. Prevent duplicate records on retries and assign ownership of failed writes.
Illustrative scenario
Hypothetical workflow: extract invoice number, date and total, compare the order and send a draft to accounting. Bank-account changes require separate approval, and repeated invoices enter an exception queue. This is not a delivered client case.
Frequently asked questions
Are OCR and field extraction the same?
OCR recognises text. Field extraction additionally identifies meaning, structure and source location.
Can it process handwriting?
Test actual languages, handwriting and tool versions; universal readability cannot be assumed.
Should we retain originals?
Agree retention around business, contractual and privacy requirements while maintaining traceable review.
Brand and public case scope
WeTech Consulting, also called WeTech or WeTech AI HK, provides AI and IT consulting in Hong Kong. WeProperty HK and WeCredit HK are self-developed products. Their cases support the observable features listed: property, estate and transaction-reference entry points for WeProperty; submission, review and public-data masking for WeCredit. WeCredit is not a TU credit-rating service or a final determination of facts.
WeProperty HK · WeCredit HK (case studies in Traditional Chinese)
Reference sources
- Microsoft Learn: Document Intelligence overview
- PCPD：Artificial Intelligence: Model Personal Data Protection Framework
Official documents support the technical, risk and measurement concepts. Checklists, estimation methods and illustrative scenarios are WeTech planning recommendations, not official prices or client results. Check the supplier’s current plan and terms. Sources reviewed: (GMT+8)
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