Business automation
Extract Arabic invoice data with reviewable evidence
Separate reading, validation and arithmetic before posting records.
Specify fields and evidence
Define supplier, invoice number, date, currency, line items and totals. Keep the source and field location available for review. Mixed Arabic-English layouts require tests for number order and units, not merely readable text.
Validate independently
Recalculate line totals using explicit rounding rules and compare them with the document. Do not let the model invent a missing number to balance the sum. Route unclear currency or unreadable fields to review.
Roll out by format
Start with selected suppliers and formats, tracking correction rates per field. Deduplicate records and keep financial posting reviewable. Expand only after difficult images meet the quality threshold too.
Separate reading, validation and approval
Extract visible information, validate explicit rules, then review before posting. Keep the original file and page reference for important values. Cropped or unreadable images should flag missing evidence rather than invite guesses. A training invoice with line values of EGP 100, 200 and 300 plus EGP 50 delivery and no other items totals EGP 650. Do not assume a real invoice lacks tax or discounts.
Field-level review rules
Calculate separately from text extraction. A confidence score written by the model is not sufficient approval.
| Field | Check |
|---|---|
| Currency | Read the source, not its language |
| Unit | Distinguish items, cartons and weight |
| Decimals | Review Arabic and Latin numerals |
| Total | Independently calculate visible items |
| Supplier and invoice number | Match records and prevent duplicates |
When should automatic posting stop?
Stop on mismatched totals, missing fields, unknown suppliers or duplicates. Payment-detail changes need verification separate from invoice extraction. Include phone photos, text PDFs, rotated pages and multilingual or multi-page documents. Score fields and correction time: saved reading effort can be outweighed by subtle errors that take longer to review. Retain failure examples with unnecessary personal data removed.
Educational content prepared with AI assistance. Proposed examples illustrate an approach and do not guarantee results. Our editorial approach