AI document review
AI commercial invoice extraction is a suggestion workflow, not an acceptance decision.
Review AI-extracted commercial invoice data field by field: keep the source file and extraction state visible, compare critical values with the document, separate suggestions from accepted data, route uncertainty to a named reviewer, and retain the resolution with the shipment record.
By Ayhan Karaca, Co-Founder · Updated: August 21, 2026
What should teams verify after AI extracts a commercial invoice?
At minimum, compare the supplier and buyer identity, invoice number and date, currency, line descriptions, quantities, unit values, totals, and shipment references with the source document. The fields that matter most depend on the transaction and downstream decision; no fixed checklist makes an invoice legally sufficient or customs-ready.
NIST's AI RMF organizes risk work into four functions—Govern, Map, Measure, and Manage—and its Generative AI Profile notes that generative systems may warrant additional human review, tracking, documentation, and management oversight. Applied to invoice extraction, that supports visible responsibility and evidence rather than silent acceptance.
| Review area | Compare with the source | Resolution state |
|---|---|---|
| Identity | Supplier, buyer, and shipment reference | Confirmed or mismatch |
| Invoice control | Number, issue date, and currency | Confirmed or needs review |
| Goods | Descriptions, quantities, and units | Confirmed, incomplete, or ambiguous |
| Values | Unit values, line amounts, and total | Reconciled or exception |
| Downstream use | The decision that will consume the field | Approved for that use or blocked |
Separate five states that are often collapsed
A file can be uploaded without being processed. A field can be extracted without being correct. A value can be correct on the page but unsuitable for a specific downstream use. A reviewer can identify an exception without resolving it. The final accepted value therefore needs a distinct state and accountable decision.
- Uploaded: the source file is present.
- Processing: extraction is still underway or unavailable.
- Suggested: a machine-proposed value is ready for comparison.
- Exception: evidence is missing, conflicting, or unclear.
- Accepted: an authorized human recorded the value for a defined use.
How do you measure extraction quality without a misleading accuracy claim?
Build a representative evaluation set from the invoice formats, languages, scan qualities, currencies, and line-item patterns your organization actually receives. For each critical field, measure exact match, acceptable normalized match, missing extraction, incorrect extraction, and reviewer correction time. Report the sample size and document mix beside the result.
Do not publish one accuracy percentage without the evaluation set, field definitions, tolerance rules, and review boundary. A high aggregate number can hide a weak critical field, while a lower-complexity format may not represent the documents your team receives.
How Tyllus keeps AI output inside an operational review
Tyllus can process supported uploaded shipment documents and present extracted suggestions with processing and resolution states inside the shipment context. The source file remains available to authorized users, and the accountable user reviews the result before relying on it for operational work.
Tyllus does not certify document authenticity, determine customs classification, file an entry, replace broker review, or guarantee extraction accuracy for every document. The importer and appointed professionals remain responsible for the records and decisions they use.
Test AI document review with your acceptance rules.
Use the guided demo to upload representative test files, inspect processing states, and review suggestions without mixing the result into live shipment data.