AI System for Checking Customs Document Readiness

The problem

Quantity and product information may differ between documents in day-to-day operations. When commercial invoice, packing list, transport document, product record and file checklist data are scattered across sensor, document, site or team records, the issue may not be identified in time.

This fragmented data delays preparation of the customs file discrepancy list and forces teams to search for supporting evidence again by hand. Before making a decision, the authorised customs broker and international trade manager must complete missing records and verify each inconsistency individually.

A common exception is that a partial shipment or a different unit of measure may create a valid discrepancy. Incomplete or incorrect automation that does not check this context could lead to an incorrect declaration being prepared or a valid file being held unnecessarily, so no physical or operational action can be taken directly.

What we set out to improve

• Number of document discrepancies confirmed by the specialist
• Proportion of outputs corrected or rejected by the authorised customs broker and international trade manager
• Time between the source event and the review output

How the system works

1. 01 Define the review scope and the roles of the authorised customs broker and international trade manager
2. 02 Retrieve the inputs: commercial invoice, packing list, transport document, product record and file checklist
3. 03 Check the source identifier, date, calibration or version, and data recency
4. 04 Identify the document type and version
5. 05 Compare the product, quantity, weight and party fields
6. 06 Check the exception: a partial shipment or a different unit of measure may create a valid discrepancy
7. 07 Place low-confidence or inconsistent records in a separate specialist queue
8. 08 Write the source identifier, rule, output and time to the audit trail
9. 09 Have the authorised customs broker and international trade manager approve, correct, defer or reject the output

Methods we used

• Cross-document field checks without producing a tariff classification or declaration
• Source, format, time and required-field checks for commercial invoice, packing list, transport document, product record and file checklist data
• A missing-data warning instead of an estimate when inputs are incomplete or inconsistent
• A separate exception check outside the main rule
• Joint retention of the source, rule result, customs file discrepancy list and human decision

Where people stay involved

Data and security

Commercial invoice, packing list, transport document, product record and file checklist data are accessed with the narrowest permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary portion of the data is sent to the model rather than the complete raw dataset. Access, outputs and the decisions of the authorised customs broker and international trade manager are recorded; production does not begin until the organisation approves the retention period and data location.

Who this suits

Frequently asked questions