Cross-Border Trade

Export-Import & Trade Process Automation

AI-powered trade compliance and documentation automation.

Knowledge Graph
OCR
RAG
ML Classification
Government APIs

01Outcome

90%
faster document processing
80%
quicker customs clearance
70%
reduction in compliance errors

Zero hallucination, every answer grounded in official government data.
Full audit trail via the knowledge graph, every regulatory change tracked.
A single platform replacing fragmented trade, customs and ministry portal lookups.

The gain comes from reconciling automatically and routing only mismatches to a person, rather than reviewing every document in every set.

02The Engagement

A single cross-border shipment generates a set of documents that must agree with one another: commercial invoice, packing list, bill of lading, certificate of origin, customs declaration and whatever the destination jurisdiction additionally requires. Each is produced by a different party, in a different format, frequently in a different language.

The documents originate outside the organisation, so there is no possibility of standardising them at source. The checking is therefore a reconciliation exercise performed by hand, per shipment, against a deadline set by a vessel or an aircraft rather than by the business.

Why it is hard

The consequence of an error is disproportionate and immediate. A mismatched value, weight or classification code between two documents can hold a shipment at a border, and the cost is demurrage, penalties and a delivery commitment missed — not a correction in next month's file.

Volume makes it worse rather than better. The checks are individually simple and collectively enormous, and they are performed on documents whose format varies by counterparty. This is precisely the kind of high-volume, exception-driven reconciliation that rewards automation and punishes fatigue.

What we built

The system applies document intelligence across the shipment's document set: extracting the fields from each document whatever its format or origin, normalising them to a common schema, and reconciling them against one another.

Where documents agree, the shipment proceeds. Where they do not, the discrepancy is raised with both source documents open at the conflicting values, so the person handling it sees the problem rather than hunting for it. Compliance staff work an exception queue instead of reading every document in every set.

Extraction across heterogeneous third-party documents, including scanned originals
Normalisation to a common schema so documents from different parties can be compared
Cross-document reconciliation of values, weights, quantities and classification codes
Discrepancies raised with both conflicting sources shown side by side
Clean sets passed through, with human attention reserved for exceptions

03Questions

Questions about this engagement.

The documents come from third parties in inconsistent formats. Does that break it?

It is the design assumption rather than an edge case. Extraction is built for documents the organisation has no control over, including scanned and photographed originals and non-English text. Requiring counterparties to standardise their paperwork is not an option in cross-border trade, so the system does not depend on it.

What happens to a discrepancy the system finds?

It goes to a person, with both conflicting documents open at the values that disagree. The system does not resolve discrepancies — deciding whether a weight difference is a rounding artefact or a real problem needs judgement and often a conversation with the counterparty. What it removes is the work of finding the discrepancy in the first place.

Does it keep up with changing customs requirements?

Requirement sets are maintained as configuration rather than embedded in the system, so a change is a configuration update instead of a rebuild. The checks themselves are stable; what changes is which checks apply to which lane, and that is exactly the part designed to be editable.

04Capabilities