Decisions across documents, counterparties and rules.

International Trade, Logistics & Supply Chain

Trade and logistics workflows assemble facts from documents issued by different parties and systems. ARVEC focuses on source-grounded extraction, cross-document comparison, semantic consistency, exception analysis and review workflows to support operations where discrepancies are consequential.

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01Decisions in context

The questions an AI-assisted workflow has to answer.

Do the documents presented agree with each other and with the credit’s terms?
Does the shipped quantity match what was invoiced and ordered?
Which exceptions need a person today, and what evidence will they need?
Which party’s document is authoritative for this fact?

From presentation to examiner decision

01Presentation receivedDocuments from several parties
02Fields extractedEach value keeps its source location Control point
03Consistency checksQuantities, parties and dates compared Control point
04Case assembledDiscrepancies with linked evidence
05Examiner decidesQualified person makes the determinationOwner: Documentary checker Control point

Architectural illustration, not a product screenshot or client deployment.

Documentary credit presentationIllustrative example · Not client work

Commercial invoice

Quantity2,400 units

Bill of lading

Quantity2,200 units

Discrepancy: 200 units. Both source passages are linked; no other document resolves the difference.
Rules applied: The credit’s terms and the bank’s examination policy.
Next step: Routed to an authorised documentary checker, who makes the decision.

Illustrates a design approach. It is not a final determination under UCP 600, which remains with qualified trade-finance examiners, and it does not describe a production deployment or client work.

02Applications

Where ARVEC’s expertise applies

Application examples, not case studies or client engagements.

Documentary credits: Presentations checked for consistency, with each discrepancy linked to its source passages for an examiner.Document & Knowledge Intelligence Ontology & Knowledge Engineering
Invoices and transport documentation: Parties, goods descriptions, quantities and dates compared across invoices, bills of lading and packing lists.Document & Knowledge Intelligence
Shipment and quantity reconciliation: Ordered, invoiced and shipped quantities reconciled, with differences explained rather than averaged away.Data & Risk Analytics Ontology & Knowledge Engineering
Exception triage and operational knowledge: Exceptions classified and assembled into reviewable cases using a shared model of shipments, parties and documents.Ontology & Knowledge Engineering AI Architecture & Engineering

03Context

Automation stops where legal significance begins

Examination of documents under a documentary credit, customs declarations and other legally significant determinations remain with qualified people. AI can make those decisions faster to prepare and easier to evidence; it should not make them silently.

Electronic trade documents are changing the form in which this evidence arrives. The architectural principles—provenance, consistent meaning and explicit review—remain the same.

04Prior experience

Selected experience of our founders. Prior professional experience of ARVEC’s founders, before ARVEC AI’s incorporation. Not ARVEC client engagements; organisation and product names omitted.

Production document intelligence

International trade and logistics · Prior research and engineering role

Architected and deployed multimodal document-intelligence models across trade and transport documentation, integrating them into live operational workflows. The work connected document interpretation, structured information and validation requirements within a commercial shipping platform.

Prior experience of Utpal Kant

Semantic architecture and data reliability

Container shipping · Prior research and engineering role

Developed a shipping-domain ontology and applied semantic relationships and validation rules to document and operational information. Also developed and operationalised a confidence model for estimated arrival and departure data drawn from heterogeneous shipping sources.

Prior experience of Utpal Kant

Both examples describe work within the same previous professional role.

05Related insights

International Trade, Logistics & Supply Chain

Which decision would you need to defend?

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