01
Evidence
Trace conclusions to source documents, data and the discrepancies that matter. Design for provenance, verification and explicit uncertainty—not unsupported confidence.
Related expertise
Decision-grade AI · Enterprise architecture · Risk & control
AI decisions you can defend.
In regulated enterprises, a plausible answer is not enough. Decisions need verifiable evidence, consistent business meaning, explicit constraints and accountable human oversight. ARVEC designs and engineers the architectures that bring these requirements together.
Specialist, senior-led AI architecture and engineering for high-scrutiny enterprise decisions.
An AI answer is not yet a defensible decision.
Models can produce fluent outputs while leaving critical questions unanswered: Which source supports the finding? What business rule was applied? Which information contradicts it? Who has authority to approve the next action? Without these answers, deployment can stall at risk, compliance, operational or audit review.
A. Model output
“The documents are inconsistent.”
- Which source?
- Which rule?
- What contradicts it?
- Who may act on it?
B. Decision-grade output
- 01Source citationEach finding links to the passage or record that supports it.Evidence
- 02Domain semanticsTerms resolve to defined business entities, units and relationships.Meaning
- 03Constraint checksApplicable policies and rules are applied and recorded.Control
- 04Exception & uncertaintyContradictions and gaps are stated, not smoothed over.Evidence
- 05Review authorityThe finding is routed to the person entitled to decide.Control
Evidence. Meaning. Control.
02
Meaning
Represent the entities, relationships and domain rules that determine what information means within a business process.
Related expertise
03
Control
Define limits, accountability, approval paths and human oversight so AI-assisted decisions fit the way regulated organisations actually operate.
Related expertise
Our approach
Define what the decision must withstand.
ARVEC uses Argument Vectors to structure five essentials before architecture becomes implementation: Intent, Context, Constraints, Evidence and Authority. The method clarifies what the system must establish, what it may do, and where a person must review or decide. AIF7™ then helps identify the organisational capabilities required to support that design.
- 01Intent
- 02Context
- 03Constraints
- 04Evidence
- 05Authority
Illustrative technical example
A discrepancy is only useful when it can be examined.
Consider a documentary-credit review in which an invoice records 2,400 units and a bill of lading records 2,200. A useful AI assistant should not merely declare a result. It should identify the inconsistency, point to the relevant source passages, apply the appropriate rules and route the finding to an authorised reviewer. The example illustrates a design approach, not a claim of production deployment or client work.
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.
Start with the decision that matters.
01
Assess
Examine a decision or workflow for evidence gaps, semantic ambiguity, control weaknesses and architecture risks.
Typical outputs: Decision map, risk and assumption register, prioritised findings.
02
Architect
Define the target system: evidence flow, knowledge representation, model interfaces, decision constraints, oversight and audit trail.
Typical outputs: Architecture specification and engineering plan.
03
Engineer
Develop or improve the document, knowledge, analytics and control capabilities that the design requires.
Typical outputs: Scoped and validated for each engagement; not a turnkey platform.
Where the question is organisational readiness rather than a single decision, an AIF7™ capability assessment can be added. It is not presumed necessary for every engagement.
Built for consequential enterprise decisions.
Banking & Financial Services
Evidence, model and operational controls for high-scrutiny risk, compliance, financial and knowledge-intensive workflows.
International Trade, Logistics & Supply Chain
Document verification and exception handling across counterparties, rules, systems and physical events.
Cross-Industry Enterprise AI
Selected complex enterprise decisions where evidence provenance, domain meaning and accountable operation are critical.
The people who design the solution stay accountable for it.
ARVEC is a focused, founder-led specialist practice combining enterprise AI architecture with data and risk analytics. Clients work directly with the specialists responsible for understanding the problem and shaping the delivery.
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.
Enterprise AI capability assessment
Banking · Prior independent architecture assignment
Designed and executed an enterprise-wide AI capability assessment spanning technical, product, governance, risk and business roles. Combined structured scoring, capability heatmaps and gap analysis to inform investment priorities and operating-model recommendations.
Prior experience of Utpal Kant
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
Banking reporting and analytics
Banking · Prior data analytics roles
Contributed to reporting automation, ETL pipeline development, dashboard creation and improvement, and KPI analysis and monitoring. Responsibilities also included managing data-product requests to support business reporting needs.
Prior experience of Akanchha Choudhary
Ideas built for scrutiny.
Source-led analysis on evidence-grounded AI, governance, decision architecture, banking and document-intensive operations.