E01 · Expertise

AI Architecture & Engineering

Design production-oriented AI systems where evidence flows, model behaviour, integration boundaries, evaluation, observability and accountable operation are considered together.

Discuss an Architecture Review

Led by Utpal Kant

01The problem

Most AI systems are designed around the model, not the decision.

Prototype architectures typically connect a model to data and an interface, then add controls when review begins. In a high-scrutiny setting that order is reversed: evidence flows, permitted actions, evaluation criteria and audit requirements should shape the architecture from the start. Retrofitting them late creates rework and delays approval.

02What ARVEC does

The design and engineering work

  • Reference and target architectures for AI-assisted decisions, including retrieval, extraction, model and agent components
  • Evidence flow and provenance design, from source systems to the recorded decision
  • Control points: input validation, policy and constraint checks, escalation and human review
  • Evaluation design: test sets, acceptance criteria and regression checks agreed with risk owners
  • Observability and audit logging for day-to-day operation and later review
  • Integration and API design with existing enterprise systems and access controls

Where control sits in an AI-assisted decision

  1. 01SourcesDocuments, records and systems of record
  2. 02Evidence retrievalSelected passages with provenance Control point
  3. 03Model or agentDrafts a finding within permitted actions
  4. 04Constraint checksPolicy and rule evaluation, logged Control point
  5. 05Human reviewAuthorised reviewer decides Control point
  6. 06Decision recordEvidence, rules and reviewer retained Control point
Architectural illustration. Control points are agreed with the client's risk and technology owners.

03Typical outputs

Illustrative artefacts

  • Reference architecture
  • Data and knowledge flow
  • Control points
  • Evaluation plan
  • Implementation design

Examples of what an engagement can produce. Scope, deliverables and responsibilities are agreed in writing for each engagement; outcomes are not guaranteed.

04Applications

Where this applies

Typical applications by sector, not descriptions of completed engagements.

Banking & Financial Services
Controlled knowledge retrieval for policy and procedure questions, with cited sources and restricted actions.
International Trade, Logistics & Supply Chain
Document-checking assistants that flag discrepancies for examiners rather than deciding outcomes.
Cross-Industry Enterprise AI
Internal decision-support tools whose outputs must be logged and reviewable.

05Responsibilities

Boundaries and human review

ARVEC designs the architecture and can engineer it with the client’s teams. The client retains ownership of production systems, risk acceptance and model approval under its own governance; security and infrastructure choices are made with its technology function.

How this connects to our Approach

06Prior 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.

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

AI Architecture & Engineering

Start with the decision this work needs to support.

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