Ontology & Knowledge Engineering

The same word means different things in different systems.

Terms such as quantity, counterparty, exposure or shipment date can carry different definitions across documents, systems and teams. Language models do not resolve those differences on their own. Without an explicit model of entities, relationships and rules, outputs can be fluent but inconsistent—and inconsistency is difficult to defend.

The design and engineering work

• Domain ontologies and knowledge models scoped to a decision, not to the whole enterprise
• Controlled vocabularies and definitions agreed with business owners
• Semantic mappings between source systems, documents and the knowledge model
• Validation rules expressed against the model
• Evidence-aware retrieval that uses the model to select and cite sources
• Change governance: ownership, versioning and review of definitions

Illustrative artefacts

• Domain ontology
• Knowledge model
• Semantic mappings
• Evidence-aware retrieval design

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

Where this applies

Typical applications by sector, not descriptions of completed engagements.

Banking & Financial Services

Shared definitions for risk and reporting terms used by analytics and AI assistants.

International Trade, Logistics & Supply Chain

Shipment, party and document relationships used to check consistency across a presentation.

Cross-Industry Enterprise AI

Policy and product knowledge models behind controlled internal assistants.

Boundaries and human review

Definitions belong to the business. ARVEC facilitates and formalises them; accountable owners approve them. Models are scoped to the decisions they serve, to avoid open-ended modelling programmes.