Ontology services that make AI accurate, safe and explainable
Six practices, one goal: a shared, governed model of your business that every AI application can rely on. Engage one, or combine them into a programme.
Ontology Strategy & AI Readiness
Before anyone writes a class, we find out where meaning breaks and where sensitive data leaks. We inventory your AI use cases, the systems they touch and the decisions they influence, then decide — honestly — whether an ontology is the right tool.
Discuss this serviceWhat you get
- Use-case portfolio scored for semantic value and risk
- Sensitive-data exposure map: which agents, prompts and indexes can reach PII, PHI, financial and confidential data
- Current-state review of glossaries, schemas, MDM and access controls
- Target semantic architecture and build-vs-buy guidance
- 90-day roadmap with success metrics
Typical stack
Outputs: Readiness report · exposure map · roadmap
Domain Ontology Engineering
We build modular, standards-aligned ontologies that answer the questions your business and your AI actually ask. Every class earns its place against a competency question, and every module ships with tests.
Discuss this serviceWhat you get
- Competency-question catalogue (scoping & validating)
- Modular OWL 2 ontology with clear upper/domain/application layers
- Alignment to FIBO, FHIR, ACORD, ISA-95, STIX, schema.org as fits
- SKOS taxonomies and controlled vocabularies
- SHACL shapes for data quality and contract enforcement
- Human-readable documentation site and diagrams
Typical stack
Outputs: Versioned ontology · shapes · docs
AI Guardrails & Semantic Access Control
We turn your privacy, security and regulatory rules into knowledge the AI stack can check. Sensitivity, purpose, consent and relationships live in the ontology; a policy decision point enforces them on every retrieval, prompt and tool call.
Discuss this serviceWhat you get
- Sensitivity ontology aligned to the W3C Data Privacy Vocabulary (DPV)
- Purpose- and consent-bound policies expressed in ODRL / SHACL
- Relationship-aware rules (ReBAC / knowledge-based access) for multi-hop questions
- Masking, generalisation and minimisation strategies per class
- Policy gate integrated into vector search, GraphRAG and MCP tools
- Decision logs with provenance for audit and incident review
Typical stack
Outputs: Policy model · enforcement design · audit trail
Knowledge Graph & Semantic Layer
We connect the ontology to the data you already have — without a rip-and-replace. Sources are mapped, entities are resolved and every fact keeps a link to where it came from.
Discuss this serviceWhat you get
- Declarative source mappings (R2RML / RML) or virtualised access
- LLM-assisted extraction from PDFs, contracts and email with human review
- Entity resolution and golden-record linking
- Lineage and provenance with W3C PROV-O
- Semantic APIs (SPARQL, GraphQL, REST) for apps and agents
- Performance-tuned on RDF or property-graph stores
Typical stack
Outputs: Running graph · mappings · APIs
GraphRAG & Agent Grounding
Ontology-aware retrieval gives LLMs the right context and the path that justifies it. We design hybrid graph + vector retrieval, expose the ontology to agents through MCP, and measure quality before you scale.
Discuss this serviceWhat you get
- Hybrid retrieval: graph traversal + vector + keyword
- Ontology-guided query planning and multi-hop reasoning
- MCP servers exposing typed, policy-checked tools
- Answer → fact → source evidence in every response
- Evaluation harness: correctness, groundedness, leakage, latency, cost
- Multi-agent orchestration patterns (LangGraph, CrewAI)
Typical stack
Outputs: Retrieval service · agent tools · eval report
OntologyOps & Governance
An ontology is a product, not a project. We set up the workflow that keeps it correct as the business changes — and the team that runs it.
Discuss this serviceWhat you get
- Git-based versioning, semantic diff and release notes
- CI pipeline: reasoner consistency, SHACL tests, competency-question tests
- Change-impact analysis across mappings, policies and prompts
- Ownership model, review board and contribution guide
- Hands-on training for data, AI and governance teams
- Optional retained support and quarterly model reviews
Typical stack
Outputs: Pipeline · playbooks · trained team
Three ways to start
Fixed scope, agreed acceptance criteria and a clear decision point at the end of each stage. Pricing follows scoping.
Readiness Assessment
Decide where semantics will make a measurable difference — and where they won't.
- Use-case & data review
- Sensitive-data exposure map
- Target architecture
- Prioritised roadmap
Ontology Pilot
One workflow, one domain, measured against a baseline you agree up front.
- Domain + sensitivity ontology
- Working graph & policy gate
- GraphRAG / agent demo on your data
- Evaluation report & scale plan
Production & OntologyOps
Turn the validated approach into a maintained enterprise capability.
- Integrations & access design
- CI/CD for ontology & policies
- Monitoring & audit
- Team training & handover