Domain Knowledge Graphs
AI without hallucinations. Constrained to your approved concepts, terminology, and relationships.
“There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy.
The problem with generic AI
Generic AI knows everything and nothing. It will confidently discuss concepts that don't exist in your domain, create relationships that aren't allowed, and use terminology that isn't yours.
For regulated environments, this is unacceptable.
AI invents terms that don't exist in your domain
AI creates connections between unrelated things
AI uses industry-generic terms instead of yours
No way to trace how AI reached its conclusions
Our solution: domain ontologies
We build domain-specific ontologies: structured maps of your concepts, relationships, and allowed attributes. The AI is constrained to operate within these boundaries.
Constrained vocabulary
AI can only reference concepts in your approved ontology. Your language, your definitions, enforced across all interactions.
Multi-hop reasoning
Navigate complex relationships: “this links to that, which impacts these services.” Relationship-aware retrieval for accurate answers.
How we build Knowledge Graphs
A systematic approach to creating domain-specific ontologies that power accurate AI.
Define your ontology
Start with a use-case ontology (concepts, relations, and allowed attributes) and version it for governance.
For a government policy system: define 'Policy', 'Regulation', 'Agency', 'Compliance Requirement' as nodes; 'administers', 'supersedes', 'requires' as relationship types.
Ingest your corpus
Extract text and metadata, chunk intelligently, and attach stable source IDs for traceability.
Parse 500+ policy documents, cabinet papers, and legislation into semantic chunks, each tagged with document ID, section reference, and effective date.
Extract candidates
Use LLM and classic NLP/ML tools to identify entities, claims, and relationships from your content.
Identify that 'Privacy Act 2020' is Legislation, 'MBIE' is an Agency, and extract the relationship 'MBIE administers Privacy Act 2020'.
Validate and normalise
Map candidates to your ontology, deduplicate, and resolve conflicts systematically.
Merge 'Ministry of Business, Innovation and Employment' and 'MBIE' into a single canonical entity; reject 'Ministry of Innovation' as unrecognised.
Persist and expose
Store in the evidence store and expose only through gateway verbs for controlled access.
Store in Azure Cosmos DB with Gremlin API; expose via /query/related-policies and /query/compliance-path gateway endpoints only.
What “done” looks like
A production-ready knowledge graph that powers accurate, auditable AI responses.
GraphRAG-style retrieval
Combine graph structure with vector search for more accurate, relationship-aware answers.
e.g. Query: 'What affects procurement rules?' traverses Policy → Regulation → Agency relationships, not just keyword matches on 'procurement'.
Structured answers
Outputs that conform to your schema, not free-form text that might drift from your standards.
e.g. Response includes policy_id, effective_date, administering_agency as typed fields, not free-form prose that varies each time.
Quality gates
Automated checks ensuring accuracy within your defined cost and performance envelope.
e.g. Automatically flag if a cited policy doesn't exist in the graph, or if a claimed relationship type isn't defined in the ontology.
Version control
Full governance with rollback capability. Know exactly what version of the ontology was used.
e.g. Diff shows: v2.3 added 'Digital Identity Trust Framework' as new Regulation under the Privacy Act branch, effective 1 July 2024.
