Data, Memory & Knowledge. Knowledge Graph
A Structured Map of Everything Your Company Knows
A continuously updated knowledge graph that captures entities, relationships, and context across your organisation, enabling multi-hop reasoning no vector search can match.
Overview
What is an organisational knowledge graph?
Vector search finds similar documents. A knowledge graph understands relationships. 'Which clients are at risk because their account manager just left?' requires reasoning across people, accounts, and HR data simultaneously, a query that kills vector RAG but is trivial for a knowledge graph. We extract entities and relationships from your documents, systems, and databases continuously, building a live map of organisational knowledge.
What's included
Entity extraction
AI extracts named entities, people, products, projects, companies, contracts, from every document and system in your data estate.
Relationship mapping
Relationships between entities are inferred from text and structured data, creating a rich network of typed edges that powers multi-hop reasoning.
Entity resolution
Duplicate entities across systems, 'Jane Smith', 'J. Smith', '[email protected]', are resolved to a single canonical node automatically.
Multi-hop reasoning
Answer questions that require traversing multiple relationships: 'Find all projects involving clients headquartered in EU countries with active contracts over $1M.'
Real-time updates
Graph mutations propagate in real time as source documents and systems change. Entity states and relationships are always current.
Graph visualisation
Explore the knowledge graph visually in the browser, zoom, filter by entity type, and traverse relationships interactively.
How it works
From setup to production
Ingest
Connect data sources. AI extracts entities and relationships from documents, databases, and APIs continuously.
Build
Extracted entities are resolved, merged, and linked in the graph store. The initial graph build typically completes within 24 hours.
Query
Query the graph via natural language or graph query language. Multi-hop answers return in under 200ms for most subgraph sizes.
Enrich
As new data arrives, the graph enriches automatically. Custom entity types and relationship schemas can be added without rebuilding.
FAQ
Common questions
Related
More from this service
Enterprise RAG
Combine knowledge graph reasoning with document retrieval for richer answers.
Enterprise Search
Surface entity-level search results from the graph alongside document search.
Data Pipelines
Feed the knowledge graph with fresh, clean data from automated ingestion pipelines.
Get started
Build a living map of your organisation's knowledge
Talk to an expert and get a tailored implementation plan within 48 hours.