A semantic layer over everything your company knows
Unify docs, chats, tickets, CRM records, and system state into a queryable graph. Agents traverse relationships to answer questions humans ask in whole sentences — not keyword queries.
Most enterprise knowledge is locked inside a dozen disconnected tools. A knowledge graph unifies them into a typed semantic model — entities, relationships, temporal facts — so agents can reason over what's true, not just what's indexed.
From sources to semantic layer
- 01
Ingest every source
Connectors for Confluence, Notion, Slack, Drive, SharePoint, Salesforce, ServiceNow, Jira, and your product databases. All content is entity-resolved and linked.
- 02
Resolve and enrich
People, projects, accounts, and artifacts are merged across sources. Aliases collapse, ownership is attributed, and timestamps track when each fact became true.
- 03
Query with reasoning
Agents translate natural language into graph traversals and SQL, combining structured and unstructured context to answer questions no single system could.
What the graph unlocks
Multi-hop question answering
'Who owns the service that handles GDPR exports for our EU customers?' — one query, three hops, cited answer.
Temporal reasoning
Every fact is time-stamped. Ask 'what was X's status last quarter?' and get a point-in-time answer, not today's view.
Access-aware retrieval
The graph respects source-system permissions. Agents never surface content a user wouldn't be allowed to see in the underlying tool.
Schema-guided extraction
Your domain ontology guides extraction from unstructured text — 'contract', 'SOW', and 'MSA' converge on the same entity instead of three noisy clusters.