See an enterprise ontology streamline your data — and guard it
Walk through five steps we deliver in every engagement: messy sources → semantic mapping → knowledge graph → purpose-bound policy → an AI agent that answers only with what it's allowed to know. Switch industries and agents to see the same question answered differently.
Illustrative demonstration with fictional data, running entirely in your browser. It shows the approach, not customer results or production performance.
Five deliverables, built on your real data
In a pilot we build exactly this — against your systems, your policies and your AI use case — and measure it against a baseline.
Source & risk inventory
Every system, field and identity key your AI could touch, with sensitive data flagged.
Ontology & mappings
A versioned OWL/SHACL model and declarative mappings with sensitivity on every property.
Knowledge graph
Resolved entities, relationships and provenance in the graph store of your choice.
Policy gate
Role, relationship and purpose policies enforced at retrieval and tool-call time.
Governed agent
GraphRAG or MCP-based agent answering with permitted, evidence-linked facts.
Evaluation report
Correctness, groundedness, leakage tests, latency and cost vs. your baseline.