Answers tied to enterprise meaning
Every enterprise holds heterogeneous knowledge, industry standards, enterprise systems, metrics, and business questions, that don’t talk to each other. Semantic Fabric unifies them into one governed, versioned knowledge graph, so answers are grounded, explainable, and trusted.
The semantic fabric connects enterprise ontologies, knowledge graphs, taxonomies, and business relationships so agents understand the meaning behind a prompt and the context around it. Generic AI retrieves documents; ADAM reasons in the language of your business.
Every critical mapping decision is reviewed and approved before it goes live.
Compliance-grade checks verify graph integrity against ontology definitions before anything is published.
Grounding is wired into every build. Teams work in the experience layer, never against the backbone.
Data catalogs focus on metadata; knowledge-graph tools are powerful but technical. The semantic fabric layers industry, enterprise, and metrics ontologies into one governed, reusable knowledge engine built for agent grounding and natural-language querying.
Ontology files are fine for a demo but break in production, and agents cannot depend on raw files at query time. The semantic fabric persists them as a governed, vectorized knowledge graph for reliable reasoning.
Human-in-the-loop approval sits at every critical mapping decision, and a hard validation gate checks graph integrity against ontology and industry rules before anything goes live. If it fails, nothing progresses.
The agent grounding center turns approved ontology into prompts, MCP tools, constraints, and guardrails, and the MCP tool registry feeds the AI gateway, so every agent runs on governed context.
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