Ground every AI query in verified business definitions, metrics, and relationships. Eliminate hallucinated SQL, accelerate AI agent development 10x, and enforce governance automatically.
DEMO NOWTransforms plain business questions from users or AI agents into optimized SQL using verified semantic reasoning about enterprise metrics, relationships, and definitions
Continuously crawls data warehouses, catalogs, Confluence documents, chat history, and user feedback to build and maintain a living enterprise semantic graph
Automatically detects conflicting metric definitions, semantic drift, and duplicate entities, proposing and applying corrections to maintain data accuracy over time
Participates directly in query planning so AI-generated SQL is grounded in verified business definitions, eliminating hallucinated column references and join errors
Executes queries across warehouses, databases, and lakehouses while enforcing row-level, column-level, and role-based access policies in real time
Exposes the semantic layer via MCP and REST APIs enabling AI coding agents and enterprise copilots to query data without hallucinating structure or relationships
Grounds every AI data query in verified enterprise semantics eliminating incorrect and fabricated SQL outputs
Semantic APIs and MCP connectors compress AI data agent build time from weeks to days
Automated natural language query generation eliminates manual SQL writing for dashboards and analytics
Saves significant time per analytics request through natural language self-service replacing manual SQL workflows - 30+ min/query
Turn operational complexity into measurable performance gains.
