Turn Enterprise Data into Trusted AI Insights with a Self-Healing Semantic Layer - Ground every AI query in verified business definitions, metrics, and relationships. Eliminate hallucinated SQL, accelerate AI agent development 10x, and enforce governance automatically.
Everything you need to make AI interact with enterprise data accurately, securely, and at scale
Natural Language to Trusted SQL - Transforms plain business questions from users or AI agents into optimized SQL using verified semantic reasoning about enterprise metrics, relationships, and definitions
Autonomous Context Discovery - Continuously crawls data warehouses, catalogs, Confluence documents, chat history, and user feedback to build and maintain a living enterprise semantic graph
Self Healing Semantic Model - Automatically detects conflicting metric definitions, semantic drift, and duplicate entities, proposing and applying corrections to maintain data accuracy over time
AI Native Query Execution - Participates directly in query planning so AI-generated SQL is grounded in verified business definitions, eliminating hallucinated column references and join errors
Governed Cross Platform Execution - Executes queries across warehouses, databases, and lakehouses while enforcing row-level, column-level, and role-based access policies in real time
MCP & Agent API Access - Exposes the semantic layer via MCP and REST APIs enabling AI coding agents and enterprise copilots to query data without hallucinating structure or relationships
