Features
Natural language querying of databases
Context management suite (encode definitions, metrics, policies)
SQL pattern matching and validation
Semantic model alignment (metrics, dimensions, definitions)
Multi-source context ingestion (Notion, Slack, email, etc.)
Golden source verification (KPI-verified, SQL-matched, definition-applied)
Full data lineage tracking (source to model to metric to answer)
Usage signals (query frequency, dashboard integration)
Role-based access control (RBAC) and row-level security
Embeddable agent frontend
AI dashboards and email scheduling
Observability and evaluation suite
Model Context Protocol (MCP) integration
Credit-based consumption pricing
Natural language querying with transparent SQL/Python
Reactive notebooks with auto-updating cells
Governed context layer for consistent metric definitions
Skills library for reusable analytic logic
Rules engine for custom AI behavior
Metrics catalog for consistent definitions
Interactive boards with auto-refresh
Embed analytics via Slack, API, or iframe
AI chat sidebar for quick edits
Collaborative notebooks with version control
Verified board approval workflow
Restrict datasource access to specific users
Request access button for private conversations
Email notifications on share actions
