The Context Layer
Warehouses compute numbers. Semantic layers execute queries. But no tool captures the business context — meaning, ownership, trust — that AI agents need to act responsibly.
Request Early AccessDatabricks, Snowflake, and BigQuery can compute any number. But they cannot tell you which definition is approved or who owns it.
dbt, Cube, and Unity Catalog translate logic into calculations. But they cannot capture the business context behind them.
What the metric means. Who approved it. Which version is current. When to use it. This context lives nowhere in the modern data stack — until now.
Five capabilities that give AI agents, BI tools, and human teams the governed context behind every metric.
The formal interface between your AI agents and your governed metric logic. Trust signals, ownership, and version history — in one query.
Learn moreBusiness users describe metrics in natural language. AI structures them into governed definitions — no SQL, no Jira ticket.
Learn moreProbes your live warehouse to verify metric logic against real data before it reaches a dashboard or AI agent.
Learn moreTier-based approval workflows, immutable release bundles, and conversation audit trails. The paper trail AI agents need to trust a metric.
Learn moreA searchable catalog of every governed metric in your organization, with overlap detection and ownership at a glance.
Learn more| Capability | Data Catalog | Semantic Layer | ClariLayer |
|---|---|---|---|
| Explains how a number is computed | |||
| Captures who owns it and which version is approved | |||
| AI-assisted metric authoring | |||
| Warehouse-backed validation | |||
| Governed release pipeline (PRs, bundles) | |||
| Contract API for AI agents | |||
| Conversation audit trail (the “why”) |
Data catalogs and semantic layers cover only a fraction of these capabilities.
Join the companies building a trusted context layer for their AI agents and business teams.
Request Early Access