Validation
ClariLayer probes your live warehouse to prove metric logic works against real data — before it hits a dashboard or AI agent. This is what separates a context layer from a wiki.
Request Early AccessCatalogs and wikis document what should be true. But documentation is never in sync with the actual SQL running in the warehouse. It diverges silently.
Upstream schema changes, column renames, and data type shifts break metric logic without warning. No one catches it until the board meeting.
We execute metric logic against your actual data within a bounded test window. Validation evidence is attached to every metric version. No guessing.
Automated checks for null values, uniqueness violations, compilation errors, and data type mismatches. Catches the obvious failures before anyone sees them.
Validation is restricted to a 30-day data window to keep costs low and execution fast. Enough to prove the logic, not enough to break your budget.
Financial-tier metrics cannot be promoted without a passing validation report. Experimental metrics get lighter checks. The rigor matches the stakes.
Every metric version carries its validation report. Auditors, AI agents, and team members can see when it was last proven against real data.
Validates metric logic against your actual Databricks, Snowflake, or BigQuery data. Not assumptions, not documentation — real execution against real tables.
Probes for null values, uniqueness violations, compilation errors, and data type mismatches. Catches problems before they reach production.
Experimental metrics can ship with lighter checks. Financial-tier metrics cannot be promoted without a passing validation report. Different rigor for different stakes.
The Switzerland of metric governance. Works across Databricks, Snowflake, and BigQuery without locking you into a single vendor’s ecosystem.
Join the companies building a trusted context layer for their AI agents and business teams.
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