The context layer for business metrics in the AI era
The missing layer between
AI agents and your metrics.
Data warehouses explain how a number is computed. ClariLayer captures what it means, who owns it, and whether your AI agents should trust it.
Works with your stack
Your data stack has a missing layer.
Warehouses store data
Snowflake, Databricks, and BigQuery can compute any number. But they can’t tell you which definition is approved or who owns it.
Semantic layers execute queries
dbt, Cube, and Unity Catalog translate logic into calculations. But they can’t capture the business context behind them.
The context gap
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.
Define. Validate. Govern. Ship.
ClariLayer manages the full lifecycle of metric logic — from initial definition to governed deployment.
Define
Business users describe metrics in plain language or import existing SQL. AI guides them into governed, structured definitions.
Validate
Probe your live warehouse to verify logic against real data. No guesswork. No fake confidence scores.
Govern
Tier-based approval workflows. Version control. Immutable release bundles. Full conversation audit trail.
Ship
Automated PRs for engineering. Direct deployment to semantic layers. A canonical API that AI agents and BI tools query.
Your AI agents are making decisions on definitions that were never approved.
Most AI failures aren't due to model logic, but semantic ambiguity. Without a context layer, agents compute correctly but act on the wrong definition.
SELECT sum(total_amount) FROM raw_orders
WHERE status = 'completed'
AND date > current_date - 30;
// Result: Ambiguous. Does 'completed'
// include partially paid? Is tax included?
// Which of 3 revenue definitions was used?
GET /api/v1/metrics/net_revenue_growth
{
"name": "Net Revenue Growth",
"owner": "Finance Team",
"version": "2.1.0",
"status": "approved",
"validated": "2026-03-15",
"tier": "financial"
}
// Governed. Versioned. Traceable.
// Matches the CFO dashboard exactly.
Built for the people who own the numbers.
Whether you're connecting AI agents, defining revenue metrics, or reviewing release bundles — ClariLayer fits your workflow.
AI & Data Leaders
VP of AI, CDO, Head of BI
Connect your AI agents to a context layer. Every agent decision grounded in governed, approved metric logic.
Business Operations
RevOps, Finance Ops, Marketing Ops
Define metrics in plain language. No SQL required. No two-week Jira backlogs. Governed at the speed of business.
Analytics Engineers
Analytics Engineer, BI Architect
Review pre-validated PRs instead of raw requests. Set guardrails, not bottlenecks. Your CI/CD workflow, respected.
Not a catalog. Not a semantic layer.
The context layer.
| 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.
Built by operators who lived the problem
Built by an operator who spent years at high-growth technology companies watching metric drift derail executive decisions. ClariLayer exists because someone got tired of the 45-minute audit.
Stop hallucinating. Start governing.
ClariLayer is in private beta. Request early access for your team.