Metric lifecycle management for the AI era
The context layer that turns business metrics into governed contracts.
Define, validate, approve and release, then serve governed metric contracts to BI tools and AI agents from one trusted layer.
Metric contract
Net Revenue Growth
- Governed status
- Approved v2.1
- Warehouse proof
- Validated on live data
- Release artifact
- Bundle + dbt YAML export
- Engineering path
- PR handoff or Tier 0 view deploy
// definition, approved version, validation, and lifecycle status included
Benchmark proof: Across 120 metric-definition questions and five stability runs, ClariLayer's governed envelope was about 16x more accurate than documented-schema alternatives and was the only baseline that consistently refused deprecated metric framings.
Agent asks for Net Revenue Growth. ClariLayer returns the approved definition, approved version, validation evidence, and lifecycle status before the warehouse is queried.
Built for the modern data 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 release.
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
Release bundles, dbt YAML export, and PR handoff for engineering. Tier 0 warehouse view deployment and rollback when teams are ready.
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/{id}/contract
{
"name": "Net Revenue Growth",
"lifecycle_status": "RELEASED",
"current_approved_version": {
"definition_version": "v2.1.0"
},
"last_validated_at": "2026-03-15T10:30:00Z",
"open_challenges_count": 0
}
// Governed. Versioned. Traceable.
// Includes definition, validation, and lifecycle status.
Built for the people who own the numbers.
Each role sees a different workflow, but the same proof travels with the metric: intent, validation, approval state, release artifact, and API-ready contract context.
AI & Data Leaders
VP of AI, CDO, Head of BI
Connect agents and copilots to governed business logic before they answer metric questions or trigger downstream workflows.
Contract proof
- Input
- Agent asks for churn, revenue, or pipeline
- Response
- Approved definition, owner, version, trust state
- Guardrail
- Deprecated or draft metrics route to review
AI systems use the governed contract instead of guessing from warehouse names or stale documentation.
Business Operations
RevOps, Finance Ops, Marketing Ops
Turn business intent into a structured metric definition that can be validated and approved without waiting on a raw Jira handoff.
Authoring proof
- Intent
- Plain-language definition and business rules
- Evidence
- Validation report attached before release
- State
- Draft, approved, released, or deprecated
Business owners move quickly while the approval trail keeps high-stakes definitions reviewable.
Analytics Engineers
Analytics Engineer, BI Architect
Review governed release artifacts, validation evidence, and deployment boundaries instead of reconstructing context from incomplete requests.
Release proof
- Artifact
- Immutable release bundle and dbt YAML export
- Path
- PR handoff or Tier 0 warehouse view deploy
- Recovery
- Version history, audit trail, rollback context
Engineering keeps control of rigor, connectors, and release safety without becoming the bottleneck for every metric question.
Catalogs and semantic layers are necessary.ClariLayer adds lifecycle context.
The distinction is not whether adjacent tools matter. It is where each system is native, partial, or operating as downstream context during the metric lifecycle.
| Capability | Data Catalog | Semantic Layer | ClariLayer |
|---|---|---|---|
| Warehouse and asset discovery | Native | Partial | Uses during lifecycle |
| Metric computation/execution | Not primary | Native | Handoff/API context |
| Business meaning, owner, and approved version | Partial | Partial | Native |
| AI-assisted definition workflow | Not primary | Not primary | Native |
| Live validation evidence | Partial | Partial | Native |
| Governed release bundles, approvals, and audit trail | Partial | Partial | Native |
| Contract API for AI and BI consumers | Not primary | Partial | Native |
Warehouse and asset discovery
Metric computation/execution
Business meaning, owner, and approved version
AI-assisted definition workflow
Live validation evidence
Governed release bundles, approvals, and audit trail
Contract API for AI and BI consumers
Proof over borrowed logos
The proof is the governed metric artifact.
Credibility starts with the workflow ClariLayer makes inspectable: define the metric, validate it, approve it, release it, and serve the contract to AI and BI systems.
Operator-built
Designed around the meeting where nobody trusts the number.
ClariLayer is built from the operator pattern behind metric disputes: business teams own meaning, analytics teams own rigor, and executives need one approved answer before decisions move.
Product evidence
Every released metric carries owner, version, validation, and release state.
The public product path is not a logo wall. It is a governed artifact path: Metric Studio, live validation, approval history, release bundles, dbt YAML export, and API-ready contract context.
Benchmark proof
Governed context beat documented-schema alternatives in Trust Benchmark runs.
Across 120 metric-definition questions and five stability runs, ClariLayer's governed envelope was about 16x more accurate than documented-schema alternatives and was the only baseline that consistently refused deprecated metric framings.
Stop hallucinating. Start governing.
Want a guided rollout, Enterprise Controls, or committed credits? Tell us what you want to govern.