About ClariLayer
ClariLayer comes from the day-to-day reality of scaling teams: business leaders need trusted numbers, analytics teams need reviewable evidence, and AI systems need governed context before they act.
Modern data teams have invested in warehouses, semantic layers, transformation workflows, and BI tools. But there is still a gap in the operating system for metrics: the business context behind the number is often not governed with the same discipline as the SQL that computes it.
What does this metric mean? Who approved it? Which version is current? Which downstream systems are allowed to use it? This context lives in meeting notes, message threads, ticket comments, and the heads of senior analysts. It evaporates when people leave and drifts when teams grow.
The cost is practical. A decision meeting slows down while teams audit a slide. A business owner waits for a metric update that should have been reviewable in hours. An analyst recreates a definition that already exists. An AI agent answers confidently from a deprecated framing because nothing told it to stop.
Role-Based Credibility
ClariLayer grounds credibility in the operator roles that feel the metric lifecycle break: executives, business owners, analytics reviewers, and AI platform teams. The proof is the workflow and evidence trail, not borrowed brand marks.
Metric drift shows up in the room where decisions are supposed to happen: revenue reviews, board decks, forecast calls, and operating reviews where leaders bring different answers to the same question.
Analytics engineers need structured evidence before they can approve a change: definition intent, warehouse validation, dependency context, release artifacts, and a rollback-aware path for production systems.
AI teams need a machine-readable contract that tells agents which metric is approved, which version is current, when a definition was validated, and when a request should be refused or escalated.
ClariLayer is Metric Lifecycle Management for business logic. It lets teams define metric intent, validate the logic against live warehouse data, move definitions through approval workflows, and release governed artifacts for downstream systems.
The product is intentionally not a replacement for the warehouse or semantic layer. It is the missing context layer between them and the humans, dashboards, and AI agents that consume their output. The warehouse computes. The semantic layer can serve modeled logic. ClariLayer governs the meaning, lifecycle, trust evidence, and contract that travel with the metric.
That means the proof is inspectable: Metric Studio captures business intent, validation attaches live-data evidence, governance records approvals, release bundles preserve artifacts, and the Contract API exposes owner, version, lifecycle state, and trust signals to consumers.
ClariLayer is designed to sit beside existing warehouses, semantic-layer tooling, dbt workflows, BI surfaces, and AI applications. It produces release bundles, dbt YAML export, PR handoff, and Tier 0 warehouse-view deploy or rollback paths where the live product supports them.
The people who own metric meaning should be able to describe the change, explain the edge cases, and request approval without starting from a blank SQL editor.
Every governed definition needs inspectable proof: owner, lifecycle state, approved version, validation report, approval chain, release artifact, and conversation audit trail.
The Contract API is designed for downstream systems that need more than a metric name. AI agents, BI tools, and internal workflows need lifecycle state, trust signals, and refusal context.
Start a self-serve evaluation if you want to inspect the workflow, or talk with us if your team is planning a guided rollout, Enterprise controls, or design-partner evaluation.