Metric Studio

Business users define metrics. AI structures them.

Describe what you want to measure in plain language. An AI guide asks the right questions, checks for conflicts, and produces a governed definition — no SQL required, no Jira ticket needed.

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NATURAL LANGUAGE"Monthly churn excluding free-tier"Should refunds count as churn?"No, exclude refunds"✓ Crystallizing definition…Churn Rate (Monthly, Paid)DEFINITION% of paid customers lost, excluding refundsSQL LOGICCOUNT(churned) / COUNT(paid_active) * 100METADATATier 1RevenueDraftConversation audit trail preserved

The engineering bottleneck creates Shadow BI.

The bottleneck

Business users own the metric meaning but cannot update definitions without a Jira ticket and a two-week wait. They understand what needs to be measured — but the tools require SQL, YAML, and Git.

The consequence

People build “Shadow BI” in Excel — ungoverned, unversioned, and invisible. AI agents query raw warehouse tables and guess at definitions. The modern data stack is fast, scalable, and untrustworthy.

From intent to governed definition in minutes

01

Describe

Open Metric Studio and describe what you want to measure in plain language — or paste existing SQL.

02

Refine

The AI asks clarifying questions, checks for overlapping definitions, and suggests the right structure.

03

Crystallize

The conversation becomes a structured definition with all fields populated. Review and edit.

04

Submit

Submit for approval. The metric enters the governance lifecycle and moves through your organization’s workflow.

Key capabilities

Conversational AI authoring

Describe what you want to measure in plain language. The AI asks clarifying questions, checks for conflicts, and crystallizes your intent into a structured, governed definition.

SQL Import

Already have metric SQL? Paste it in. The AI extracts the business logic and structures it into a governed definition — bringing existing work under governance in minutes.

Template accelerators

Start from built-in templates for common patterns — MRR, churn, ARR, NRR, pipeline, and more. Templates pre-seed the AI conversation with domain expertise.

Overlap detection

Before you create a duplicate, the system surfaces related metrics in your organization. Fuzzy name matching and semantic comparison catch conflicts early.

Conversation audit trail

Every AI conversation is preserved as part of the metric’s history. The “why” behind every definition is always accessible — for governance, onboarding, and institutional memory.

Governed freedom, not governed friction

Without ClariLayer

Business user submits a Jira ticket

Two-week wait in the engineering backlog

Engineer guesses at business intent

Definition ships without validation

Drift discovered in a board meeting

With ClariLayer

Business user opens Metric Studio

AI conversation in 15 minutes

Business owner authors with AI guidance

Definition validated against real data

Governed artifact with full audit trail

Ready to close the context gap?

Join the companies building a trusted context layer for their AI agents and business teams.

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