Metric Studio
Describe what you want to measure in plain language, paste existing SQL, or start from a template. An AI guide asks the right questions and turns intent into a definition ready for validation and review.
Start FreeBusiness users own the metric meaning but cannot update definitions without turning nuance into a ticket and waiting for translation. They understand what needs to be measured — but the execution tools usually expect SQL, YAML, and Git.
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.
Open Metric Studio and describe what you want to measure in plain language, paste existing SQL, or choose a template for a common metric pattern.
The AI asks clarifying questions, checks for overlapping definitions, and suggests the right structure, grain, filters, and source assumptions.
The conversation becomes a structured definition with the reasoning trail preserved. Review, edit, and keep the business nuance attached.
Submit for validation and approval. The metric enters the governance lifecycle with owner, policy tier, and evidence requirements visible.
Authoring Workflow
Most metric requests fail before SQL is written because the important context is missing. Which customers are excluded? Which time grain is expected? Is the metric exploratory, operational, or financial? Metric Studio makes those questions explicit while the business owner still remembers the nuance.
The result is not a loose chat transcript. ClariLayer crystallizes the conversation into a structured metric definition, keeps the original reasoning trail, and prepares the artifact for warehouse validation against live data and tier-based approval governance. Business users can move quickly, while analytics engineers inherit the context they need to review safely.
Metric Studio also gives technical teams a practical escape hatch. Existing SQL can be imported instead of recreated, then documented, checked against the registry, validated against warehouse reality, and released through the same governed path as a new conversational definition.
Metric Studio captures the plain-language definition, source table assumptions, filters, grain, owner, policy tier, related metrics, and any template that shaped the conversation.
When a user starts a new churn, ARR, retention, or pipeline metric, the workflow points them back to the Metric Registry so existing definitions and managed variants are visible before another duplicate is created.
SQL Import lets an analytics engineer bring existing logic under governance without retyping the whole metric. The imported SQL becomes context to refine, validate, approve, and release rather than an unmanaged warehouse artifact.
Describe what you want to measure in plain language. The AI asks clarifying questions, checks for related definitions, and crystallizes your intent into a structured metric definition.
Already have metric SQL? Paste it in. The AI uses the SQL as source context, extracts the business logic, and brings existing work into the same validation and governance path.
Start from built-in templates for common patterns — MRR, churn, ARR, NRR, pipeline, and more. Templates pre-seed the AI conversation with required fields and governance constraints.
Before you create a duplicate, the workflow surfaces related metrics in your organization. Scored name and description matching catches conflicts early.
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.
• Business user files a vague metric request
• Two-week wait in the engineering backlog
• Engineer guesses at business intent
• Definition ships without validation
• Drift discovered in a board meeting
• 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
Join the companies building a trusted context layer for their AI agents and business teams.
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