Metric Studio
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.
Start FreeBusiness 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.
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 — or paste existing SQL.
The AI asks clarifying questions, checks for overlapping definitions, and suggests the right structure.
The conversation becomes a structured definition with all fields populated. Review and edit.
Submit for approval. The metric enters the governance lifecycle and moves through your organization’s workflow.
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 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 conflicts, and crystallizes your intent into a structured, governed definition.
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.
Start from built-in templates for common patterns — MRR, churn, ARR, NRR, pipeline, and more. Templates pre-seed the AI conversation with domain expertise.
Before you create a duplicate, the system surfaces related metrics in your organization. Fuzzy name matching and semantic comparison catch 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 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
• 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.
Start Free