Registry
A searchable, filterable catalog of every governed metric in your organization — with ownership, trust signals, and overlap detection. Discovery for humans and AI agents alike.
Start FreeThree churn definitions. Five revenue metrics. Nobody knows which is current, which is deprecated, or which was approved for the board.
New analysts create metrics that already exist because there is no authoritative index. The same logic is defined, debated, and approved multiple times across teams.
The metric registry gives every team and every AI agent a single place to find the canonical, governed definition for any business concept in the organization.
Browse all metrics by name, concept category, governance tier, owner, status, and version. Filter and sort to find exactly what you need.
Drill into any metric for the full definition, SQL logic, conversation audit trail, version history with diffs, related metrics, and governance information.
Fuzzy name matching and semantic comparison automatically surface metrics that might be duplicates or variants. Catch conflicts before they cause problems.
Discovery Layer
Search is not a convenience feature when metric definitions become infrastructure. If an analyst cannot find the current retention definition, they create another one. If an AI agent cannot distinguish a deprecated draft from the approved metric, it may act on the wrong business logic. The registry reduces that risk by making status, ownership, relationships, and release history visible in one place.
The registry also creates the connective tissue for the rest of the lifecycle. New definitions from Metric Studio authoring workflows can be checked against existing concepts, while released definitions become discoverable through the Contract API for AI agents and internal tools.
That makes the registry the safest starting point for investigation. Users can confirm whether a metric is current, inspect related definitions, and follow links into validation or governance evidence before they reuse the number.
Teams can see which metric is enterprise-canonical, which definitions are domain-specific variants, and which exploratory versions should not be used for executive reporting.
Relationships such as variant_of, replaces, conflicts_with, and derived_from help reviewers understand how one metric affects another before approving changes.
Owner, policy tier, lifecycle status, validation recency, approval trail, and version history are surfaced together so users do not have to triangulate trust from Slack, docs, and warehouse names.
Registry entries point users into the definition, validation evidence, governance history, and API context. The path from question to evidence stays short for humans and machine consumers.
Find metrics by name, description, category, or intent. AI agents and human teams alike can discover the canonical definition for any business concept.
Every metric shows its owner, governance tier, approval status, validation date, and version at a glance. Know who to ask and whether to trust it.
Fuzzy name matching and semantic comparison surface potential duplicates before you create them. Prevents the metric sprawl that breaks AI agent trust.
Track how metrics relate: variant_of, replaces, conflicts_with. Understand the full landscape of definitions for any business concept.
Every metric carries its full version history with human-readable diffs. See what changed, who changed it, and why — across every iteration.
Join the companies building a trusted context layer for their AI agents and business teams.
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