Registry
A searchable, filterable registry of governed metric definitions with owner, lifecycle state, validation recency, versions, relationships, and overlap detection visible at scan speed.
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 governed definition, inspect its trust signals, and follow the evidence trail before reusing it.
Browse all metrics by name, concept category, governance tier, owner, lifecycle status, validation recency, and version. Filter and sort to find exactly what you need.
Drill into any metric for the full definition, SQL logic, conversation audit trail, validation evidence, version history, release/draft diff context, related metrics, and governance information.
Scored name and description matching automatically surfaces 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, validation recency, 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, lifecycle state, or authored business intent. Owner and trust context stay visible beside the governed definition.
Every metric shows its owner, governance tier, approval status, validation date, lifecycle state, and version at a glance. Know who to ask and whether to trust it.
Scored name and description matching surfaces potential duplicates before you create them. Prevent metric sprawl before it reaches dashboards or agents.
Track how metrics relate: variant_of, replaces, conflicts_with. Understand the full landscape of definitions for any business concept.
Version history stays visible, and release/draft diffs help reviewers see what changed before the next version ships.
Join the companies building a trusted context layer for their AI agents and business teams.
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