The Context Layer
Warehouses compute numbers and semantic layers execute queries. ClariLayer preserves the intent, validation evidence, approval state, and contract metadata that make a metric safe for people and agents to reuse.
Start FreeDatabricks, Snowflake, and BigQuery can store and compute the raw ingredients. ClariLayer adds the metric lifecycle evidence that says which definition is approved, current, and safe to reuse.
Semantic layers and dbt models are necessary execution surfaces. ClariLayer prepares the governed contract, owner context, and validation state those downstream tools should receive.
What the metric means, who approved it, which version is current, when it was validated, and what it is allowed to power. That evidence needs a durable home before AI agents act on it.
Metric Lifecycle Management
ClariLayer is built around the path a business metric takes from first intent to trusted consumption. A revenue operations lead may know the policy nuance, an analytics engineer may own the warehouse connection, and an AI transformation team may own the agent that consumes the final contract. The product keeps those responsibilities connected without forcing every participant into the same tool or workflow.
That is why the public feature pages connect to one another. Authoring, validation, governance, registry discovery, and API access all preserve the same underlying evidence. A metric definition should not lose its reasoning when it moves from a conversation to a release bundle, or from a release bundle to an AI agent.
The workflow is intentionally neutral about the warehouse and semantic layer underneath it. Catalog browse, validation probes, direct deploy, and rollback are shared across Databricks, Snowflake, and BigQuery. Observe/query-history ingestion remains Databricks-only today. ClariLayer is where the business contract is made explicit: which definition is current, what it is allowed to power, and what evidence proves it has been checked.
A reader can start with the overview, jump into a specific workflow, inspect API details, and return to use cases without losing the thread of the product promise. The public site keeps the path from problem framing to docs, pricing, and use cases short enough for buyers and technical reviewers to verify the claim.
Metric work starts where the business owner has the most context: the conversation about what the number should mean. ClariLayer captures that intent, related definitions, template choices, source assumptions, and the reasoning trail before anyone treats the metric as production logic.
Explore AI-assisted metric authoring in Metric StudioThe next question is whether the proposed logic actually runs against the current warehouse. Validation probes live Databricks, Snowflake, or BigQuery connections for catalog fit, SQL compilation, and bounded data checks so teams can catch broken assumptions before a dashboard or AI agent consumes them.
Explore warehouse validation for live data checksDifferent metrics deserve different rigor. Experimental definitions can move quickly, operational definitions can require validation and owner review, and financial definitions need stronger review. The release artifact preserves approval state, validation evidence, version history, and the human reasoning that explains why the metric changed.
Explore tier-based metric governance and release evidenceOnce a metric is governed, the registry and API give downstream systems a stable contract. AI agents, BI tools, and internal applications can ask for the approved definition, owner, version, policy tier, and trust signals instead of scraping stale warehouse artifacts.
Explore the Contract API for AI agents and BI toolsFive capabilities that attach concrete evidence to every metric before AI agents, BI tools, and human teams rely on it.
A read API for approved definitions, owners, versions, policy tiers, validation state, and trust signals so agents and BI tools do not scrape stale artifacts.
Learn moreBusiness users describe intent, import existing SQL, or start from templates. AI structures the reasoning trail into a definition analytics engineers can review.
Learn moreRuns bounded validation probes against live Databricks, Snowflake, or BigQuery data, then attaches the evidence to the metric version before release.
Learn moreTier-based approvals, immutable release bundles, audit trails, and risk-aware deploy posture keep high-stakes metrics handoff-first.
Learn moreA searchable registry of governed metrics with ownership, lifecycle status, validation recency, variants, and overlap detection at scan speed.
Learn moreEvidence Model
A context layer earns trust by being specific. It should show what was proposed, which warehouse assumptions were checked, who approved the release, which version is current, and what downstream systems should use. Those details matter for humans, and they matter even more when an AI agent is deciding whether to act on a metric.
For a more scenario-led view, read the metric trust and AI governance use cases. For implementation details behind the public contract, start with the Metrics Contract API documentation.
For commercial evaluation, the same evidence model connects to ClariLayer pricing and Free Core usage. For editorial context, the ClariLayer blog on metric governance and AI context expands the use cases into implementation guidance.
The goal is a public path that mirrors the product path: problem, workflow, evidence, integration surface, and trust signals stay connected instead of scattering across isolated pages.
Teams do not only need the SQL. They need the business question, the excluded edge cases, the scope of the metric, and the tradeoffs behind the definition. That context is captured during authoring instead of reconstructed months later.
A metric should say whether it is draft, validated, approved, released, or deprecated. Lifecycle state tells people and agents whether the definition is safe for experimentation, operations, or board-level reporting.
Catalog browse, validation probes, direct deploy, and rollback are shared across Databricks, Snowflake, and BigQuery. Observe/query-history ingestion remains Databricks-only today.
Governed metrics expose ownership, approval context, validation reports when present, immutable release history, and conversation audit trail. That is the evidence an analytics engineer, auditor, or AI agent needs before relying on a number.
ClariLayer does not replace execution tools. It creates the governed contract those tools should receive: SQL logic, business description, source assumptions, release metadata, validation status, and review guidance.
Public pages link from problem framing to feature detail, API documentation, pricing, and editorial context so buyers, champions, and technical reviewers can investigate without starting over.
The distinction is not whether adjacent tools matter. It is where each system is native, partial, or operating as downstream context during the metric lifecycle.
| Capability | Data Catalog | Semantic Layer | ClariLayer |
|---|---|---|---|
| Warehouse and asset discovery | Native | Partial | Uses during lifecycle |
| Metric computation/execution | Not primary | Native | Handoff/API context |
| Business meaning, owner, and approved version | Partial | Partial | Native |
| AI-assisted definition workflow | Not primary | Not primary | Native |
| Live validation evidence | Partial | Partial | Native |
| Governed release bundles, approvals, and audit trail | Partial | Partial | Native |
| Contract API for AI and BI consumers | Not primary | Partial | Native |
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