The Context Layer
Warehouses compute numbers. Semantic layers execute queries. But no tool captures the business context — meaning, ownership, trust — that AI agents need to act responsibly.
Start FreeDatabricks, Snowflake, and BigQuery can compute any number. But they cannot tell you which definition is approved or who owns it.
dbt, Cube, and Unity Catalog translate logic into calculations. But they cannot capture the business context behind them.
What the metric means. Who approved it. Which version is current. When to use it. This context lives nowhere in the modern data stack — until now.
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. The context layer is useful because 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. Databricks, Snowflake, and BigQuery can all participate in catalog browse, validation, direct deployment, and rollback through the shared product path. The context layer 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 should make the critical context layer pages discoverable within a few clicks of the homepage.
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 need owner signoff, 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 give AI agents, BI tools, and human teams the governed context behind every metric.
The formal interface between your AI agents and your governed metric logic. Trust signals, ownership, and version history — in one query.
Learn moreBusiness users describe metrics in natural language. AI structures them into governed definitions — no SQL, no Jira ticket.
Learn moreProbes live warehouse data to verify metric logic before it reaches a dashboard or AI agent across Databricks, Snowflake, and BigQuery. Observe and query-history ingest remain Databricks-only today.
Learn moreTier-based approval workflows, immutable release bundles, and conversation audit trails. The paper trail AI agents need to trust a metric.
Learn moreA searchable catalog of every governed metric in your organization, with overlap detection and ownership at a glance.
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.
ClariLayer works beside existing warehouses and semantic-layer tooling. The shared warehouse path covers catalog browse, validation, direct deploy, and rollback across Databricks, Snowflake, and BigQuery. Observe and query-history ingestion remain Databricks-only today.
Every governed metric carries ownership, approval chain, validation report, immutable release history, and conversation audit trail. That is the evidence an analytics engineer, auditor, or AI agent needs before relying on a number.
The context layer does not replace execution tools. It creates the governed contract those tools should receive: SQL logic, business description, source assumptions, release metadata, and validation status.
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.
| Capability | Data Catalog | Semantic Layer | ClariLayer |
|---|---|---|---|
| Explains how a number is computed | |||
| Captures who owns it and which version is approved | |||
| AI-assisted metric authoring | |||
| Warehouse-backed validation | |||
| Governed release pipeline (PRs, bundles) | |||
| Contract API for AI agents | |||
| Conversation audit trail (the “why”) |
Data catalogs and semantic layers cover only a fraction of these capabilities.
Join the companies building a trusted context layer for their AI agents and business teams.
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