What ClariLayer Does (And What It Does Not)

We get asked the same question in almost every conversation: "So is ClariLayer a data catalog? A semantic layer? A metrics store?" The answer is no to all three. ClariLayer is a new category — a context layer — and this post explains exactly what that means.

What ClariLayer is not

Not a warehouse. Snowflake, Databricks, and BigQuery store your data and compute your numbers. ClariLayer does not touch your raw data. It captures the meaning and governance around the metrics those warehouses produce.

Not a semantic layer. dbt, Cube, and Unity Catalog translate business logic into consistent SQL queries. ClariLayer does not replace this translation. It adds the business context that these tools were never designed to capture: who owns this metric, what version is approved, and whether an AI agent should trust it.

Not a data catalog. Catalogs index tables, columns, and lineage. ClariLayer governs business metric definitions — the human-readable meaning, approval workflows, and trust signals that catalogs do not track.

Not a wiki. Confluence pages and Notion docs are where metric definitions go to die. They drift from reality because there is no connection between the documentation and the warehouse execution. ClariLayer validates definitions against your actual data.

What ClariLayer is

ClariLayer is the context layer for business metrics. It sits alongside your warehouse and semantic layer, capturing five things that no other tool records:

Meaning. A plain-language business definition that anyone can understand, authored by the people who own the metric — not by engineers guessing at intent from SQL.

Ownership. Every metric has a named owner who is accountable for its accuracy. Not a team, not a channel — a person.

Approval. Tier-based governance that matches rigor to stakes. Experimental metrics ship fast. Financial metrics require multi-approver review. The rigor matches the consequences of getting it wrong.

Validation. Integrity probes that check metric logic against your live warehouse data. Not documentation that says the metric should work — evidence that it actually does.

Trust signals. When an AI agent or BI tool queries ClariLayer, the response includes everything needed to make an informed decision: approved or draft, last validated date, governance tier, version number, and the full approval chain.

How it fits into your stack

ClariLayer is platform-neutral by design. It works with any warehouse (Snowflake, Databricks, BigQuery), any semantic layer (dbt, Cube, Unity Catalog), and any AI framework or BI tool. We are not replacing anything in your stack. We are adding the layer that was always missing.

The flow is simple: your AI agents and BI tools query ClariLayer first to get the governed definition and trust signals. Then they query your data stack with the confidence that they are using the right metric. ClariLayer is queried first, your warehouse is queried second.

Five capabilities, one mission

Everything in ClariLayer serves a single mission: make business metrics trustworthy for both humans and AI agents. We do this through five interconnected capabilities.

The Contract API gives AI agents a formal interface to query governed definitions. Metric Studio lets business users define metrics in natural language, with AI structuring the result. Warehouse-backed validation probes your live data to verify metric logic. Tier-based governance enforces the right level of rigor for each metric. And the Metric Registry provides a searchable catalog with overlap detection.

Who this is for

ClariLayer is built for organizations that have invested in their data stack but still struggle with metric trust. If your board meetings spend more time auditing numbers than making decisions, if your AI agents are acting on ungoverned definitions, or if your analysts keep reinventing metrics that already exist — this is the missing piece.

We are currently working with design partners to refine the product. If this resonates, we would love to hear from you.