Deterministic metrics
Save the exact SQL for MRR, activation, churn, retention, or any model your company depends on.
Semantic layer
Define metrics once.
Use them everywhere.
Build a semantic layer from reusable SQL definitions for your most important metrics and models. Basedash AI can reference them across chat, charts, dashboards, insights, and automations.
14-day trial. No credit card required.
Data / Production warehouse
Run definition
Definitions
Activation rate
activation_rate
Monthly recurring revenue
mrr
Qualified pipeline
qualified_pipeline
Cohort retention
cohort_retention
Activation rate
Users who complete onboarding within seven days of signup.
01 select date_trunc('week', signed_up_at) as week,
02 count(*) filter (where onboarded_at <= signed_up_at + interval '7 days')
03 / nullif(count(*), 0) as activation_rate
04 from users where email not like '%@basedash.com'
reference
activation_rate
latest week
42.8%
used by AI
Everywhere
Why it matters
Your metrics get a source of truth.
The definitions feature turns repeat SQL into reusable, AI-readable building blocks.
AI-aware by default
Basedash agents see available definitions and can reference them when creating charts, answers, insights, and reports.
Reusable everywhere
Reference definitions inside CTEs with Liquid syntax instead of copying SQL into every dashboard.
Coverage
Every AI workflow can reuse them.
The semantic layer follows the work your team already does in Basedash.
Chat
Ask questions that reuse approved SQL.
Charts
Generate visualizations from trusted models.
Dashboards
Keep every report on the same calculation.
Insights
Spot trends from consistent metric logic.
Automations
Schedule reports with deterministic SQL.
SQL editor
Compose definitions inside larger queries.
Examples
Model the numbers teams trust.
Start with calculations that show up in dashboards, board reports, and chat.
Revenue
Monthly recurring revenue
mrr
Recurring invoice lines, excluding trials and one-time credits.
Growth
Activation rate
activation_rate
Users who complete onboarding within seven days of signup.
Product
Cohort retention
cohort_retention
Weekly retained accounts by signup cohort and plan.
Governance
Centralized metric definitions.
Looker-grade metric governance, owned in plain SQL instead of LookML.
The semantic layer is more than reusable SQL snippets. It is where enterprise teams centralize the metrics the business depends on, control who can change them, and keep an auditable record of every change — the same governance backbone that Looker provides through LookML.
| Governance capability | Looker LookML | Basedash definitions |
|---|---|---|
| Single source of truth | Metrics modeled once in LookML and reused across Explores. | Metrics defined once as SQL and reused across every surface. |
| Centralized ownership | The data team owns the model; changes ship through code review. | Admins own definitions; members can run them but not edit them. |
| Change history and audit | Versioned in Git alongside the rest of the project. | Every edit creates a restorable version with full history. |
| Documented business meaning | Descriptions and labels live inside the model files. | Each definition carries a name, reference, and description. |
| Consistent AI and query logic | Explores constrain how analysts query modeled fields. | AI reuses approved definitions instead of inventing SQL. |
| Reuse surface | Explores, Looks, and dashboards. | Chat, charts, dashboards, insights, automations, and the SQL editor. |
| Access scope | Access grants and access filters scope modeled fields. | Definitions are scoped per data source under workspace roles. |
| Implementation cost | Requires learning and maintaining the LookML modeling language. | Plain SQL — no new modeling language to staff or maintain. |
Migrating from Looker? See how governance continuity maps to a modern BI tool, or read the Looker migration playbook.
Semantic layer, answered.
What is the Basedash semantic layer?
The Basedash semantic layer is powered by definitions: saved SQL queries scoped to a data source. Each definition has a name, reference name, description, and SQL query that Basedash can expand inside other queries.
How does the AI use the semantic layer?
Basedash gives AI agents a catalog of definitions for the data sources they are using. The AI can inspect a definition, reference it in SQL, or create and update definitions when an admin asks for reusable metric logic.
How do I reference a definition in SQL?
Use Liquid syntax like {{ definition("mrr") }} inside a query on the same data source. We recommend placing definitions inside CTEs so the final query stays readable.
How is the semantic layer different from skills?
They are deterministic SQL. Skills are prose instructions for the AI. Use definitions when the calculation itself should be reusable, and skills when the AI needs broader business guidance.
How does the Basedash semantic layer compare to Looker's LookML for governance?
It delivers the same metric governance teams expect from LookML — a single source of truth, centralized admin ownership, documented business meaning, version history, and AI that only reuses approved logic — without a separate modeling language. Definitions are plain SQL, so the data team governs metrics with the language they already use rather than maintaining LookML.
Can metric definitions be audited and version-controlled?
Yes. Every change to a definition's SQL or description creates a new version, and you can review the full history or restore a previous version from query history. Only organization admins can create, edit, delete, or restore definitions, so metric changes stay governed and auditable.
Get started in under 30 minutes
We can help you migrate your data and dashboards from any other tool.