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.
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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.
FAQ
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.
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