Models

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What you can model

• Transformations: Clean, join, rename, and reshape raw tables into analysis-ready datasets
• Measures: Define aggregate expressions such as revenue, active users, conversion rate, or retention
• Segments: Define reusable predicates such as active customers, paid accounts, or completed orders
• Dimensions: Document model columns and choose a default time column for time-based analysis
• Shared business logic: Give every person and AI workflow the same source of truth

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Create a model with AI

1. Open Models from the main navigation.
2. Describe the dataset or metric you want to model.
3. Submit the prompt and review the model Basedash creates.
4. Run the SQL to preview its rows, then refine any details, columns, measures, or segments.

Create a customer activity model and define active users as people who completed at least one session in the last 30 days.

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Model editor

• Details: Set the model name, SQL reference name, description, data source, and verification status
• Columns: Document inferred output columns and select a default date column
• Measures: Add named aggregate SQL expressions, such as SUM(monthly_amount)
• Segments: Add named SQL predicates, such as status = 'active'

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Choose the right grain

• A monthly_recurring_revenue measure with SUM(monthly_amount)
• An active_subscriptions segment with status = 'active'
• started_at as its default time column

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Query a model

SELECT
  DATE_TRUNC('month', started_at) AS month,
  SUM(monthly_amount) AS monthly_recurring_revenue
FROM models.subscriptions
WHERE status = 'active'
GROUP BY 1
ORDER BY 1;

SELECT
  customers.plan,
  COUNT(DISTINCT activity.user_id) AS active_users
FROM models.customer_activity AS activity
JOIN models.customers AS customers
  ON activity.user_id = customers.id
WHERE activity.occurred_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY 1;

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How AI uses models

• Create or update a complete model from a plain-language request
• Query models when answering questions or building visualizations
• Reuse measure expressions in SELECT and segment expressions in WHERE
• Prefer verified models when multiple models could answer a question
• Inspect model SQL and metadata before editing or using it

Show monthly recurring revenue for active subscriptions by plan.

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Verification and version history

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Permissions

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Best practices

• Keep model output at the most reusable grain.
• Put aggregates in measures and reusable predicates in segments.
• Use stable reference names because queries address models through them.
• Describe the business meaning of models, columns, measures, and segments.
• Set a default time column when a model supports time-based analysis.
• Verify models that represent approved business logic.
• Keep each model within one data source and its native SQL dialect.

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FAQ