Transform raw data and define reusable measures and segments. Basedash AI builds the model, then every AI workflow can use it.
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Models
Customer activity
models.customer_activity
Subscriptions
models.subscriptions
Qualified pipeline
models.qualified_pipeline
Product usage
models.product_usage
Customer activity
One row per customer event with account and plan context.
01 select e.user_id, e.occurred_at, e.event_name,
02 a.plan, a.region, a.created_at as account_created_at
03 from events e join accounts a on a.id = e.account_id
04 where not a.is_internal
reference
customer_activity
measures
3
segments
4
Query the same model from every chart, dashboard, agent, and SQL workflow.
select subscription_id, account_id,
started_at, status, plan,
monthly_amount
from subscriptions
where not is_test
- Measure
- mrr
- sum(monthly_amount)
- Segment
- active
- status = 'active'
select date_trunc('month', started_at) as month,
sum(monthly_amount) as mrr
from
models.subscriptions
where status = 'active'
group by month
Transformation, semantics, governance, and AI creation in one product.
Transformation and semantics
Each model pairs reusable SQL with documented dimensions, measures, segments, and a default time column. It's a first-party alternative to dbt plus Cube, with no separate service to run.
AI context
- Chat Answer questions with shared business logic
- Dashboards Keep every report on the same measures
- Insights Spot trends using consistent segments
- Automations Schedule reports from trusted models
Governance
- Every SQL or semantic edit creates a restorable version
- Admins verify approved models for people and AI
- See every chart and automation that depends on a model
- Existing data-source access controls carry through
Create clean datasets with the measures and segments every team needs.
Models
3
Subscriptions
RevenueOne row per subscription with account, plan, status, and billing details.
Customer activity
GrowthClean customer events joined with account and plan attributes.
Product usage
ProductFeature usage at the event grain with documented product dimensions.
Models, answered.
What are Basedash Models?
Basedash Models are reusable SQL datasets with semantic metadata. Each model belongs to one data source and can include documented dimensions, aggregate measures, reusable segments, a default time column, verification, and version history.
Can AI create a complete model?
Yes. Describe what you want to model, including business concepts such as active users or retention rate. Basedash AI can inspect your schema and create the model SQL, column descriptions, measures, segments, and default time column, then explain what it built.
Can Models handle data transformation?
Yes. A model can clean, join, rename, and reshape warehouse data into a reusable dataset. Row-grain output is usually the most flexible because measures and segments can then adapt it to many analyses, while fixed pre-aggregated output remains available when you intentionally need it.
What are measures and segments?
Measures are reusable aggregate SQL expressions, such as total revenue or active users. Segments are reusable predicates, such as active customers or completed orders. Basedash AI places measure expressions in SELECT and segment expressions in WHERE using normal SQL.
How do I query a model?
Query a model like a read-only virtual table using models.<reference_name>, such as FROM models.subscriptions. Join, filter, aggregate, and group it with the native SQL dialect of its data source.
How are Models different from skills?
Models contain executable SQL and structured semantic metadata. Skills are reusable prose instructions for AI behavior. Use Models for datasets, measures, and segments; use skills for broader analysis methods, preferences, and business guidance.
Can Models replace dbt or Cube?
Models provide first-party transformation and semantic modeling inside Basedash, so teams can create reusable datasets, measures, and segments without operating a separate dbt or Cube deployment. They use the data source's native SQL and are available to every Basedash AI workflow automatically.
Can Models be audited and verified?
Yes. SQL and semantic changes create restorable model versions, and the Used by view shows dependent models, charts, variables, and automations. Organization admins can verify approved models, and semantic changes remove verification until the new version is reviewed.