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The fastest path uses local connectors: no server, no network. Pick either SQLite or DuckDB.
canonic setup walkthrough on the rental example

canonic setup end-to-end on the vehicle rental example.

See the Rental guide for the full walkthrough behind this recording.
Don’t have a database handy? The Guides section ships 5 ready-to-run sample projects (dbt Jaffle Shop, e-commerce, rental, SaaS analytics, Dutch railway), a good way to try the wizard before pointing it at your own data.
Point at a local .db file:

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What the wizard does

1

Name your project

Give your canonic project a name.
2

Connect a source

SQLite/DuckDB file, or Postgres, MySQL, ClickHouse, Redshift, Snowflake or Databricks if you have a server or account.
3

Configure an LLM (optional)

Skippable: the core works without one. See Configuring an LLM.
4

Bootstrap

canonic introspects the schema and drafts your semantics.
5

First answer

The wizard runs a real query against your data and shows the result, plus how fresh it is and which definition it used.
Connecting to Postgres, MySQL, ClickHouse, Redshift, Snowflake or Databricks, or configuring an LLM provider, needs a credential. Set the corresponding environment variable before running canonic setup: canonic never stores secrets in canonic.yaml directly, only a credentials_ref like env:WAREHOUSE_PASSWORD pointing at it:
See Configuring an LLM and canonic connection for the full credential reference formats (env:, keyring:, file:).

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Ask your own questions

You now have a working context layer committed to your repo:
Using your own data instead of a guide? Replace revenue with a metric that actually exists in your project. Run canonic status to see what’s available. For a query with more filters or joins than is comfortable inline, write a SemanticQuery JSON file and pass it with -f instead. See canonic query for the full flag reference.

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Review what canonic drafted

It’s all an ordinary git diff:

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Out of the box vs. a bit more effort

Works immediately, zero modeling:
  • Connect a database (SQLite, Postgres, Snowflake and others) and canonic introspects the live schema.
  • It auto-drafts semantics: typed columns, primary-key grains, foreign-key joins, and additive measures (sums, counts).
  • The setup wizard ends by answering a real question from your data, so you see the payoff in minutes.
  • Connect your agent over MCP and start asking.
A bit more manual effort (when you need it):
  • Canonical bindings: when two sources define “revenue” differently, you pick the authoritative one. canonic surfaces the ambiguity. You resolve it once.
  • Knowledge prose: the business “why” behind a definition. canonic drafts it, you refine it.
  • Guardrails & contracts: mandatory filters, required dimensions, final-vs-provisional rules. Added when a number needs protecting.
  • Non-additive metrics: ratios, averages, distinct counts, balances. Declared as composable definitions so they stay correct at any grain.
  • More sources: dbt / Metabase / Notion / web pages, layered on as context evidence. The connector contract is extensible, so a Confluence, Jira, or other wiki/knowledge-base connector can be added the same way.
The design principle throughout: canonic proposes, you approve. It never silently edits your context: every change is a reviewable diff.
See the end-to-end example for the full loop in one place: ingest → review → apply, what happens when your data actually changes, defining a canonical metric and guardrail by hand, and running the same loop headless in CI.