Use cases: - Engineers: query your data from Claude/Cursor, debug issues, build with analytics in dev flow (like [1] but with memory and observability built in) - Data teams: chat with your DB, define rules for how AI should query, share dashboards and analysis
Works with Postgres, Snowflake, BigQuery, Redshift, and more. Any LLM. Swap or mix instantly
What's different: - Memory – stores context, preferences, usage down to table/column level. Learns over time. - Rules – instructions, terms, guardrails with versioning. Git sync with dbt, markdown, code. - Observability – traces, plans, evals, feedback. See exactly what happened.
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