Count is a collaborative analytics notebook that integrates directly with dbt, BigQuery, Snowflake, Redshift, and other modern data warehouse connections. It combines SQL, Python, visualizations, and text in a single shareable canvas, designed for data teams that want analysis their entire organization can understand and contribute to. The AI assistant writes SQL from plain-English questions, explains existing queries in plain language for non-technical stakeholders, and can suggest next steps in an analysis. The dbt integration is the core differentiator: Count reads your dbt project metadata, so AI suggestions are aware of your actual data models and column definitions. Growth-stage companies and mid-market data teams use Count as an alternative to Jupyter notebooks or Mode that is more accessible to business stakeholders without sacrificing technical depth.

What the community says

Count has a strong following among data engineers who work with dbt and want a notebook tool that understands their actual data models. Users praise the clean interface and the ability to share analysis with non-technical business stakeholders without dumbing down the analysis. Some users note that the visualization options are more limited than dedicated BI tools like Tableau, and that the tool works best when the team is already using dbt.

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