Nao is an AI powered data IDE for analysts, engineers, and scientists to write SQL, Python, or dbt workflows, preview changes, catch issues early, and deploy confidently. It connects directly to your warehouse and understands your schema so you can build faster, fix fewer bugs, and maintain trust in your data. Build data pipelines, launch quality checks, run analytics, and collaborate across teams without context switching. Think of it as your AI teammate built specifically for modern data work.
About nao
What is nao?
nao is an open-source, AI-powered data IDE designed for analysts, engineers, and scientists who work with SQL, Python, or dbt workflows. It connects directly to your data warehouse, understands your schema, and helps you build, preview, and deploy data pipelines with confidence. Think of nao as an AI teammate that catches issues early, reduces bugs, and keeps your data trustworthy — all without forcing you to switch tools or contexts.
Who it's for
- Data analysts who need to write SQL queries faster, preview results, and collaborate without leaving their IDE.
- Data engineers building and maintaining dbt workflows, data pipelines, and quality checks with automated context awareness.
- Data scientists who want to run analytics, explore schemas, and deploy models while keeping their work reproducible and team-friendly.
Key features
Context engineering like a file system
nao lets you build your agent's context as a structured file system. You can add anything — data, metadata, rules, docs, tools, and MCPs — with no limit. Run nao init to create the context, then organize it however your team needs.
Synchronize with your existing stack
Pull context automatically from databases like Postgres, Snowflake, BigQuery, Databricks, DuckDB, MotherDuck, and Redshift. It also integrates with repositories (dbt, Looker, Cube, Airflow, GitHub) and external sources like Notion, Atlassian, Google Drive, and Linear — so your agent always has the latest context.
Measure and improve context reliability
Run nao test to create unit tests that turn questions into SQL. You get instant metrics on context reliability, answer rate, average response time, and total token usage. This helps you monitor performance and continuously improve your agent's accuracy.
