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.
I respect Nao's effort to automate data tasks. The agentic coding area is established, yet agentic analytics and data science development are lacking. I am happy to see a company advancing this area.
What needs improvement
In addition to writing DBT models, I'd like the editor to have an intuitive way to plan and create analysis-related tables, data models, and visualizations. This will speed up and simplify the analysis works significantly.
Hi Product Hunt, I’m Claire, co-founder of nao Labs! 👋🏻
nao is the AI data IDE I wish I had when I was working in data.
2 years ago I was head of data at sunday. I was trying to keep up with the speed of the business, helping my team avoid breaking changes in prod, while trying to save them time for the interesting analytics stuff.
But it was tough. Our tools just seemed so unadapted to our work. We kept switching from the IDE to the warehouse console and manually applying data model changes in the BI tool. I kept reconnecting extensions until I just gave up.
Then AI came!
But once again, AI coding tools were thought for developers, not for data people. They handle code context very well, but not the variety of context you need for data work: data schema, documentation, data stack, business definitions.
So one year ago, when Christophe and I started thinking about how to make data tooling more efficient for data people, it became clear: we wanted to create the best place to work on data with AI.
nao is the AI data IDE, designed specifically for data workflows. It’s a fork of VSCode, directly connected to your warehouse. And the AI agent has all the context of your code + metadata + data stack and business context - all in a secure local setup.
We released our first version 6 months ago, so I can tell you a bit about what our users love:
It’s all in one. Anyone in the data team - technical or not - can easily connect their data and stack integration in one click.
One prompt from data schema to a full data pipeline with data quality checks.
One prompt for deep-dive analysis. nao plans analytics to run, runs all checks, and provide a full analytics summary.
nao is still in beta and there’s much more coming. Our goal is to be fully integrated across your entire data stack, end-to-end — from data engineering to data science to analytics. We believe that for AI to be adopted in analytics, it must first be adopted and trusted by the data team itself.
We have a free trial and free version - give it a try and let us know what you think!
Happy data vibing,
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@claire_gouze Congrats on the launch. I like how clean everything looks... the demo video, landing page, gallery images. :)
@claire_gouze This is one of the best PH launch comments I've read in a while. No buzzwords - just a real story about a real problem. The journey from "I gave up on extensions" to building the solution yourself is an inspiring one. Congratulations on the launch!
@chilarai yes the agent can run some data edits queries as well - but it will ask you for permission to avoid any breaking changes! For now the generated analytics is not shareable. But it will be soon!
hi @chilarai for the moment it only support data viewing when it comes to your warehouse! if you want to edit your data it's still up to you depending on the pipelines you're writing. But, nao can help you write these pipelines.
For the moment the generated analytics dashboard are not sharable but it's on the near roadmap!
@nuseir_yassin1 With nao as it's a local IDE supporting git workflows you can handle versioning acrross your pipelines with git (or whatever other scm you want to use)!
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I really like how AI natively works with the context and data. Wish Supabase will do the same!
@pasha_tseluyko Yes I guess what we found is that AI experience is not the core product of any database / data warehouse. But you already work with your supabase database within nao!
hey@pasha_tseluyko , nao works with Supabase actually! You have to set up a Postgres connection with Supabase creds. Let me know if you need help set it up
hey Product Hunt, Christophe here, co-founder of nao Labs!
I've been working in the data industry for more than 10 years and I think that data people finally deserve a tool that is made for them. AI is completely reshaping how software, data and analytics is made. On the software side we've witnessed major breakthrough, but on the data side we are still working with clunky UIs, juggling between tools and changing code without even knowing what are the impact of the changes.
With nao we want to change this, we believe data people can have better tools in which they can build, fix and explore data while using AI to do their daily job faster than before and focus on high value-added tasks.
You can try nao for free! It's the best way to work on data using AI.
@mateolbs@claire_gouze Hey Claire and Mateo, congrats on the launch of nao. If you're ever in need of a marketing analyst to help in user adoption of this great product, I'll love to discuss on how my experience can contribute to your success.
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Congrats on the launch @claire_gouze and @bleff ! @nao hits a real gap in data tooling. I’ve spent years architecting, building and operating pipelines across analytics and finance, and the friction is always the same: context and code lives in too many places and tools (warehouse, BI, docs, code), so changes are slow and risky.
An IDE that’s natively "data-aware" (schema, lineage, business definitions) and connects directly to the warehouse is the right abstraction.
My experience so far has been fantastic:
Connected to my warehouse and generated a dbt model + tests from tables in minutes; lineage preview caught a join issue early.
Ran an ad‑hoc analysis via prompt; the agent planned the checks and produced a solid summary with links back to sources.
Environment switching worked cleanly for dev → staging; diffs were readable and prevented drift.
Curious about two things:
Do you plan to continue enhancing the SQL query experience? I'm still switching between nao and DataGrip for some tasks.
Do you plan to offer BYOK in the PRO plan? I would love to use our own Bedrock keys for the Claude models.
nao
Thank you for your review Chau, that's very great to read! We're working on a way to make nao even better for exploration and analytics. Stay tuned!