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.
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!
Congratulations on the launch! Does this connect to Databricks? I could see a great fit here. I’m also curious if you spotted anything in Google anti gravity that is inspired you to add future features to your product?
I have been working with NAO for 2 month consistently and it is really leading the game as soon as you need data to speak making link between the sense and the code... ( challenge and validate business logic, debug some value, ...) it is just making you win a lot of time keeping the context of your projects. If you are using DBT - it is just magic having DBT best practice and integration in heart.
The product is constantly changing and improving which is good - even if from time to time those evolutions are disturbing because it is difficult to keep a stable behaviour related to the upgrade in the Internal Prompt of the solutions and for this, you need to be careful and follow up regularly - if there could be transparency on this would be good
Congratulations on the launch! With projects like this I always have a fear that the AI will delete something ) I don’t know why.
But overall it’s a great project. Send out a newsletter to universities — you have thousands of clients there.
Huge congrats! The all-in-one workflow and local secure setup is a big win for analytics teams.
@nao Congrats on the launch! The "10x faster" claim is bold, curious what the main speed boost comes from. Is it the AI code generation, or more about streamlining data exploration workflows?
Also, does nao integrate with existing data stacks (Snowflake, BigQuery, etc.), or is it more standalone?
Data teams are always juggling multiple tools, so tight integration would be huge.
Congrats on the launch team Nao! 🚀 We’re live with hatable.art today and ran your site through it for a friendly roast 💀. Do check it out and go roast your competitors!
Wow, Nao sounds amazing!
😄 An AI-powered data IDE that helps analysts, engineers, and scientists build workflows, catch issues early, and collaborate without context switching is such a game-changer.
I love the idea of it acting like a dedicated AI teammate makes managing data pipelines and analytics so much smoother. Can’t wait to see it in action!
I could see how our tool could pair nicely with what I’m seeing at getnao.io to streamline community engagement and automation, so I’ll go check out their launch and take a closer look.
@claire_gouze an amazing product. It’s really made my workflow more efficient being able to query BigQuery and build dbt models all in one place with a clean interface. Great work from you and the team!
Let's goooo! Awesome product, amazing team, we didn't find the way to give it a try until now but it's on our radar for a long time, this new version looks freaking cool, we cannot wait to use Nao 🙌
I really think this idea is brilliant 🔥 Developers use cursor, you can do many great things with it, but data work is way more specific, and clearly lacking tooling on this part - and it's much more than just generate me this SQL query
I'm super eager to see where all this will lead, but clearly the industry needs more specialised tools, and I think you're nailing it! Will definitely push in my team :)
Congrats Claire. I'm loving Nao and will be showing it off next week to my team. Having the ability to query and profile the database within prompts along with the dbt models creation is so valuable. Wishing you all the best and look forward to the future of Nao.
Hello @claire_gouze@bleff - Any plan to squeeze the whole set of .naorules into a single folder? Having multiple naorules files on the root directory of a repo with hundreds of dbt models and yml files looks suboptimal to us
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,