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.
Free Options
Launch Team / Built With
cubic
Congrats on the launch Claire and team! I've been using nao and it's been incredible. The all-in-one setup and direct warehouse connection just removes so much friction. Excited to see where you take this.
nao
@paul_sangle_ferriere1 thank you for your feedback, we're glad it's helpful for you in building cubic!
nao
Hey! I'm Stan, founding engineer at nao Labs.
Glad to be working on this beautiful product. We've come a long way since last year.
With nao, you can connect your data, visualize it, interact with it, and build powerful data pipelines, all with the help of AI and IDE tools we all love.
We're just getting started. We have so much more coming out soon.
Stay tuned and happy data vibing!
nao
@stan_dlc yes what a long ride in a year ! 🏎️
Product Hunt Wrapped 2025
nao as an AI IDE that actually knows the warehouse hits a nerve. I’m always bouncing between vs code, BigQuery, and dusty docs. If dbt + tests work with minimal setup, that’s a win. Local-first is nice. Parking this to try after standups.
nao
@alexcloudstar yes that was exactly the motivation behind nao - stop witch tabs, and have all your data context at the same place for AI!
nao
@alexcloudstar That's exactly this! I can't wait to see you build stuff with nao 🔥
Orchestra Data Platform
Nao is LIT
nao
@hugo_lu yeess it's FIRE 🔥
nao
@hugo_lu @claire_gouze and it's FREE 🆓
Nas.io
How does it handle versioning across SQL/Python/dbt workflows as they grow? Congrats on the launch.
nao
@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)!
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
nao
heyyy @_aneema , this is a great question! At the moment we have a single .naorules in the root folder, but as rules are growing in size we are considering moving everything inside a single folder with easy to use selectors.
Just to be sure would you like to have a single folder at the top with selectors or would you prefer being able to add .naorules in subfolders of your dbt project?
nao
@_aneema for now you can use one single .naorules so that it's just one file at the root of your folder. But we have on our roadmap to have the agent understand multiple files of .md in your repo!
@claire_gouze @bleff Thanks for getting back to me.
I think Nao rules should live in /.naorules (place in the root directory) with extension specific sub-rules. For example, /.naorules/sql-rules.txt stores formatting rules for .sql files, /.naorules/python-rules.txt for Python files, etc. (.txt is a placeholder, not the actual extension I would assign).
I also think this could save a nontrivial amount of tokens in API calls: when processing only .sql files, Nao passes only sql-rules.txt to the LLM instead of the entire generic .naorules file containing rules for all extensions. More manageable for data teams: analytics engineers maintain rules for their files, data engineers maintain rules for theirs, data scientists their, etc
Hope it makes sense, happy to help if you have any further questions :)
nao
@bleff @_aneema yes that's similar to what we have in mind. We'll keep you updated when we put this in place!
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.
nao
thank you@jchletsos 🥹 Awesome! This is exactly how nao can be useful doing analyses while being able to work on your data models at the same time
nao
@jchletsos Thank you Jason! Love seeing you so enthusiastic about nao. Looking forward to keep building it with you and your team!
@claire_gouze Me too. I couldn't build out such robust models at my dbt skill level yet. Since I'm more on the ingestion side, nao allows me to easily build my ideas or concepts for demos to prospects. Couldn't be happier to haven't stumbled onto your website a few weeks ago.
And thank you for all your support.