Six years ago I got into Y Combinator and built the first version of @Basedash: AI data analyst. It was a simple tool that let you edit data in your database.
Now, six years later, we have a whole new product, and again we're shipping... database editing. But this time it's a look into the future of how companies are going to be run.
At the surface, this feature is kinda neat but nothing new. You can ask the Basedash AI to edit things in your database or call MCP servers to take action in other tools.
But, being a BI product, Basedash has a super deep understanding of your business. Probably better than you do yourself.
Soon, Basedash will proactively start suggesting ways to make your business better.
"If you extend this user's trial, they'll have a 50% higher chance of converting"
"If you tweak your ad sequence with this copy, it'll increase conversion rate by 20%"
One click to approve and run the experiment.
This is how we transition to AI-run companies.
Please check out our PH launch for actions and give us any support or feedback: https://www.producthunt.com/prod...
Curious how this handles more complex queries that need joins across multiple tables - does it figure out the relationships on its own or do you have to map those out somewhere first?
Connected my Postgres database and asked it to show weekly churn by plan in plain English, and it built the chart in seconds. Way easier than dragging fields around in my usual BI tool.
The conversational UI for generating charts feels really considered, you can tell the team obsessed over the small stuff like how follow-up questions modify the existing visualization instead of starting from scratch.
Connecting my Postgres was painless and the natural language chart builder nailed the visualization on the first try. Wish the dashboard sharing flow was a bit smoother though.
Connected a Postgres database and asked it to show weekly churn by plan. The chart generated in seconds and was actually accurate, which I was not expecting.
Finally something that lets me skip the SQL step entirely. Connected my Postgres in a couple minutes and asked for a churn chart by plan, which came out surprisingly clean without any fiddling.
How does it handle complex joins across multiple tables when generating charts from natural language?