Basedash is the AI-native Business Intelligence platform that lets you create dashboards and understand your customers using natural language. Connect your data, describe the chart you want, and let AI generate the visualization. No SQL required.
This is the 14th launch from Basedash. View more
Basedash Dashboard Agent
Launching today
The Dashboard Agent builds entire dashboards from a single prompt. Describe what you want to see — "Everything about new user signups" or "MRR and churn this quarter" — and the agent picks the charts, writes the SQL, and lays them out for you. You get KPIs, a hero chart, and the breakdowns that actually answer your question, all connected to live data. No SQL. No composing dashboards chart by chart.
Describe a dashboard. Get a dashboard.
Free Options
Launch Team
Basedash
RiteKit Company Logo API
@maxmusing This is a genuinely clever approach to dashboard creation. The ability to go from vague prompt to useful output in seconds while still respecting more specific requests is the hard part most tools get wrong. Curious how you handle cases where the AI's SQL interpretation doesn't match what the user actually meant—do you surface that discrepancy easily so they can correct it.
Basedash
Super pumped about this. The part that took the longest wasn't generating SQL, it was figuring out what "a thoughtful default" actually means.
When someone types "everything about new signups," what do they really want to see first? Which chart type carries the headline number vs. the breakdown? How many KPIs before it stops feeling useful? We rebuilt the layout logic a bunch of times, and the version that shipped is the one where dashboards started feeling like something a person made, not something a model generated. Which is super cool!
Would love to hear what kinds of dashboards you try it on... the weirder the prompt, the more useful the feedback!
Basedash
@kris_lachance it's so much fun to watch the agent stream in charts as they're built and figure out the optimal layout.
The "thoughtful defaults" piece is where I'd expect this to break down in practice. If I ask for "MRR and churn this quarter" and my data model has three different tables that could plausibly be "revenue" — does the agent ask me to clarify, or does it just pick one and hope? How does it handle ambiguous schema without turning into a back-and-forth chatbot?
Cal.com
This is so good. Feels like the result of a lot of hard work, product vision and full-stack polish coming together. This is the way to interact with data.
Basedash
Appreciate it @dexter_storey! Definitely a lot of hard work coming together on this launch.