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 24th launch from Basedash: AI data analyst. View more
Basedash SCIM
Launched this week
Basedash SCIM keeps workspace identity aligned with the directory your company already manages. As people join, change teams, or leave, users, groups, and organization memberships stay current—reducing app-by-app cleanup and helping leaders answer who still has access. Together with SSO, RBAC, and RLS, it gives enterprise teams more confidence to roll Basedash out without turning identity administration into a bottleneck. Your org changes. Access keeps up.
Free Options
Launch Team
Metabase
Hooked up our Postgres and asked it to chart weekly active users by plan, got a clean visualization back in seconds without touching SQL. Impressed it actually understood the relationships between tables without me hand-holding it.
Basedash: AI data analyst
@kuzeyatmac1qeh glad to hear! Let me know if you have any other feedback.
Pulled it up to poke around and the natural language chart builder actually understood my messy request on the first try, which surprised me. Setup to connected data was quick and the dashboard looked clean without any fiddling.
Basedash: AI data analyst
@hayrunnisa69ve awesome to hear, let me know if you have any questions getting started!
how does the pricing scale as the dataset grows, and are there any limits on the number of rows or sources you can connect before the AI starts getting sluggish on the queries?
How does the AI handle messy or poorly documented schemas when you point it at a database it has never seen before?
How does the AI handle data sources it has never seen before, like a weird internal database or a spreadsheet with merged cells and weird formatting? Curious how forgiving it is in practice.
How does it handle messy or inconsistent schemas when you point it at a real production database, or do you need to clean things up before connecting?
How does it handle complex joins across multiple tables when you describe them in natural language, and are there limits on how many rows it can process per query?