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
How does Basedash handle data freshness when underlying sources update frequently, and does it cache results to keep queries snappy without re-running them every time?
How does the AI handle more complex queries that need joins across multiple tables, and is there a way to fine-tune or correct it when it misinterprets what you're asking for?
How does it actually handle messy real-world schemas, like when column names are inconsistent or fields change between sources?
How does the AI handle more complex queries that need joins across multiple tables, and is that included in the standard plan or gated behind a higher tier?
Curious how this handles complex joins across multiple tables when you ask in natural language, does it figure out the relationships on its own or do you need to set up the schema first?
how does it handle messy or unstructured data sources, like CSVs with inconsistent columns? curious if the AI can clean things up on the fly or if you still need to prep the data beforehand.
How does the AI actually figure out which fields to pull when you just describe a chart in plain English, especially if the column names are messy or inconsistent?