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.
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Launch Team
How does the AI handle more complex queries that need joins across multiple tables, especially if my schema isn't perfectly cleaned up?
how well does it handle messy or joined data sources, like when the schema isn't super clean or tables need to be linked together before you can actually ask it anything useful?
How does the natural language query actually handle ambiguous requests, like when you ask for "active users" and there are multiple valid definitions in the underlying database?
How does it handle more complex queries that need joins across multiple tables, especially when the natural language gets ambiguous about which fields to relate?
How does it handle really large datasets or joins across multiple tables — does the AI write the SQL under the hood and is there any latency when generating more complex dashboards?
How does it handle complex joins across multiple data sources when you ask in natural language, and are there limits on query complexity or row counts?
how does it actually handle joins across multiple data sources when you ask a question in natural language, and is that something that just works out of the box or does it need setup?