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 it handle more complex queries that need joins across multiple tables, especially when the natural language description is a bit ambiguous?
how does it handle more complex queries that would normally need joins across multiple tables, does it still hold up or do you end up writing SQL under the hood anyway
How does the AI actually handle messy or ambiguous natural language requests, and what happens when the chart it generates isn't quite what I meant? Can I easily refine from there without rewriting the whole prompt?
How does the AI handle more complex queries like cohort retention or multi-step funnel breakdowns, or does it really shine only on simpler visualizations?
How does the AI handle data sources that change schema frequently, and does it automatically pick up new columns or do you need to reconfigure the connections each time?
Curious how this handles complex queries when natural language gets ambiguous, like when I want to compare churn across segments but the phrasing could go a few ways. Does it ask for clarification or just guess?
How does the AI handle data sources that aren't perfectly clean, and is there a way to edit the generated SQL if the chart isn't quite what you expected?