Basedash SCIM - Your org changes. Access keeps up.
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.
Replies
How does Basedash handle more complex data queries beyond simple visualizations, like cohort analysis or predictive modeling?
Connected it to our Postgres database and asked it to show weekly active users, which nailed it on the first try. The natural language chart building actually works and saved me from writing SQL I was dreading.
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?
The natural language to chart flow feels really dialed in, it actually understands what you're asking rather than just throwing spaghetti at the wall. Nice execution on something that's usually clunky.
This looks like a genuinely useful way to spin up dashboards without bothering engineering every time. The natural language chart generation seems to handle basic requests pretty well from what I tried.
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?
the natural language to chart flow feels really well thought out, like the team actually obsessed over what happens when the AI gets it slightly wrong. quiet polish you dont notice until you use it.
How does it actually handle messy real-world schemas when you just describe the chart you want in plain language?
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?