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Basedash lets teams query databases in natural language, showing the underlying SQL every time.
Grounded in available product and source data
An AI that answers a data question but hides how it got there isn't something a data team can actually trust — Basedash is AI-native BI with governed metrics specifically to close that gap, showing the underlying SQL behind every natural-language answer rather than returning a number with no way to verify it.
That transparency runs through the whole product. The AI Data Analyst generates answers in plain English while exposing the SQL that produced them, and dashboards build the same way — described in natural language, assembled in minutes rather than through a traditional BI tool's drag-and-drop builder. None of this works without real data underneath it: a Warehouse component connects to 750+ sources spanning databases like PostgreSQL and Snowflake, SaaS tools like Salesforce and Stripe, and analytics platforms, with an example on the site showing a revenue dashboard pulling from Snowflake and Stripe built in 21 seconds.
The semantic layer is what keeps 'governed' from being just a marketing word: a metric gets defined once, and that same definition gets referenced consistently across chat, charts, dashboards, insights, and automations, rather than getting silently redefined differently in five different reports. Insights delivers AI-generated daily briefings automatically, Automations handles scheduled data workflows, and an MCP Server lets any AI client connect to the same governed data directly rather than working from a disconnected copy.
Three tiers — Starter, Growth, and Enterprise — scale primarily by team size and governance needs, with a 14-day trial requiring no credit card for anyone testing it first. The site cites 200+ companies using the platform and states it ranks highest on BI Bench for AI-analyst accuracy, backed by SOC 2 Type II certification, row-level access control, and both cloud and self-hosted deployment options for teams with stricter data-residency requirements. What separates this from just asking ChatGPT a data question, per the site's own framing, is the production-grade access control and direct schema understanding — a general AI tool doesn't have governed metrics or row-level security built in the way a purpose-built BI layer does.
You can check — every natural-language answer comes with the underlying SQL visible, so a data team can verify the logic rather than trusting an opaque result.
Two dashboards quietly disagreeing on the same number — a classic BI problem where 'revenue' gets calculated slightly differently in different reports because nobody centralized the definition.
Not if row-level security is configured properly — access control is enforced at the data layer itself, so the AI chat interface can't surface anything a user's underlying permissions wouldn't already allow.
Yes — self-hosted deployment is offered alongside the standard cloud option, which matters for organizations with strict data-residency or compliance requirements that rule out a fully cloud-hosted tool.
The page reviewed doesn't break down specific feature differences or pricing between the three, so that's worth confirming directly — though Enterprise is described as the tier with custom terms for larger, more complex deployments.
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Hey everyone, Max here from Basedash. Today we're launching Actions: the Basedash agent can now change things, not just report on them. Turn on Allow edits for a database connection and the agent writes and runs SQL against it: extend a trial, fix a bad record, update the state of a hundred items, spin up a demo org. Connect an MCP server and it takes action in your other tools too: update a subscription in Stripe, create a lead in HubSpot, send an email through Resend. The part that makes this safe to actually use: nothing consequential runs without you. The agent shows you the exact SQL or tool payload and waits for approval, and every MCP tool has its own permission (always allow, needs approval, or blocked) so you decide which actions run automatically and which ones pause for a human. We run Basedash on this internally. Extending a customer's trial used to mean a database edit, a Stripe change, and a follow-up email across three tabs; it's now one skill the agent runs, with one approval. PH community gets an extra week on their trial this week. Happy to answer anything.
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