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 23rd launch from Basedash: AI data analyst. View more
Basedash Actions
Launching today
Basedash answers questions about your data. Now it acts on them. Ask the agent to extend a trial, fix a record, or seed a demo org — it writes the SQL and runs it against any database an admin has enabled for edits. Ask it to update a Stripe subscription or create a HubSpot lead and it acts through any MCP tool you've connected. Every consequential action pauses for your approval, and every tool has its own permission. Skills chain it all into workflows. From answers to actions.
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Launch Team
Basedash: AI data analyst
Tabstack by Mozilla
@maxmusing S/O for this new launch! keep up the great work, and keep launching
Basedash: AI data analyst
Thank you @fmerian! Means a lot coming from you 🙏
An LLM writing and running mutating SQL against a real database is exactly the part I'd want the guardrails on. When it says 'update the state of a hundred items', does it show me the statement and the affected-row count before it runs, and does it wrap the write in a transaction I can roll back? The classic failure is a dropped WHERE turning a 100-row update into 100k, and the model sounds equally confident either way. Preview plus row-count plus rollback is what would get me to flip 'allow edits' on in prod.
Basedash: AI data analyst
@dipankar_sarkar yes exactly, you can preview the exact query and see what would be affected before deciding whether you want to approve or reject the action. Reliability and trust are super important for a feature like this.
Love that the natural language input sits front and center instead of burying it behind a SQL editor or settings panel. The "describe what you want" framing makes it feel like a creative tool rather than another BI dashboard.
Basedash: AI data analyst
@mzeyyenrdjx yes exactly, that was super important to us. We market ourselves as AI-native because we really designed and built this product around AI from the start. In many ways, we're much closer to Lovable or v0 than Tableau or Looker.
Preview plus approve/reject covers the intent check nicely. The one that's bitten me: the affected-row count you show at preview is a separate query from the write, so under any concurrent traffic the number I approved and the number that actually changes can drift. Do you run the count and the update inside one transaction, or is that preview count more of an estimate?
Basedash: AI data analyst
Kris from @Basedash: AI data analyst here! Super excited about this launch. We built this because we realized that we often wanted to act on all the insights we were getting from our own product. But carrying out these write tasks is still pretty tedious for a non-technical user, whereas technical teams need guardrails in place before non-coders like me can update the database. So we built both :)
Now the agent writes the fix, shows you exactly what it's about to run, and waits for your approval. One click. All with tons of controls. Admins enable edits per connection. Every tool gets its own permission level. Routine operations run automatically, sensitive ones pause for a human, dangerous ones stay blocked. Your rules, enforced every time.
We've run this internally for weeks and it has already changed how our own team operates. The person closest to the customer fixes the issue.
Happy to answer anything!