That's the ceiling nobody prices in. The AI reads your data, writes a beautiful summary of the problem, and then a human copies the customer list into another tab and starts the actual work by hand. So we built the part after the insight. Basedash Actions writes and runs the SQL to find the answer, then reaches into the tools where the work actually lives (Stripe, HubSpot, and anything else with an MCP server) and does the follow-through. Find the accounts, update the CRM, chain the steps into a workflow that runs on its own. The obvious objection: nobody sane wants an AI acting on production data unsupervised. Agreed, which is why every action runs through a human approval gate. You see exactly what it's about to do, in plain terms, before it does it. The gates add friction, and that's deliberate. Trust in agents gets earned one approved action at a time, and I'd rather ship the training wheels than ship the incident report. We just launched yesterday on Product Hunt. Would you trust this?
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.
If someone asked you to list every person, contractor, and tool that can see your revenue numbers and customer data, could you do it without opening five different settings pages?
I couldn't, for a long time. We added people fast, handed a couple of contractors access to "just take a look," wired up some tools, and quickly didn't know who has access to what.
See our full changelog here: https://www.basedash.com/changelog
Edit charts directly in chat
You can now refine a chart without starting over. When you ask the assistant to tweak a chart it already made in the current conversation, it edits that chart in place instead of spinning up a brand-new replacement so iterating on a visualization feels like a real back-and-forth rather than a growing pile of near-duplicates.
Basedash now works with Excel, both ways. Drop an .xlsx file into the agent and it reads your data, analyzes it, and builds charts and dashboards in seconds — no formulas, no pivot tables. Then export any chart's data back to a .xlsx file with one click and keep working in spreadsheets. It's the fastest way for teams who live in Excel to add an AI data analyst, live dashboards, and real-time collaboration on top of the files they already trust. From Excel to dashboard, and back.
We ve been growing really fast (30%+ MoM ARR) at @Basedash since launching last year. Most of that growth has been the result of hard work, but we ve also had a secret weapon: an AI agent that acts as both a data analyst and a PM, working 24/7 to optimize our product s activation and conversion rates.
For decades, companies have been making product decisions based on intuition and manual data analysis. We wanted to see what would happen if AI could take the wheel completely.
We had a comment in our codebase that said SQL Server doesn't handle pagination well, so we should just avoid pagination for now. And it did exactly that: shipped the entire result set to the browser and let it sort itself out. Same deal for Spanner.
A lot of Basedash is just "run the SQL the user wrote, show the rows." We support 10+ dialects (Postgres, MySQL, BigQuery, Snowflake, Athena, SQL Server, Spanner, and friends), and every query gets paginated because we can't stream a 12M row result set to a browser.
Basedash now has groups and access controls. Bundle users into groups — internal teams, external clients, leadership — and give each one access to exactly what it needs: data sources, MCP servers, dashboards, chats, and automations. Set a group's AI context so the assistant answers differently per audience, and row-level security applies to every question. The right people see the right data.
Basedash for Slack is your AI data analyst inside Slack — now in the official Slack Marketplace. Mention @Basedash in any channel and it queries your real data sources, thinks in the thread, and replies with an answer and a chart, right where your team is talking. Automations deliver scheduled reports to your channels, and insights surface anomalies automatically — charts included. Ask in Slack. Answered by your data.
