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.
Hey everyone, Max here from Basedash.
Today we're launching Basedash for Slack: your AI data analyst, now living in the place your team already talks. It's in the official Slack Marketplace as of this week.
Mention @Basedash in any channel — "how's revenue trending this month?", "which signup source converted best last week?" — and it queries your connected data sources, shows Slack's native thinking state while it works, and replies in the thread with a written answer and the chart behind it, embedded as an image.
It's not just Q&A. Automations send scheduled reports to your channels, and insights post automatically when something in your data changes — both with charts attached. Follow-ups keep context in the thread, and row-level security applies to every question based on who's asking.
We run Basedash on this ourselves: our #metrics channel gets a daily revenue report at 9am, and most "quick numbers" questions never leave Slack anymore.
Happy to answer anything.
Natural language to SQL gets a bit tricky when you have a lot of tables and joins; any benchmarks?
The insight behind this one is simple. Most data questions are small. "How's revenue trending?" "Did signups recover after the pricing change?" Questions like these don't deserve a dashboard, a login, or a tab switch. They deserve an answer in the place you asked.
That's why we built Basedash for Slack. The whole point of an AI data analyst is that it comes to you.
What I love most: the answers are governed. Same semantic layer, same row-level security as the rest of Basedash. So when someone on your team asks a revenue question in a public channel, the answer is both correct and appropriately scoped to them.
Would love to hear how your team handles quick data questions today — that's exactly the workflow we're trying to replace.
The permissions model looks solid, but Slack threads get forwarded and screenshotted constantly. How do you handle the risk of sensitive data being exposed after the AI has already surfaced it to an authorized user?
Having this directly inside Slack feels way more practical than opening another dashboard every time. How long does the initial setup usually take?
The promise sounds great but I'm always wondering how these tools handle messy real world data. That's usually where things get interesting
I love products that meet users where they already work, and Slack is definitely where a lot of teams spend their day.
Being able to ask a quick question and get an answer with a chart directly in the thread sounds much more convenient than switching between dashboards and analytics tools.
The scheduled reports and automated insights are a nice touch too, sometimes the most valuable data is the information you didn't think to ask for.
Having data answers show up right inside Slack feels like the right place for this. It saves the usual back-and-forth of opening dashboards, asking an analyst, or chasing a chart later.
If @Basedash answers in a shared Slack channel using my RLS permissions, who can see the chart in the thread? Is it visible to the whole channel, or can sensitive answers stay private?
This looks like a massive time-saver for answering ad-hoc executive questions! Since it's translating natural language to query real data sources, how does Basedash handle complex or messy database schemas to ensure it doesn't pull the wrong metrics or hallucinate an answer?
Congrats on the launch! What are the most common datasources Basedash for Slackuses for analysis? Relevant data lives across multiple platforms for most companies so curious on about what you and the team have seen so far
About Slack Data Agent on Product Hunt
“Ask about your data without leaving Slack”
Slack Data Agent launched on Product Hunt on June 12th, 2026 and earned 108 upvotes and 17 comments, placing #10 on the daily leaderboard. 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.
Slack Data Agent was featured in Artificial Intelligence (470.8k followers), Data & Analytics (5.7k followers) and Business Intelligence (3.6k followers) on Product Hunt. Together, these topics include over 104.3k products, making this a competitive space to launch in.
Who hunted Slack Data Agent?
Slack Data Agent was hunted by Max Musing. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Slack Data Agent stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Max Musing