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Today we’re launching Basedash for Slack — your AI data analyst, now living in the place your team already talks. And as of this week, you can find it in the official Slack Marketplace.

Mention @Basedash in any channel, ask a question in plain English, and the agent queries your connected data sources and replies in the thread — with a written answer and the chart behind it, embedded right in Slack.

Why we built it

The best analyst is the one you don’t have to context-switch to reach. Most data questions are born in conversation: someone asks “how’s revenue trending this month?” in a channel, and then the question goes somewhere else to get answered — a BI tool, a SQL editor, a ticket for the data team. By the time the answer comes back, the conversation has moved on.

Basedash for Slack closes that loop. The question gets asked in Slack, and the answer lands in Slack — in the same thread, where the whole team can see it.

Just mention @Basedash

Type @Basedash in any channel or DM and ask away. The agent figures out which of your connected data sources holds the answer, writes and runs the queries, and checks its own work before replying.

Asking @Basedash a revenue question in a Slack channel

While it works, you’ll see Slack’s native agent status — Basedash is thinking — so you know exactly what’s happening from the moment you hit send.

Slack's native thinking status while the Basedash agent analyzes your data

Follow-ups keep their context. Ask “now break that down by plan” in the thread and the agent picks up where it left off. Conversations started in Slack also sync to Basedash, so you can continue the same analysis in either place.

Answers come with charts now

This is the part we’re most excited about: the agent can reply with a visualization, embedded in the thread as an image. Ask a trend question, get the trend — not just a sentence about it.

The Basedash agent replying in Slack with a written answer and an embedded revenue chart

Every chart links back to Basedash, so anyone who wants to dig deeper is one click away from the live version.

Automations and insights, delivered to Slack

The agent answering questions is half the story. The other half is Basedash reaching out to you:

  • Automations run on a schedule — “every weekday at 9am, summarize yesterday’s revenue” — and post the report to the channel you choose, charts included.
  • Insights watch your data continuously and post when something meaningful changes, like a conversion spike after a pricing change.

Automations, insights, and shared charts from Basedash landing in Slack channels

Both now support images too, so a scheduled report or a surfaced anomaly arrives with the chart that proves it.

How it works

Ask, analyze, answer — the Basedash for Slack flow

Under the hood it’s the same AI data analyst that powers Basedash chat: it explores your schema, writes and verifies the SQL, and uses your custom business context. Permissions carry over too — row-level security applies to every Slack question based on who’s asking.

Getting started

Basedash for Slack is available today for all Basedash users:

  1. Sign up for Basedash (or log in) and connect your data sources
  2. Install the Basedash app from the Slack Marketplace, or from Basedash via the command menu (⌘K → “Install Slack app”)
  3. Mention @Basedash in any channel and ask your first question
  4. Point your automations and insights at the channels that should hear about them

For setup details, see the Slack app docs.

What’s next

Every team has a channel where the numbers get discussed. Now the analyst is in it.

Install Basedash for Slack today — ask in Slack, answered by your data.

Written by

Max Musing avatar

Max Musing

Founder and CEO of Basedash

Max Musing is the founder and CEO of Basedash, an AI-native business intelligence platform designed to help teams explore analytics and build dashboards without writing SQL. His work focuses on applying large language models to structured data systems, improving query reliability, and building governed analytics workflows for production environments.

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