Basedash: AI data analyst

Name: Basedash: AI data analyst
Rating: 5.0 (9 reviews)

AI-native Business Intelligence Platform

5.0•9 reviews•

2K followers

AI-native Business Intelligence Platform

5.0•9 reviews•

2K followers

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Data visualization tools

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Business intelligence software

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AI Data Scientist

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 20th launch from Basedash: AI data analyst. View more

Slack AI Data Analyst

Launching today

Ask about your data without leaving Slack

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.

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Launch tags:Artificial Intelligence•Data & Analytics•Business Intelligence

Launch Team / Built With

FramerLaunch websites with enterprise needs at startup speeds.

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Basedash: AI data analyst

Maker

📌

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.

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5h ago

Honestly

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

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2h ago

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?

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2h ago

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Forum Threads

p/basedash

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1d ago

✂️ How we cut our client bundle by 30%

Someone on the team finally looked at our production client bundle, which we hadn't checked in a while, and it was 25.67 MB. We got it down to 18.34 MB without removing a single feature. Most of the wins were stuff we were shipping to customers that had no business being there.

Bundle size is the metric that drifts up one dependency at a time until somebody notices. We noticed, turned on rollup-plugin-visualizer, and read the treemap. That treemap is the most useful 30 seconds of the whole project. You stare at the rectangles and immediately have opinions about every one of them.

p/basedash

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4d ago

New in Basedash: SQL definitions 📘

SQL definitions: reusable snippets across your queries

You can now create SQL definitions scoped to a database connection versioned, named SQL snippets that you can reference inline in any query with {{ definition("name") }}. Definitions get their own home in the Data page sidebar where you can create, edit, describe, and preview them, and the expansion is applied both for human-run queries and for queries the chat agent runs on your behalf.

The chat agent understands definitions too, so when it writes SQL it can reach for your team s definitions automatically. Admins can also let the agent propose and save new definitions when it spots a pattern worth keeping around so the SQL your team relies on becomes a real, reusable building block instead of something everyone re-types.

p/basedash

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3d ago

📄 How we paginate queries across every SQL dialect

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.

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Previous Basedash: AI data analyst Launches