AI Data Scientist giving insights directly in Slack
We just launched our Slack Agent. Now you can directly interface with Julius within your company's Slack workspace. Julius connects to your company's data sources, and automatically sources insights for your team.
I'm Rahul, founder and CEO of Julius. The team and I have been building Julius (the AI Data Scientist) for over 2 years to empower people to be more data curious.
Here's something we discovered while using our own product: Our best product and marketing insights were happening inside Slack channels, not scheduled meetings or pre-made dashboards. Someone would ask "which feature is driving the most engagement this week?" or "which marketing campaign drove the most adoption on a certain feature” and suddenly the whole team would jump in with ideas.
The problem? The questions were happening in real-time, but the answers weren't. Someone technical would have to tab out, pull data, write queries, create visualizations and come back.
That's why we're bringing Julius (the AI Data Scientist) to Slack.
Think of it as your team's Data Scientist that lives where your conversations already happen. It connects to your actual databases (Postgres, BigQuery, Snowflake, MCPs) and answers questions in natural language. Julius' Slack Agent queries your connected data sources directly, generates charts, runs SQL on the fly, and lets you collaborate with your team live in Slack to get the insights you need.
Example:
Marketing Analytics: "Using Postgres, show daily signups by UTM source for the last 14 days" → Julius writes the SQL, runs it, creates a chart, posts in thread.
Product Analytics: "Compare feature adoption rates between our free and paid users this month" → Julius queries your data, segments by plan type, visualizes the comparison.
Customer/Revenue Analytics: "Show me our top 10 customers by revenue and their usage patterns" → Julius pulls revenue data, correlates with product usage, creates a summary table.
Operational Analytics: "How many support tickets came in yesterday tagged as 'billing issues'?" → Julius queries your support system, shows volume trends and common themes.
Why it's different:
Most AI chatbots just search internal docs or give generic answers. Julius connects to your data and does real analysis: it writes SQL and builds visualizations.
While using it internally, we've found that each analysis turns into collaborative discussions where the team comes together to better understand our product and growth funnels. It makes your product, marketing, and ops teams more data-curious.
We'd love your feedback: what data questions are you constantly Slacking about? What would make this indispensable for your team? Happy to talk details about how we built it too.
Hi Product Hunt! 👋
I'm Rahul, founder and CEO of Julius. The team and I have been building Julius (the AI Data Scientist) for over 2 years to empower people to be more data curious.
Here's something we discovered while using our own product: Our best product and marketing insights were happening inside Slack channels, not scheduled meetings or pre-made dashboards. Someone would ask "which feature is driving the most engagement this week?" or "which marketing campaign drove the most adoption on a certain feature” and suddenly the whole team would jump in with ideas.
The problem? The questions were happening in real-time, but the answers weren't. Someone technical would have to tab out, pull data, write queries, create visualizations and come back.
That's why we're bringing Julius (the AI Data Scientist) to Slack.
Think of it as your team's Data Scientist that lives where your conversations already happen. It connects to your actual databases (Postgres, BigQuery, Snowflake, MCPs) and answers questions in natural language. Julius' Slack Agent queries your connected data sources directly, generates charts, runs SQL on the fly, and lets you collaborate with your team live in Slack to get the insights you need.
Example:
Marketing Analytics: "Using Postgres, show daily signups by UTM source for the last 14 days" → Julius writes the SQL, runs it, creates a chart, posts in thread.
Product Analytics: "Compare feature adoption rates between our free and paid users this month" → Julius queries your data, segments by plan type, visualizes the comparison.
Customer/Revenue Analytics: "Show me our top 10 customers by revenue and their usage patterns" → Julius pulls revenue data, correlates with product usage, creates a summary table.
Operational Analytics: "How many support tickets came in yesterday tagged as 'billing issues'?" → Julius queries your support system, shows volume trends and common themes.
Why it's different:
Most AI chatbots just search internal docs or give generic answers. Julius connects to your data and does real analysis: it writes SQL and builds visualizations.
While using it internally, we've found that each analysis turns into collaborative discussions where the team comes together to better understand our product and growth funnels. It makes your product, marketing, and ops teams more data-curious.
We'd love your feedback: what data questions are you constantly Slacking about? What would make this indispensable for your team? Happy to talk details about how we built it too.