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We finished again #3 Product of the Day - thank you 🎉

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Yesterday (June 1st), we launched Databox MCP, and we finished #3 Product of the Day!

This wouldn't have happened without the dedicated and highly engaged team in Databox and this community. The upvotes, the comments, the shares - and everyone who followed along and took the time to try it. You helped us validate that people genuinely want to chat with their business data from the AI tools they already use.

A few things we learned from the launch:

  • The pain point that resonated most: teams already have dashboards, but still can't get fast answers to questions like "why did conversions drop this week?"

  • The use case people got most excited about: connecting it to Claude or ChatGPT and asking questions across multiple data sources at once

  • What surprised us: how many people said they'd tried similar tools but gave up because the setup was too complex

We're going to keep building and sharing progress here.

If you tried Databox MCP - what's working? What's missing? Would love to hear it.

And if you haven't tried it yet, you can get started for free at .
Ask it anything about your business data.

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Many congratulations Ziga, well deserved. Keep shipping.

 thanks! It’s always wonderful to have you on the launch team!

Congrats - a great product like this deserves the PH kudos

 Thank you!!

Congrats on the launch. The MCP integration is the piece that actually changes behavior — dashboards are only useful when someone remembers to open them. Putting the answer where the question happens removes that step entirely. The hard part is going to be keeping metric definitions consistent across sources when the model is doing the interpretation. Would love to see how you handle that at scale.

 Isaac, "putting the answer where the question happens" is exactly the shift. On consistency at scale - you're right that it's the hard part. The way we handle it is by keeping metric definitions in Databox as the single source of truth, so the model interprets a number, not raw data. The more governed your metric catalog, the more consistent the answers. It breaks down when definitions are loose or duplicated, which is a real challenge for larger teams - something we're actively working on.