Databox is an AI-powered business intelligence and analytics platform for teams that need clear, trusted answers fast. It offers the best of BI, without the complicated setup, steep price, or long learning curve. It provides a blend of powerful, but easy-to-use, features, from preparing datasets and creating the custom metrics your company needs to track, to building beautiful dashboards, customizing your reporting, and receiving AI-powered insights.
This is the 6th launch from Databox. View more
Custom Integrations by Databox
Launched this week
Custom Integrations lets you connect virtually any API to Databox without writing code. Turn API responses into structured datasets and analyze them alongside your other sources to get a complete view of performance.
Free Options
Launch Team / Built With
Databox
Hi Product Hunt! 👋
I'm Davorin from the Databox team, and today we’re excited to share something we’ve been working on for a while: Custom Integrations.
Every team has a tool that runs a critical part of the business but doesn’t connect to their reporting. So to work with that data, teams export it into spreadsheets, maintain scripts, or rely on engineering to build and fix connections.
These workarounds get the job done, but they take time to set up, break easily, and create ongoing work for the people managing them.
Custom Integrations solves that.
It lets you connect virtually any API to Databox and turn the response into a structured dataset without writing code. Once the connection is set up, the data syncs automatically and becomes part of your existing workflow. You can build metrics, visualize them on dashboards, or analyze them with our AI Analyst.
That opens up a lot of possibilities. You could pull historical S&P 500 data and overlay it on your HubSpot pipeline to find seasonality in your sales cycle. Or connect a niche tool your team depends on, like a partner referral system, and finally see its performance next to the rest of your reporting.
Here’s what this means for your team:
Work with complete data: Bring in data from tools that weren’t part of your reporting and analyze performance without gaps
Eliminate ongoing maintenance: Replace manual exports and fragile workflows with automated syncing
Give your team direct access to data: Anyone can explore performance and get answers on their own, without waiting on an analyst or engineer
Custom Integrations is built for analysts, RevOps teams, and agencies who want to eliminate reporting gaps.
We’d love your input 👇
What’s the one tool you wish your reporting stack supported—but doesn’t today?
Thanks for checking it out 🙏
@davorin Many congratulations Davorin, Ziga and team on shipping this! :)
I’m excited to be hunting Databox again today after their previous launch, Genie.
This time, the team is tackling one of the most painful gaps in reporting: all those critical tools that don’t have a native integration and force you into spreadsheets, scripts, or ongoing engineering favors.
Custom Integrations by Databox lets you connect virtually any API without writing code and turn those responses into structured datasets that sync automatically.
Once connected, that data becomes part of your existing Databox workflow... you can build metrics, visualize them on dashboards, and even analyze everything with their AI Analyst side by side with your existing sources for a truly complete view of performance.
What I love about this launch is how it removes the “export to sheet / maintain a fragile workaround / ping engineering” loop for analysts, RevOps teams, and agencies, and instead gives them direct, self-serve access to the data they rely on.
Give it a spin today!
I tested Custom Integrations against a few APIs we had been unable to connect before - tools with non-standard pagination and OAuth2 flows. All of them connected cleanly and the datasets synced without issues. For anyone who has spent time maintaining workarounds for this kind of thing, the difference in reliability is immediately noticeable.
Databox
@tadej_kelc This is exactly the kind of feedback that matters - non-standard pagination and OAuth2 were the two things that broke the most setups with the old approach, so hearing they held up cleanly is good to know. The reliability gap is real and it's usually invisible until something breaks at the wrong moment.
Appreciate you actually putting it through its paces rather than just taking our word for it.
GrowMeOrganic
Would be cool if you added community-built connectors where users can fork/edit integrations.
Databox
@iamanantgupta This is actually on our radar - shareable integrations where setups can be passed from user to user (or made available more broadly) is something we've been thinking about as a natural next step. The fork/edit angle is an interesting framing for it. Glad the demand signal is there.
jared.so
Congrats on the launch? Every BI tool is shipping "AI insights" right now. In 18 months, what's the part of Databox that isn't a commodity?
Databox
@maks_bilski Fair challenge, and honestly a great question.
The "AI insights" layer is table stakes in 18 months, you're right. What doesn't commoditize as fast is the data layer underneath it. Most BI tools will have a chat interface - fewer will have a truly complete, unified dataset that the AI can actually reason over. The moat isn't the AI. It's whether your data is in good enough shape, and complete enough, for the AI to give you answers you can trust.
That's what we're building toward. Custom Integrations is one piece of it - the bet is that the team with the most complete, cleanest data foundation wins, not the team with the fanciest model on top of a pile of gaps. Genie is only as good as what's connected to it.
18 months from now - that's the part we're not willing to cede.
the 'export to spreadsheets' loop is basically a part-time job for revops teams at this point. 😅 every time a client asks for a metric from a niche tool, we end up in a manual sync nightmare. being able to turn an api response into a structured dataset without code is a massive time-saver. @zigapotocnik
Databox
@priya_kushwaha1 "Part-time job for RevOps" is painfully accurate - and it's one of those things that never makes it onto anyone's official task list but somehow eats hours every week. The structured dataset piece is what makes it stick: you set it up once and that particular spreadsheet export just stops existing. Glad it landed for you!
Congrats on launch! The problem is real, I struggled with it firsthand, while running my previous company. The only thing: I hope you don't rely purely on AI provided calculations, since now and then it tends to be incorrect.
Databox
@davitausberlin Thanks Davit - glad this resonates from firsthand experience. That's exactly the gap we set out to close.
On your point about AI calculations: totally fair concern, and worth clarifying what's happening under the hood. Genie doesn't calculate on your raw data - it queries a structured dataset that's already been synced from your API. So when you ask a question, you're getting answers drawn from your actual source data, not from AI inference. The AI part is just the natural language interface - the numbers themselves come from the same place they always did.
Happy to dig into any specifics if you want to poke at a real use case.
Databox
This is so easy a CEO can do it.
When this launched, I spent my saturday morning building an integration with AuthoredUp, the tool I use to publish and analyze my Linkedin posts.
I then built a Claude skill that connnects to the Databox MCP to help me draft my posts. It looks through my past post's performance and analyzes why certain posts perform better than others. Then, it uses that to propose post angles, validate my hooks, draft my outlines, and edit my drafts.