Systems | Development | Analytics | API | Testing

Setting up MCP for AI-powered data analysis sounds technical, it isn't. Rick Kranz breaks down how fast you can actually get from zero to asking Claude real questions about your business data. Here are 4 skills that he has built and are free to run on your own data.

A chart shows you what happened. It doesn't tell you why, or what to do next. Rick Kranz explains why the real value of connecting AI to your data through MCP isn't the visualization, it's the trend-spotting and analysis that happens after.

Rick Kranz has built over 100 AI automations for his community and clients — he has no reason to defend Databox. But when he tried to run his AI analysis without the Databox MCP, it just stopped working. In this episode, Rick and Pete break down exactly why: the semantic layer, the metric definitions, and the standardized math that make an AI's answer trustworthy instead of a guess. If you've ever wondered why connecting five random MCP servers to Claude doesn't give you the same results as a purpose-built data layer, this is the episode.

Ask Genie a question. Get a report back, ready to share, with real data already in it. This is Genie Artifacts, Databox’s AI analyst turning a prompt into a finished document. Watch it build a report from scratch, convert that report into a slide deck with a single follow-up prompt, then get shared as a public link anyone can open, no Databox login needed. What you’ll see: Genie pulls from 130+ connected data sources, so every report and deck is built on numbers your team already trusts. No manual formatting, no rebuilding for a different format, no dashboard for the recipient to log into.