Systems | Development | Analytics | API | Testing

Before building an AI-powered real estate product, understand the data that will power it. In this episode of The Innovation Blueprint Podcast, Roman Havrylyuk of ORIL and David Lovins, CEO of The Warren Group, explore why understanding your data — where it comes from, how it’s structured, and how different datasets connect — should come before building AI solutions. The better you understand your data, the better equipped you are to build technology that delivers reliable, meaningful results.

Why did MRR drop this month? Connect Stripe to Genie, the Databox AI Analyst, with the Stripe MCP connector and ask. Genie checks the MRR metric in Databox, then reads the subscriptions, invoices and payments in Stripe. In this demo, two canceled subscriptions and three failed payments explain the whole 3.9% drop, and $2,900 of it is still recoverable. In this video: Chapters.

Agents are easy to build and hard to measure. When your company runs hundreds or thousands of them, hand-written evals can’t keep up, and it’s unclear which agents are correct, efficient, and ready for users. In this conversation, Ryan Yackel, SVP of Product Marketing, and Andrew Demers, Product Manager for Agent Score at Tricentis Labs, explain how to measure agent quality, score agents automatically, and trace issues back to behavior.

One developer burned through 300 million AI tokens in a single week. The payoff was far less impressive. For Todd Trevillion, it's a striking example of what can happen when AI adoption outpaces the controls needed to manage it. And with autonomous AI agents beginning to make decisions on behalf of businesses, the stakes are getting higher.