Short answer: Jackson Reiter says property information is digitized but fragmented with no single source of truth, so Realm built models that go out and find the data needed to quickly analyze any US property, with Canada coming soon.
Watch this answer from Episode 47 (starts at 2:24) · Episode 47 notes and transcript
From Episode 47: AI Agents Doing Commercial Real Estate Due Diligence in Minutes. We Demo It Live., starting at 2:24.
Jackson Reiter [2:24]: Yeah. So our platform kinda comes in two forms. We have two, different products that we offer through Realm. And I know you guys said you may or may not be interested in commercial real estate in the future. Maybe this is possibly a sales call too. Joking. Let's please but what you're seeing here on the screen is our first half of what the company does. This is Realm's self serve platform, we'll say, where anyone, who subscribes can come in and research any property in The US, and soon to be Canada as well. The whole idea here is that my background comes from a bit in real estate, so does both of my cofounders. And the big problem that we found there was that in order to get any sort of information about any property, what be it a single family home or, again, a tower in Manhattan, Getting information is just really, really difficult because a lot of the industry, though it is digitizing and has mostly digitized, it is still very fragmented.
Jackson Reiter [3:20]: There's no single source of truth. And up until now, there hasn't really been a good way to collect all of that information quickly, or efficiently or thoroughly. So what you're seeing here is Realm's self serve platform that allows you to get all of those that information in one spot. The way we do that, now is the best time ever to be able to do that. Because all that data lives so fragmentedly in so many different places on the Internet, in so many different ways, shapes, and forms, we built models that essentially go out and find the information that's needed in order to quickly analyze a property.
