We decided to go all in with AI from the start. It was less of an “aha” moment with AI and more of a mindset of solving any problem we could, and AI could help us do that. We knew natural language processing was advancing quickly, even in 2017 there was a lot of investment into research, and we anticipated it would get better. And so we asked ourselves, how do we take the great research, great technology, that is coming out and actually apply it to really important fundamental problems we experience in society.

Housing was such a greenfield—no one was really trying to address its challenges, even with traditional technology, which was pretty outdated. But even older techniques still added a lot of value, even without generative AI. The real aha moment came when we realized just how much of the industry's problems we could actually solve with it.

Early on, there weren’t strong generative models yet, so we used models like BERT to create more of a traditional conversational experience. Over time, we upgraded as new advancements rolled out, and now, we move fast with testing and integrating the latest innovations that can improve any part of our solution as soon as it drops.

It really depends on your audience and what "natural" means to them. For us, we had to replicate as much of the existing ways people did things as possible so it felt familiar. This was incredibly important for non-technical industries like housing and healthcare.