The Exchanges

Q So you're basically providing these customer service, AI agents slash workflows that help I guess, function 24 seven and multiple different languages out of the box, and do you basically do like a lot of integrations into what they are already providing, or how do you tend to work with folks?

A Yeah, I think the way you should think about agents here are that, uh, it's more of a substitute for the mundane human labor. So whatever systems they're already using, generally an AI agent, at least when you first deploy, is not going to disrupt the tooling you currently have. So whatever CRM they're using, whatever, you know, telephony stack, We will just integrate with that. And then it's, it's kind of doing all the tasks you would expect a human to do. And, uh, over time that's just continued scaling. And so one of the benefits of AI agents is that they're always on, you know, either awake, 24 seven there. You don't have to train them really. There's no churn. You can just like scale them out.

AI assessment note: “whatever CRM they're using, whatever, you know, telephony stack, We will just integrate with that”

Q What made you realize that this was the thing to do?

A The, the real answer is we just saw a lot of folks that were willing to pay us like, you know, six figure contracts, which at the time when you're at zero ARR, it's like, oh wow, that's huge. And a lot of folks that were willing to, you know, do the same thing. And it was the only idea we really explored that really had that property where people were like, Hey, yeah, like if you did this, I would literally pay you money because I can justify it. The sort of flip side of that at the time was more just, oh, well, this is such an obvious idea. Like why do this? Cause you know, people would have thought of this stuff before, but Uh, that, that's a whole nother thing. I think like once you start doing anything, once you get into it, you, you, you understand there's way more nuance than the overall narratives. The sheer fact that people are willing to talk to us, like, you know, two people and willing to pay us money was signal enough that it was worth doing.

AI assessment note: “we just saw a lot of folks that were willing to pay us like, you know, six figure contracts”

Q that anybody can access, right? The, the GPT for us of the world or GPT for the cloud sonnets, et cetera. Um, and then there's all the stuff you've built on top of it to actually make this work well for your specific use case and for, um, customer support agents. Cause you, could you tell us a bit more about what you all have had to build over time?

A Of course. Uh, like you said, everyone has the same access to the same models. We see ourselves very much as a software company, and we're obviously doing a lot of work around AI and like using AI models a lot, but I would argue that most, you know, applications nowadays are, they're real software companies and AI models are kind of tools that everyone can use. And so most of the sort of alpha or most of the specials stuff that you build is on top of models. It's either the orchestration layer or the software around it. Uh, for us, there's been a big focus on both. Uh, the orchestration layer is kind of how you can use all these different models together. You probably have evals set up that measure how good each model is at certain things. You put them together and the whole goal of putting them together is to mold it around the business logic of the customer. That's part one. The other thing you build is just very classic software, right? You have this AI agent there. It's all the things I was saying before, like, you know, Transparency is a big piece. It's, you really don't want this to feel like a black box. That's just their answering questions. And so how can you build all the tooling to see like, okay, what's the data that the agent's using? How's, what steps is it taking? Um, can I analyze all these conversations that are coming in? If you have a million conversations, i…

AI assessment note: “It's either the orchestration layer or the software around it. Uh, for us,”

Q advice. Maybe they don't invest in each other. Like, there's kind of a thriving community, and every five to seven years, it kind of shifts who it is, and I feel like, you know, the IOI sort of math Olympiad community or coding competition communities are kind of very engaged right now. Is there any formal version of that, or are you all just kind of informally helping each other?

A Uh, yeah. I mean, I angel invested in a lot of the companies you just listed. A lot of their founders are angel investors in our company. It's, it's very informal. Obviously it's just like, you know, casual friends helping each other. I think the main thing is that, uh, with company building, there's just a lot of, a lot of service area, right? As you know, it's just like, how do you hire people? How do you like do sales? Like, how do you build this thing? Like, how do you structure like comp or like, I don't know, there's infinite things. So yeah, having the other data points is obviously super helpful. So I hang out with them quite often, play games, play card games. It's a Chinese version of bridge that I play with a lot of these folks quite often. And, uh, it's just, it's fun where you just kind of hang out. Everyone's kind of in this relative same stage of life. And so, yeah, like you said, there is definitely a lot of camaraderie and help that goes around.

AI assessment note: “It's, it's very informal. Obviously it's just like, you know, casual friends”

Q What would make you feel like you've accomplished what you set out to do?

A Well, on one hand, there is like a specific goal for our company, right? We want to grow. We want to grow the scale of the business, and we want to be, you know, The winner in this, in this like exciting market. So how's that defined? I mean, in five years, we want to, of course, be working with largest companies and have just like all the, just powering sort of the conversations for all, all the major brands out there and essentially just reinvent the way that most consumers interact with, you know, products and, and have conversations. And the other metric is, yeah, we'd like to get there through just having a very sharp, you know, product and just to go to market execution. Um, in the same way that I'm currently talking about, like, the Databricks and the ramps of the world, like, we want to build, you know, a business like that where we're just, like, doing everything, like, super sharp and, uh, very thoughtfully.

AI assessment note: “in five years, we want to, of course, be working with largest companies”

Q point out is you, you all ship really fast. Uh, you're very responsive as a team and company, you know, but third and most importantly, just the product tends to outperform. And so I think that's really been, um, you know, uh, great to watch over time. How do you think about the areas where AI agents are going to be, um, successful versus not successful in the short run?

A So basically one of the things that we have been thinking through, and this is something that was pretty big for us when we were first starting out, is that a lot of, there's gonna be a huge variance between like the different types of AI agents and how successful they'll be and like how quickly they'll take the rollout. Because when we were first starting the company, right, like, uh, we, we were pretty Open to what to build, and we knew that AI agents was exciting. At that point, we didn't even know that if there would be any, like, real use cases that would emerge even in the next 12 or 24 months, but we're kind of exploring. I think our view is that for the vast majority of use cases right now, it is still, like, Like there's not going to be real commercial adoption, uh, with the state of the current models because of a bunch of things. So one, one big thing is that if in a lot of spaces, you can't, there's really no like structure there to like incrementally build up. Like it has to be good, like almost perfect off the bat. So if you think about like a space, like, you know, security or, or something like that, where, okay, okay. You have all these like Sims out there and It's like, it makes sense. There's like tons of logs. Like that's, that's perfect for AI models. Uh, but the goal of that job is like you need to catch like any small thing that happens. And so because th…

AI assessment note: “space, like, you know, security... adoption there is going to be really, really, really slow”