A Product Market Fit Show | Startup Podcast for Founders
00:03:12 · Pablo Srugo, Ali Khokhar
[00:03:12] S1: Okay. Meaningful. [00:03:13] S0: Yeah. Meaningful. Exactly. As in like, you know, we're looking at CEOs are only signing off on this, like, this is not bottoms up, there's no PLG, it's pure play. You know, what at the time for us especially was considered quote unquote enterprise. We've now moved even more upmarket. But at that moment, that first 240 k deal was the largest deal we had ever signed in the history of our company. We had done it in like sub twenty days through Christmas and New Year's. Every few weeks then we were signing deals like that. And that feeling of like the market is pulling the product out of you versus you are pushing it into the market, that feeling is very distinct. And I started feeling that over and over and over, and that gave us the signal that we were on the right track and that we had sort of struck a nerve in terms of the product and platform and infrastructure we had built with this really good match in terms of the pain points that a lot of our customers were feeling.
00:04:01 · Pablo Srugo
[00:04:01] S1: Everybody will tell you that there's no number that indicates product market fit, like when you hit a million or 2,000,000 or whatever. But when you're signing 100 k plus deals consistently, and especially fast paced, you're just getting through cycles and it's a high priority, it's almost impossible to not feel like you have true PMF. Because it's real money. Nobody's spending that money unless they feel they really need that product, which leads me to that question. Tell me a bit more about that product at that point, because I know it changed before, we'll get to that. But at that point, what was the product that you were selling that was resonating so clearly?
00:04:33 · Ali Khokhar, Pablo Srugo
[00:04:33] S0: Yep. So we were very focused on, and still are. We think of ourselves and have had sort of framed ourselves as an infrastructure and platform layer to build healthcare specific agents, especially those agents that were able to perform tasks and works generally that is has high risk of failure. Right? In healthcare, there's a lot of back office administrative functions, there's a lot of rev cycle type of building that's happening. We started our journey being focused on patient facing agents. Patient facing agents in what I call subclinical use cases. Think like med adherence and side effect management. Think like post discharge, post surgery recovery, etcetera, etcetera, messing up and, like, cost of failure is extremely high. And we had built and have built sort of this entire infrastructure deep layer to be able to build and train and quantify the performance of those agents performing that type of work in the world. [00:05:24] S1: And these were voice agents, like, that you would call or text or email? How did people interact with them?
00:05:28 · Ali Khokhar
[00:05:28] S0: All the above. So multichannel, voice, text, email, and then inbound and outbound. So we're reacting, and when patients are talking to us, we're responding, or we're proactively sort of, you know, identifying certain signals that we should be reaching out to those patients. And then multimodality. Right? Voice and text. So, like, multiple channels. We could be over phone, in a web app, in a phone app, whatever the case may be, multiple modalities, and then both proactive and reactive. And so we sort of think of us as the training, the simulation engine, the orchestration engine, memory management, the underlying data platform layer, and on top of that, you build these agents across the interaction layer. So think of that as the sort of full deep stack. That's what sort of everybody was gravitating towards, because most people at that point were also like, I want to own control over how the system gets trained on what is in scope, what is out of scope. Some of the sort of clinical workflows, the clinical intelligence that's taking place, they did not wanna just buy that off the shelf, given the nature of the use cases that we had started off with. And so there was this really good fit at that moment in time where our customers were like, look, I care deeply about how this agent is trained, what the scope is, how it reasons. I want control over that. I want ownership over that. But I'm not going to build and maintain all of the underlying infrastructure to be able to sort of create that. And so it's a really good fit in terms of, you know, everybody talks about an enterprise build versus buy, build versus buy. We landed in this middle ground, which is like, you, customers, should build at the right layer, but buy a different layer, essentially.
00:06:56 · Pablo Srugo, Ali Khokhar
[00:06:56] S1: You know, you won that because of customization and control, but what would you say was the number one reason why these customers even wanted AI agents in the first place? Like, what for them was the obvious problem that this would solve? [00:07:08] S0: I mean, we know in health care there's a massive labor shortage. Right? So a lot of them had way more demand than they could handle. They could not hire and train really high quality clinical staff fast enough in order to provide the sort of very personalized hands on care that they wanted their organization to be able to deliver. And so there's this big gap that they were trying to solve for. [00:07:27] S1: And for them, is that like more revenue for them? Is that like what's the business ROI? [00:07:31] S0: It ends up being better clinical outcomes for them, so a lot of value based care organizations want to be able to drive and deliver and prove some of those things. It ends up being more revenue for certain organization or preventing revenue leakage in some cases. That's sort of where a lot of folks are focused on. Or it ends up being more revenue in the form of being able to handle, you know, two x the volume of patients without two x the volume of staff. Right? Like as we know, a lot of these organizations are very labor heavy, labor constrained, and like ops heavy. And so they're saying, okay, can I increase the demand side without having to go and find, which we know is really hard to do so, really high quality clinical staff that is trained up?
00:07:31 · Ali Khokhar, Pablo Srugo
[00:07:31] S0: It ends up being better clinical outcomes for them, so a lot of value based care organizations want to be able to drive and deliver and prove some of those things. It ends up being more revenue for certain organization or preventing revenue leakage in some cases. That's sort of where a lot of folks are focused on. Or it ends up being more revenue in the form of being able to handle, you know, two x the volume of patients without two x the volume of staff. Right? Like as we know, a lot of these organizations are very labor heavy, labor constrained, and like ops heavy. And so they're saying, okay, can I increase the demand side without having to go and find, which we know is really hard to do so, really high quality clinical staff that is trained up? [00:08:07] S1: And when was this? You you mentioned it was, like, December. What year? [00:08:10] S0: So December, January of last year, basically. [00:08:12] S1: 2425. Okay. And when did you start Amigo AI? [00:08:16] S0: The January prior. So '12 this is twelve months into our first round of sort of fundraising and capital. [00:08:21] S1: So tell me about the origin story. Now that we know that moment when things started to find click, there was a year before that where presumably it wasn't clicking yet. Right? But maybe just start with that, like, just a little bit of not not maybe your full background, but right before you started Amigo, what were you doing, and and why did you decide to start this business in the first place?