He hit $1M ARR with just 2 people. 2 years later, he's worth $1.5B. | Ashwin Sreenivas, Co-Founder of Decagon

Decagon started 2023, he raised $230 million already. You've built it one and a half billion dollar company in two years. A lot of first-time founders and I also included at the time, spent a lot of time, I think, over into actualizing the product, being able to figure out, "Oh, what is the exact three-year strategy at the start? I need to know what the exact plan is before I start doing stuff." And I think it's really easy to fool yourself into thinking you're doing great, important work, and then you have this grand strategy and you bring it out to the market and nothing worse the way you thought it would. They would tell us, they're like, "Hey, we really need something." And we have looked at solutions, A, B, C, D, and this is all the ways in which they don't work for us at all. Just go to your customers and ask them, "There's all these other players in the market, why haven't you bought one of them?" They will tell you your competitive differentiation, you know, why all the other products in the market don't work out for them, and what you need to do to be able to differentiate me and be successful. Because all of this ultimately comes out of customer need. They kept telling us the same kinds of problems that they had, right? They were like, "This is the problem that I had," and which was the same as everybody else. "Here are the other solutions I looked at on the market, and this is why it doesn't work for me, and that why it doesn't work for me was similar to what everybody else was telling us, and when we showed them our product, they were like, "Yup, this is great. I'm ready to buy, and I'm ready to buy quickly." Right? And that's what allowed us to grow revenue so quickly with just two people. That's product market fit. Product market fit. Product market fit. I called it the product market fit question. Product market fit. Product market fit. Product market fit. Product market fit. I mean, the name of the show is Product market fit. Do you think the product market fit show has product market fit? Because if you do, then there's something you just have to do. You have to take up your phone. You have to leave the show five stars. It lets us reach more founders, and it lets us get better guests. Thank you. Ashwin, welcome to the show, man. Yeah, thanks so much for having me excited to be here. Yeah, man, I'm hyped up for this one. I mean, it's a pretty crazy journey you've been on. Decagon started 2023. So like two years ago, he raised $230 million already, which signals that things must be going pretty insane internally. And the crazy thing is you think about a high level. I mean, it's AI for customer support, which I would have thought, you know, it was kind of a self-problem. You've had chatbots for a long time, drift, this and that, and so many different players over time seemingly trying to solve this problem. And yet, clearly, you found insane traction. So we'll get into all of that, maybe as a starting point. Tell me just your background. What were you doing the last few years before you decided to start this company? Thanks so much for having me. Super excited to be here. Yeah, before I started Decagon, I'd actually started another company, also in the AI space. But this was AI for video at the time, because this was pre-LLMs. So there's a company called Helia. I got acquired by Scale in 2020. I was at Palantir for a while before that. And then Stanford before that for undergrad and graduate school. Tell me a bit about Helia. Like, what was it exactly when you started? Yeah. So we started Helia with some close friends of mine from undergrad in 2018, early 2019. And at its core, what we did was we kind of processed video data in real time. So at the time, what we did was we were like, okay, we're looking at these incredible vision models that are out there. And it seemed like one of the only things that they were being used for at the time in production was in self-driving cars. And we're like, okay, what these models are really good at is taking video data, extracting structure from them. Right, okay, this is a person. This is another car. This is drivable road. And then doing interesting things with that. And we're like, okay, there's all these workflows within the enterprise that used this as well. Enterprise security was one of those things. You know, you have tons of CCTV data within an enterprise. You have all these enterprises that are worried about security threats people breaking in. And we're like, hey, can we use some of that data and some of those same techniques to help there as well? Did you have some sort of security background? It was more of the Palantir stuff, like the AI piece that put you in this kind of journey. No, it wasn't the security background. It was more the UI background of these models are great at taking vast amounts of unstructured data right at the time. It's, you know, raw images from video beats and extracting structure from them and trying to find interesting insights. So a lot of it was, how can we apply these, you know, advancements in vision models to enterprise workflows? Which again, it's very similar to what we did here with Decagon, right? Which is how can we take these great advancements in, in underlying LLMs and apply them to enterprise workflows? In this case, you know, enterprise support and kind of customer interactions. And so you started that in 2018-2019. When did you, you said you sold it to Scally Eyes, all right? Yeah, at the end of 2020. Okay, so that was like a two-year journey? That was quick. That's quick, man. It was pretty crazy times because, you know, it started the pandemic. You know, the whole world was going upside down, but, you know, very, very different time than now. And what happened, like, high level? Did you get it into the hands of customers? Did you have traction? Was it just acquired more for like the team in the tech? Now, Helia was a much like quicker journey for us, right? Like, you know, one of the things that we, I think, really figured out how to do was figuring out from all this data that we get from customers, it's figuring out how do we understand what data we need to label to make these models a lot better. And, you know, scale was working on a bunch of similar things at the time. So that was kind of the most interesting part of what we'd built. Of course, you know, super different time than Decagon. Did you stay at scale, yeah, after the oxygen? No, I left pretty quickly after. So, you know, both my co-founders were there for quite a long time. So one of them stayed all the way through to the meta acquisition and went over to meta. My other co-founder was also there for about four or five years since and now he's president of cognition. So what did you do between 2020 and starting Decagon in 2023? Yeah, I spent a lot of time thinking about the next thing to build, really. So, it worked on a bunch of different ideas. And then, you know, Decagon was the one that really, really inflected. There's spent a lot of time thinking about, you know, how can I now take the next set of AI improvements and apply them to portfolios in the end of price? Chad GPT comes out end of '22. Were you tracking closely all the developments happening in GPT? Before then, or like, how big was Chad GPT of a surprise for for you who were like building an AI, you know, for many years? It was actually pretty remarkable at the time it came out, right? Because for the longest time, like, language models were the kind of second cousin of the vision models that like worked really well. And people were using it for all these cool things. But, and this idea that, hey, you can just train these models on tons of internet data and have them talk to you and generate like intelligent sentences. Was actually pretty remarkable, surprising, unintuitive result. And so it was pretty crazy when it came out. It was pretty crazy how well it worked. It was both the number of different ways people figured out how to use it. Right? Because at the start, it was a chatbot. And then people were like, oh, actually, if you take context and add context to the model, this idea of, you know, rag was also a pretty cool idea, right? Now it's become, you know, actually very quickly in two years, become, oh, this is a standard. Everybody knows what in context learning is. But at the time, it was pretty crazy, right? This idea of, you can have the model learn from new information at inference time. This was not something that was possible before kind of in context learning was figured out. So one, it was crazy how quickly kind of new techniques were layered on half of these models. And number two, it was pretty interesting how quickly the underlying models themselves improved for a long period of time. And arguably even today, the rapid pace of which the underlying models themselves improved was, was, was pretty astonishing. Did that lead to the idea of Decagon or were you already working on AI for customer support before tech, UBT concept? No, that was a big part of why we built Decagon out, right? Because the underlying shift was, wow, this is like really transformative breakthrough technology that did not exist before. That fundamentally allowed us to take this area that was very important to enterprises. And, you know, have them adopt this new technology, right? Because this idea of automation in customer support and customer experiences isn't something that's neat, right? There were chatbots for like 20 years at this point. But the interesting thing about that was most of them weren't very good, right? Like I've used chatbots before and like the first thing that I would do is go in and say, "Hey, Asian, Asian, Asian, because I know I'm like, oh, this is probably not going to help me." And the thing that was interesting was, now you have this technology that could adapt to the things that customers were saying it did have, you know, this ability to like learn from an enterprise's own data without needing to go and train custom models for them. So you've got all these like really interesting new properties about these models. And that's actually what kind of opened the window for new companies to be born in this space. So that's kind of the rationalized, you know, how it happened. But tell me the specifics of how you ended up deciding, yeah, we're going to build AI for customer support. Like where does the idea come from? Yeah. So both my co-founder and I were very sort of customer driven in how we approached problems to pick this time. By the way, was that true the first time around or was that a learning from that first time? That was definitely a learning from the first time around. I think a lot of first time founders and I also included at the time, spent a lot of time I think over intellectualizing the problem right, being able to figure out, oh, you know, what is the exact three-year strategy at the start? And I need to know what the exact plan is before I start doing stuff. And I think it's really easy to fool yourself into thinking you're doing like great important work. And then, you know, you have this grand strategy and you bring it out to the market and nothing works the way you thought it would. So this time we're like, hey, let's put all that aside. Let's just talk to customers. Like this is the only thing that's important. Let's talk to customers and figure out what their problems are. And so we were extremely customer driven this time around. And you know, we realized, hey, you have this really breaks through piece of technology. And you know, even pretty earlier on, we were like customer interactions, operations teams were places where there was a lot of overhead within enterprises. And you know, enterprises wanted to do better. And we're like, instead of coming up with this grand roadmap on day one, let's just go talk to a lot of these enterprises. Right. So we talked to, you know, over a hundred leaders of operations teams, leaders of support teams, leaders of sales teams. With the full blank slate, or did you have some ideas you were testing out with them? We had a couple of ideas we were testing out with them because, you know, going in full blank slate is hard, but, you know, we were pretty focused in. I think reasonably early on in this idea of, hey, using these language models to help augment or automate parts of ops teams, workflows, customer interaction teams, workflows was something that we were interested in. So the highest level idea was really LLMs for the enterprise to drive efficiency, trying to figure out, okay, where's the biggest pull? Correct. But the thing that that wasn't obvious to us at the time, actually, that was somewhat surprising, was how much of a blank space there was in customer support, right? Because from the outside, like you said, it seems like a space that would have been extremely crowded. But the thing that was interesting to us was the part of the market where, you know, you come in and you were like, hey, I want some basic FAQ stuff. I want to put down a credit card for $500 a month. That space was actually very crowded. However, the enterprise part of the market where you're like, hey, I'm a large enterprise. I have, you know, hundreds of integrations that I need into legacy systems. What's an example just to make it tangible, like a company selling what to who, like, who would be a good example of this, of this enterprise that you're talking about? One would be a company like Hertz, right? There are a hundred-year-old business, very, very complex sophisticated enterprise that are global, and also companies like Gororring, also at scale, very tech-forward, much younger company sells a very sophisticated, like tech hardware device. Like, you know, I love my horror ring. So it's not companies selling to enterprise. It's just that it is a large enterprise. And with Hertz, like, there's obviously a lot of complicated pieces of the product. Yes. And these are not companies selling to enterprise. These are large enterprises themselves that are very consumer-facing. Got it. Because both of these companies are at scale, millions of customers, very complex policies, lots of systems internally that they need to integrate with. So having AI support systems that can kind of handle that complexity, that was kind of a wide open space. And we realize this actually by talking to them, right? And they would tell us, they're like, hey, we really need something. And we have looked at solutions, A, B, C, D, and this is all the ways in which they don't work for us at all. And the interesting thing about that is this helps that intellectualizing part of that kind of early stage founder. But instead of doing it yourself, just go to your customers and ask them, hey, you know, there's all these other players in the market. Why haven't you bought one of them? They will tell you your competitive differentiation. They will tell you, you know, why all the other products in the market don't work out for them. And what you need to do to be able to like differentiate and be successful because all this ultimately comes out of customer need. They won't necessarily tell you how to solve the problem. That is your job as a founder, but they will tell you what their problems are and what they need fixed and why existing solutions are the market don't fix it today. And the other thing is when you come at it without too strong of an opinion, you get a sense of what's truly a top priority problem because a lot of times you come in too specific, you know, with this idea of trying to validate if this is a problem, you might get, yeah, this is a problem, but you don't find out what the bigger problem is that you never asked about. And that's the thing that's really getting budget. I got a bunch of questions about these conversations because I think this is this will really set a tone for what you decide to build and how and all this. First one is you had a hundred of these conversations. What was your way in like, what did you even say to these people for them to give you 15, 30 minutes of their time? Yeah, you know, a lot of this is just early sales hustle in a way, right? It's like using whatever connections you have into into a company, you know, one of our earliest customers. The way that we got in was my co-founder went to school with a PM there and he asked her for an introduction to their head of operations. Under the guise of, I just want advice or I just want to learn more about your job or I want to tell you something, what was the kind of context you set up? Well, I actually, at the time, it was legitimately, hey, we want to learn about, you know, the problems that you're facing and how we can help solve them. So, you know, we were pretty open about that. We were pretty good at using our early investors for introductions into a lot of these companies and also found a lot of time cold emailing, cold LinkedIning folks just to make sure that, you know, this was a problem that resonated with people, right? Like if you can cold emails, someone about something and say, hey, this is the thing that we're working on. Like this is a problem you have would love to chat. That's also some amount of validation. This is a real problem. The other piece I want to dive into a little bit is you went to like operations to sales into customer support. I assume that there were problems to be solved everywhere, but you kind of, you felt something different in customer support. Can you maybe just dive deeper on the comparisons? Like what you saw there in customer support that you didn't see as much of, let's say in sales or ops, just to get a sense of what true market need kind of feels like? Yeah. It was willingness to pay immediately, right? Like we, with a couple weeks of work, we were at the point where people were like, yes, if you can deploy this thing, I will sign a $150,000 truck immediately, right? And this happened repeatedly. This was on like a one off thing. So this ability to go from, here's what we're building. And you know, this V1 is going to be ready in two weeks. And it's going to cost you $100,000 a year. And we need that commitment from you up front. Yes, if, you know, we'll make sure that ABCD things work. But the moment those work, you're ready and willing to sign a check for this. Like that happened repeatedly with the support use kits just because the kind of customer pain was so deep and they were ready to buy. Did you try that with sales and officer? Did you get to that stage where like, okay, what if we did this? Would you pay and you just kind of felt like, okay, the poll, you know, these guys will pay these guys are kind of like, yeah, maybe we'll see. Yeah. So, so we had a number of different like ideas that we tried where either it was, love the idea is super interesting. And then when we went to that, oh, how much would you play for this conversation to him? Oh, you know, this quarter budgets are time. Maybe, yeah, come back next quarter, yeah, classic. Or it was, oh, yes, you know, this is useful. But, you know, maybe I would pay $1,000 a month. And, you know, we really need to pay month to month to see if this works. Whereas for support, it was, yes, this is awesome. If you can make it work, yeah, $100,000 a year, that's, yep, we can do that. No, not an issue, right? And so that at a stark contrast in willingness to pay, you know, it's just direct signal how much business pain is there truly. And then just so I have the timelines right, when do you like incorporate? When do you raise money? And then when are you having these conversations? Yeah. So, you know, both my co-founder and I were second time founders, so we incorporated like pretty much right away and actually ended up raising capital pretty much right away. This is like the game of 23. It's probably middle of 23. So we incorporated basically right away and raised capital pretty much right away. And then, you know, started having all these conversations right away. And your pitch to the investors was like, we're going to find a way to deploy in the enterprise. We'll figure it out sort of thing. We were actually preempted. We actually, and nicely enough, we'd actually never run a full fundraise for Decagon, because luckily, we ended up being preempted at every round. Our first round was led by A16z and a couple of folks, like, including ASTAR, because we just had very long relationships with all these folks for several years across both our prior companies. And that was a $5 million round? Yeah. 4.8, I believe. Yeah. Tell me just a little bit more about, like, take herds or any of these other, like, early customers, what it was that, whether it's drift or ADA or the million other chatbots was just unable to solve for them, why they felt there was still such a big gap. Yeah. So a lot of it was around this ability to handle complex multi-step workflows. I'll give you an example of what that means. Let's use simple kind of generic example of an e-commerce retailer, right? Like, one way, if someone comes in and says, hey, I need a refund, right? It's one thing to give them the FAQ version of how to do that, right? It's like, oh, you know, go to your account and then click on your order and then click, I want to return my order and then follow the steps. That's one thing. Anybody can do that. However, if you want something more complicated, more personalized to that end user, what it might look like is being able to say, okay, if someone comes in and says, I want to return an order, first, you need to go check this fraud database to make sure this user is in flag for fraud. If they're not flagged for fraud, then you need to check the CRM system to see if they're a gold tier member. If they're a gold tier member, you always want to let them execute their return. Otherwise, if they're a silver tier member, check Net Suite to see if the order was placed within the last 14 days. And if it was, then offer them a full refund and then, you know, hit the UPS API to print them a shipping label. Right? So this idea, I need to like work across lots of different systems, execute business logic, but also do this in a way that's very conversational with the end user. Like doing that was what was hard, right? And as you can, as you can imagine, like, if you can do that well, that really feels like, oh, this feels like there's a human on the other side. This feels very personal. It feels very smooth. Like, that's what was important. And that's what none of these other companies could do. Well, does that mean that you need to set up these kind of rigid if this, then that work flows to no, especially in e-commerce. There are some players that let you do that. And, you know, it can get out of hand pretty fast as like a PM is trying to figure out like somebody internally or on the support team is trying to figure out like, okay, this case, okay, you got to do, you know what I mean? Like, how do you know it's a great question. Effectively, what you need to do is you need to build a product that can do both well, right? So in cases where you want a lot of flexibility, you want to give the model a lot of flexibility to say, hey, just work with the user and find a solution. In cases where you do need a lot of rigidity, you need to have a system where you can constrain it down and say, you know what, for refunds, I don't want you to accidentally give a refund out to someone that's not eligible to it. So in certain cases, you want a lot of that rigid, if this then that logic, but in, you know, let's say, 80% of cases you want the model to be very flexible, very personable with that end customer. What was the first version like you mentioned at some point, you went out to, you know, even to sales and office with different ideas, but to customer support with like something that they would pay for. What was that that first kind of MVP like? Oh, actually, for this, we ended up needing to build most of this out for the MVP, but, you know, both my co-founder and I were technical. So this was something, you know, of course, it wasn't the full robust platform that we have today. The first version was probably something like co-founder and I built in three weeks for the first set of paying customers, right? And, you know, we were, we were very scrappy in the early days. Now, by which, I mean, because both of us were technical, I think we probably built up to around, you know, the first million in revenue with just the two of us and those probably in about six months or so. No other employees. Yeah, yeah, nobody else. I think we got our first employee, Amy, who's great. Probably when we were around, like, I think 950 K&R or something like that. What was the reasoning behind that, especially since you kind of figured like you knew what to build, why not get at least five engineers or ten engineers in? Well, it's kind of because in retrospect, it was the right thing to build. And actually once we settled on that initial idea, this is probably, you know, a month in, we did not pivot at all. But this is something that was obvious in retrospect, right? Like at the time, the goal was, hey, we want to be very nimble up until the time where we're like, hey, we definitely have, we feel like we very likely have product market fit. And, you know, at the time, it was just the two of us, we were moving fast, you know, it's like call customers during the day, code at night kind of a thing. And honestly, a big part of it was also we're spending so much of our time talking to customers and coding that we didn't have as much time to go do recruiting goals. Was this also learning from first drive? Because it's another difference that I find a lot of times repeated in first time. Like, you don't realize just how much being nimble and flexible matters until you face the time when you're not nimble and you need to change fast. And you're like, oh my god. Yeah, yeah. No, exactly. This was something that we that we kind of learned learned from the first companies. Luckily for this one, it was a much shorter period of time needing to be agile and nimble in terms of like, you know, shifting completely pivoting the company. But yeah, it was something we were pretty cautious about. And, you know, it was what let us very rapidly iterate to this idea in the in the early days. So tell me about like after three weeks, just tell me a bit more about what that product did. And again, the use case for that product. Honestly, did a lot of what I said before it had the ability to, you know, do this capture this enterprise complexity. I'm like, do workflows end to end. And at the time, we didn't have the full self-serve capability. So it was a lot of custom builds that we'd built out for these enterprise customers. And so from there on, like essentially what we were trying to validate was not, can we build this amazing platform like product where anybody can do anything in it like that wasn't the goal, right? The goal was to say, if we custom build everything perfectly for one person, can we give that person a great experience, right? And then once we got our second and third customer, if we were to build something perfectly custom for this person, can we give them a great experience where they're willing to pay for? And then once you build the first three, you take a step back and say, okay, we can't do this for customers four through 10, right? And so what is common amongst these one, two, three customers? And then how do we build that into a great platform? We have tens of thousands of people who have followed the show. Are you one of those people? You want to be part of the group? You want to be a part of those tens of thousands of followers? So hit the fall about it. One of the things that struck me as you said that the amount of complexity that you're handling is the standard way to do it would be to verticalize, to be like, okay, we're just going to do say e-commerce and everybody need commerce needs returns and they need coupon codes and they need, you know, here are things that happen. And so we'll just get super deep. But you didn't, it doesn't sound like you did that. No, we didn't end to be clear. There is an absolutely world in which that's the right path to take. But what we noticed was the problems that someone like aura and someone like invent bright and someone like herds face were very similar actually. And this was somewhat counterintuitive at the time, but we were like, hey, let's just be a completely horizontal platform, right within the enterprise, of course, but across enterprise industries because of the problems that all these large enterprise companies faced were very similar. But the way to do that, which is not, I would say, that common is to not worry about scalability, not worry about platform, just go in super custom, deliver the full value because this is the problem. If you try to get everything like you triangle horizontally, try to go platform, you'll have a hard time delivering true value, full value. And then, you know, you don't get going, you don't get the customer love that you really need. That's absolutely right. But the way that we kind of got around this was by going very deep with every customer, not worrying about scalability in the early days. And by the early days, I mean, months, you know, kind of one through four. We don't worry about it. And months four through 12, we're like, okay, great. How can we with a more platform like approach give all of these people the exact same experience, right? So at no point did we ask any of our customers to compromise on that fully tailored feeling that they got the hard challenge for us there was how do we build a platform where you can configure it to be something that is that that kind of fits like a glove for every single one of our customers. Then that of course is the is the interesting product challenge. And then for those first few customers, how did you structure the sale like was it a pilot? Because also there's this worry about hallucinations and stuff like a lot more back then. How did you kind of make them be willing to take the risk? Because it's pretty critical. Like at the end of day, you're serving customers. I mean, if you do a really bad job, there's big impact. Yeah. So again, we'd built out, you know, even in the very early days, we'd built out a number of controls to do things like detect and cache hallucinations and prevent them from going out to the end customer. So we'd built out a lot of that. And in terms of the sales process, we structured it like a regular sale, right? So we said, okay, here is the product that that we have today. Here are the changes that are coming in the next two to four weeks. We're going to do a pilot. It's going to be for not a very large fee for, let's say, four weeks. And after that, we're going to move into a full annual contract. And you know, it's going to cost, you know, let's say at least $100,000 or something like that, right? So reasonably meaningful amount of money. And it was all pre-signed up front because a lot of these things, I mean, they matter in terms of speed. Like a lot of times startups will get, especially something enterprise, which move at a different speed than a startup does, you'll get stuck in like pilot land, for example. Like, yeah, they love the pilot, but now we're trying to work to commercial. Did you do anything there to streamline that conversion? So in a way, this was a part of our kind of pain validation, right? Like if you get stuck in pilot land forever, in a way, this is signal that the enterprise doesn't care that much about the problem that you're solving for them, right? Because if they really care, they want to your product in production as quickly as possible. So for us, we actually didn't end up with too many of those problems because this was a true deep digital pain that these companies were facing because they were like, look, we have a lot of customers reaching out to us. We can't get back to them in time, right? And being able to give our customers a better experience is such a high priority that they're like, great, if it's, you know, people getting stuck in commercials land, let's kind of bulldoze through that. Let's get to a place where we can deploy this in production because it works for our customers. What was the KPI that you center around? Like when you think about ROI, a lot of these things are like, oh, cost reduction because you need less customer support people or maybe it's like NPS, but that takes a long time to measure what did you center in on as the thing that you delivered value on? Yeah, it was two things. It was what percentage of conversations can we handle ourselves? And what is the NPS for those conversations, right? Because you can handle a conversation by just frustrating the user, right? And we want to make sure that our customers feel good about the conversations that we handle. So we're like, great, we'll show you that, you know, we handle 80% of conversations ourselves. And those 80% of conversations have a high NPS score, so that, you know, both of us feel good about the fact that we are actually solving the problem for these end customers. So you would ask, at the end of the chat, like we, you know, rate this conversation, one to five, sort of thing. Exactly. And from the perspective of the buyer, is there ROI ultimately mainly around this higher NPS? Is it around having less customer staff? Like, what was the biggest thing for them when they thought about spending this, you know, 100k year on deck of on? Yeah. So it was the way that our customers thought about ROI different from customer to customer, right? For some people, their business was growing so quickly that they were like, wow, to just keep up with the scale of customers, which meant an equivalent scale of customer support increase. I'm going to need to like double or triple the size of my, my human supporting. And they're like, I physically cannot hire enough people to do that. And therefore, if that could on comes in, I can, you know, pause hiring, you know, instead of doubling my team, I increased it by 10%. Right? So that was kind of number one. And number two, which interestingly enough for a lot of customers was also, they would expand the amount of support that they offered. Right? So instead of saying, oh, you know, we offer support for only our premium members from 9 a.m. to 5 p.m. Monday to Friday, it became everybody gets support all the time, right? Because there is this like significant kind of latent demand for support in a way, right? Like the tiny paper cuts where it's just annoying enough where you're not like, I don't want like dial a number and call someone and have them fix it. And most of these brands were like, Hey, let's just make support available to everybody. Let's make it easy. Let's make it available all the time. Because at the end of the day, that's what leads to happier customers that activate more often and are more engaged and just get their problem solved immediately. By the way, had most of these enterprises tried out and paid for other solutions and then churned out, or most of the cases, they saw the demos and said, this is just not going to good enough. It's not going to work. Now, a lot of them had tried things before, which was also another signal that we live for when validating ideas, which was they're like, Hey, this is a pain. I'm going to go try a bunch of solutions on the market because this is a real pain that I'm trying to solve. And so most of the customers that we talk to back then and even today have either seriously looked at or tried a number of solutions on the market. That's why we were also able to hone in on what is valuable to build because our customers were very informed and educated at the time. And they would say, Hey, we're looking for A, B, C, D. And these solutions provide A and B. But the C thing is what's really critical to us. And we haven't been able to find that on the market yet. That is a great indicator. Obviously, when a company is already spent on trying to solve this problem, clearly, it's a problem. The only flip side to that is sometimes you almost have like burnout, which is like last-ten companies have promised me that they could do all the stuff. And then when I put them in, they can't do it. So like, why are you any different? Did you have some, was there any of that burnout in the market? Not yet. But again, the nice thing about it being a really painful problem is again, the customer really wants to solve it, right? And you're right. There were some customers that are like, Hey, I've been burned by this before because these, you know, these other vendors would come and tell me they can do everything. And why are you any different? But then the burns on us to kind of say, great, you're worried about, you know, these two things, let us de-risk them for you right away, right? If it's inability to handle really complex logic, we're like, great, let's start in the pilot just doing the complex logic workflows, right? And we'll show you right away that we can handle those well because, you know, if we can handle the complex ones, well, obviously, you know, we're going to be able to handle the simple ones well as well. Let's talk a little bit of just about competitive dynamics in AI. This is something that I've noticed and, you know, it's like pretty calm and so it's like, you know, I've noticed something unique, but it's really changed pre-gen AI post-gen AI. I mean, competitions always been a thing, but I almost feel like pre-gen AI was a bit of a secondary concern. I mean, you're worried about incumbents and systems of record, but new entrance coming in was like, if I have enough of an edge, it's just going to be hard. I mean, they could copy me, but, you know, I'm always going to kind of be a little bit ahead with AI. It's just things are changing so fast that you have to worry about, you know, foundation models. You've got to worry about incumbents. You've got to worry about new entrants. And in this particular thing that you're doing, you know, one of the things would be, well, AI for customer support, I mean, there's going to be so many people trying to do this now that the LLM's can do all the language piece. How do you think about competition? How do you think about that whole piece of it? I think in terms of competition, I think the dynamics really differ based on how much of the value you provide comes directly from the foundation models themselves, right? So I think in some spaces where 90% of the value provided just from the picking the right model and the model doing the right thing, I think they're competitive dynamics are much, much higher because again, like the only thing that comes down to is is your model better than the other person model. I think in the space that we're in, right, which is customer experiences specifically targeted at the enterprise. Yes, having the best models is really important, right? We spend a lot of our time on taking the best models, fine tuning our own models in house. We have a research team in house as well. But there's also a lot of other things that you need to do to be able to deploy this well within the enterprise, right? You need really good versioning and roll outs, right? Like you can't just hit save and have this deploy to a million people. You need really good tests and automation. You need really good insights and analytics, especially if you're having a million conversations a month. You know, one's going to go read all that, right? Like what can you learn from what your customers are trying to tell you? So basically, there's a lot of software you need to build, and this is like, traditional non AI, almost like, you know, regular SaaS software that you need to build around something like this to make it work within the enterprise. This idea of, you know, not all the value just comes from having the best model. That's where they actually competitive dynamics changed quite a lot. Well, I assume in your case, you know, all the natural language processing, if you want to call that, is from the model, like the chatting piece, but then the actions behind it, the workflows, if this and that, the business logic, that's the part that has nothing to do, frankly, with the models, I would think. Of course, we invest very heavily in our kind of agent orchestration system. In my view, I think we have some of the most sophisticated orchestration systems out there on the market today. But in addition to this, it's all the other things that we built around it to make it enterprise-ready, right? Like, you know, deep integrations with their existing systems, the abilities like version, rollout, experiment, analyze, conversations, it's a skill like all this other software that you need to operationalize it. I think that's what makes it particularly sticky within the enterprise. And then what about existing startups that already have distribution? So, A to support is one I keep mentioning just because it's in Toronto and it happened, actually, interview Mike on this podcast. He's one of the first PMF show that we did, like four years ago, but it doesn't have to be them. I mean, any of these companies that already have tens of millions and not hundreds of millions in AR, they already have distribution. They have customers. So they would have a lot of the things around it that you're talking about when it comes to delivering products, the enterprise, but obviously they started pre-Genei. You know, how easy is it for them to flip a switch and all of a sudden be like, oh, we're actually Genai II, you know, we can do all this conversational stuff? It's actually quite difficult, because again, like I said, the agent orchestration piece is also quite hard, right? Being able to do simple FAQs, not hard at all, this is just directly, you give the model a bunch of stuff and say, hey, what's the right answer? This is in fact, building a chatbot over your data as like the hello world version of AI these days, right? But the hard part is being able to say, okay, when I'm an enterprise scale, I want to follow these complex workflows well, like doing that well is actually quite difficult. And I think that's where the, you know, initial wedge for Decongon to get built out was created because doing that well was hard. And I think our kind of Asian operating procedural approach seems to be what is one of the market. So let's talk a little bit about go to market. I think, you know, go to market tactics is something that frankly every early stage founder really cares about. So, you know, you've got your first three customers, you hire Amy kind of first employee, you start to build out more of a platform. So on the product side, you're probably feeling like you're onto something. How do you go out and close the next 10, 20 customers? Like, what's the approach? Honestly, a lot of early code market is the same early sales hustle as finding the people to talk to, to validate your idea, right, which is find any connection that you have into the companies you want to get to, right? Is it an old class me that is a coworker there? Austin for an intro and leverage your investor network to get introductions to companies, cold email people. Ask your existing customers. Actually, existing customers are a great source of this because they are friends with their peers and other companies, right? They've met at conferences and meetups and, you know, they've interviewed for the same position and things like that. So they know each other. So going to them and saying, Hey, like, you know, you're a happy customer of mine, which of your friends do you think would find something like this interesting and useful? So a lot of it is just doing the again, unskilled, well, thing. And at a certain point, of course, you have a sales team and a marketing team's generally inbound and SDRs and things like that. But up until that point, it is just doing the same things that you did to get those first 10, 50, a hundred conversations. I'm curious if you agree or disagree with this. But one of the things I've found doing these interviews is that because I'm talking mainly to founders like yourself who are on, you know, kind of crazy growth trajectory. And I find a lot of founders' perception of these companies is they must have figured something out on the go to market side. They must have figured something out on the distribution side that I haven't cracked yet. My feeling or my learning, let's say, talking to people like you is for the most part, the stuff they're doing on go to market is pretty vanilla. The thing they've unlocked is insane value delivery and frankly, like insane value just sells itself. I curious what you think about that kind of that statement. Yeah, I think for most companies that grow really fast is just because they've identified the right pain, right? And they're solving the right set of problems that you could have an amazing perfect go to market motion. And you know, you've done all of the tricks and all the standard stuff and you're just doing all that perfectly. But you're just not solving the right problem where you're solving a problem. Nobody really cares that much about and you're not going to grow very fast. Conversely, you could just solve a problem that some subset of people care a lot about and they're willing to pay a lot for and you just have the absolute worst go to market motion and you will inflect very quickly. All of this just comes down to picking the right problem. How much did word of mouth referrals play a role if at all through your growth trajectory in last years? It was important, but we're a very enterprise product, right? So unlike a consumer business, it's not that, you know, it's just all word of mouth and you know, 100 people sign up the next day kind of a thing. Word of mouth was very important though because within the enterprise, trust is really important when making a buying decision, especially when it's, you know, hundreds of thousands or millions of dollars. So being able to point to other happy customers was extremely important for us. Like even today for a lot of our larger deals, they'll say, "Hey, can I talk to some of your other existing customers?" What about time to value? Like that's another area that I've noticed matters a lot more than people realize, especially in enterprise where you know it's going to take a long time, but finding a way to deliver some value as fast as possible tends to have a big impact in terms of conversion rates and speed and all these sort of things. Is that something you think a lot about and have you done anything interesting in terms of being able to deliver some value, you know, really fast? Yeah. So the nice thing about, you know, about a product like this is it's very obvious when it works, right? And it's very obvious that it can create a lot of value. So we were pretty rigorous, like, you know, even in like early sales calls, like we'd show up and we'd say, "Hey, you know, don't worry, we already scraped your help center, like anything we could find publicly." And, you know, we made some example AOPs, like agent operating procedures for what we think some of your common workflows might be. And here, let's go do this together. And oh, you know, we didn't, you know, we didn't guess this process right. Like, great, let's change it right now and show you what it feels like, right? So for a customer, it's very easy for them to say, "Oh, you know what? This actually seems to work." And if it's worth, it's pretty obvious that it can create a lot of value for them. I like that. Actually, what's an example of something you might have done in a demo? Because obviously, just answering an FAQ was not it. And yet, you're not plugged into the database, you're not plugged into their stuff. You can't really like do a refund. So what would be an example of something you might have put into that kind of a demo? Right. You can mock an API call or a database call or something like that, right? So for instance, when we went and spoke with one of our airline customers, right? Instead of saying, "Oh, here's an FAQ thing." We're like, "Hey, let's build out what your reimagined check-in experience could look like." Right. And so we built a, if someone comes in and says, "Hey, I want to check in." And you know, we'll show them, "Oh, this is the seat that you currently have on your airplane." And oh, by the way, because of this status, I see you checking a bag, but don't worry, that's not going to cost you anything. And oh, this is because of this status, we can offer you an upgrade here. And it's going to cost this many miles. Would you like to do that? We can kind of simulate all these things without actually having to do it. So you can paint a very vivid picture of what it would actually look like in practice instead of showing like a pre-cam demo from an industry that has nothing to do with them. So you would tailor it, but it would be obviously like within a box, but it would still be really eye. It's not like you're just, you know, clicking through an interface. So you could still, you know, they could say, "What do you ask it this?" And then it'll figure out what to do. Exactly. And we'd tell them, we're like, "Hey, we don't have access to your databases, obviously." And so this is, you know, fake data that we made, but the workflows are best guess at what we think your actual internal workflow is. And if we need to change anything on the flight, let's just change it right now. We'll show you how easy it is to do that. How many employees are you today? We are about 225 people today. It's insane you just to imagine like internally as an organization, market pull aside, just what it's like to have that many people join a team, like, you know, that quickly. Yeah. Yeah. And then, you know, we had to be very thoughtful about maintaining culture as we grew that fast. Are you in person? We are in person. So we have offices in San Francisco. We have an office in New York. We actually just announced our London office a few, a few weeks ago, now I think. So yeah, we were an in-person company. And then you mentioned you hit a million, about a million ARS six months in. How was that ramp to like 10 million? Like how did that, because a lot of times how fast you had a million matters, but how fast you get the 10, you know, tends to matter a lot more. Yeah. So we don't share that revenue growth here publicly, but it was quite fast offer. So we were able to like maintain actually like substantially, increase the rate of which our revenue grew from from one to 10. Perfect. Well, listen, let me stop it there. And I'll ask the kind of last three questions that we always end on. The first one is when did you feel like you'd found true product market fit? I think it was probably around the, you know, fifth, third, sixth customer that we had because of two very specific things. One, they kept telling us the same kinds of problems that they had, right? They're like, this is the problem that I had. And which was the same as everybody else. Here are the other solutions I looked at on the market. And this is why it doesn't work for me. And that why it doesn't work for me was similar to what everybody else was telling us. And when we showed them our product, they were like, yep, this is great. I'm ready to buy and I'm ready to buy quickly, right? So carrying that same thing repeatedly from like, you know, the fifth, sixth customer like in a row in a pretty short period of time. And everybody's saying, yep, I'm ready. I'm willing to buy. Let me sign up, you know, 100, 200K check right away. Like that, that was what was that gave me confidence. I'll just go just a bit of attention here, but we talked about competition earlier has has like at that time, you were selling a bit into vacuum in the sense that nobody was able to offer what you were offering. Is that still true today? Or now you finding yourself like most deals are competitive deals where it's a bit of a big off. And there's other people kind of at the table. Yeah. No, I mean, even in the early days, you had other people at the table, right? But a lot of your values showing, hey, here are the three things that you care about. And this is why we're the only people that can do it, right? And like, what those things are might change over time as a market mature. But for instance, even today, like one of the really valuable things we have is this like this idea of, you know, the Asian operating procedures, being able to allow non-technical people at your company to in natural language build these like very complex workflows. So, you know, that has tended to be constant throughout. But then, you know, over time, we kind of add new things that different people care about. So, for instance, several of our customers in more regulated spaces care a lot about our testing and simulation feature. So that, you know, every day, we can simulate hundreds of conversations that they care about and show that, yes, we're still handling these as your internal teams repires to handle them. So the kinds of things that customers care about changes over time as a market mature, but a lot of the core things are still remain the same. And then was there on the flip side, like, was there ever a time where you actually felt like things might not work and things would completely fail? You know, luckily for this company, we've been growing so quickly and there's just been clear market demand that that that didn't happen. Well, you've built it one and a half billion dollar company in two years. So, yeah, I can't imagine there were too many like near-death moments, hopefully never, but certainly so far. Yeah. People are talking a lot about this, at least at talking about like one person billion dollar startups, right? Like, you've raised $230 million so far. As you said, you've never actually gone out and done a roadshow. So people are just, you know, effectively preempting you, but you're deciding to take that in. What is your philosophy for deciding to take that money? Are you using it to grow and hire? Or are you just, a lot of it is just sitting there and it's just like just in case? How do you think about that? Yeah, we've actually been very capital-efficient in that we've spent very, very, very little of the money that we raised. A lot of the reason to raise capital has been we're building a big team here because again, like really taking advantage of this opportunity requires that we have the best research engineers that can build us the best set of models, the best infrastructure engineers, so we can build something that's scalable to the entire enterprise. And secondly, it's about bringing the right partners on board, right? Because we're trying to build a company here that will ideally outlast all of us. And having the right partners on board to help us get through that scale is something that we're looking for as we raise capital. Last question. What would be like your top piece of advice for an early-stage founder that's looking for product market fit? The only thing that matters is what customers care about, right? So don't spend any time ideating by yourself in your head, just go pick some types of buyers, right? Maybe it is I'm going to go talk to every VP of marketing that I can find at a 500 to a thousand-person company and just go ask them what their problems are, right? And then try and figure out what is it that they care about that you're hearing repeatedly that you can build a great product for? Because if these are, you know, reasonably informed buyers, they'll know what's out there in the market. And if they still say it's a problem for them, that means they have some unmet need. So instead of guessing ideas yourself, just go talk to real buyers and find out what their problems are, that's going to be the quickest path to park market. Ashwin, thanks for jumping on the show, man. It's been great having you on. Yeah, thanks so much for having me. Wow, what an episode. You're probably in all your absolute shock. You're like, that helped me so much. So guess what? Now it's your turn to help someone else. Share the episode in the WhatsApp group you have with founders, share it on that Slack channel, send it to your founder friends, and help them out. Trust me, they will love you for it.