The Exchanges

Q And, and being a model company, can you walk us through like, ah, because now models have become really popular, walk our audience through the infrastructure required to build a model company?

A Yeah. So, um, you know, I think a lot of research companies, um, Need, like, many different moving pieces to, um, actually build the product, which is the model, ultimately. So for us, like, it, it goes from everything from, like, infrastructure that's required to build massive data sets to pre-training to post-training to, um, you know, the architectures that we design and develop, uh, to how they're implemented on, you know, hardware. To how the training infrastructure actually works and how efficient that is. To, you know, how you actually then take these models that we train and inference them. And then how do you optimize that inference? To how do you take that model that can be inferenced on one request and make it possible for it to be used on, you know, tens of millions of requests. Uh, to, um, you know, how do you actually then take that and turn it into a great API? To how do you then make it easy for people to understand how to use and control the model for what they want? So these, these are all of the pieces that you need to really build a great, great model and, and to put it in the hands of a user. Um, I think model training and building models is a bit of an art. And the reason it's a bit of an art is In a lot of these new areas, like multimodal models, like, there's no, Known recipe, right? Like you can't just go, go to the internet and say like, hey, this is h…

AI assessment note: “it goes from everything from, like, infrastructure that's required to build massive data sets”

Q And can you tell us since you raised your pre-seed, you raised your seed, Uh, the progress of the companies since then all the various milestones that the company achieved?

A Yeah. So we've been, uh, you know, we've been operating pretty much like a model lab. So we've been, uh, to start with pretty focused on our voice models. Um, voice has, I think two major problems. How do you understand what somebody is saying? And then how do you respond to them? Right? And then the middle is this big part, which is let's think about what the user is saying and help them accomplish their goals. Right? So that's, those are the pieces when it comes to an interactive model. Um, we've been really focused on being super, super good at parts one and three. So how do you listen and how do you respond? And a lot of our progress in the last couple of years has been a, Building this, like, foundation for interactive models. So for example, uh, there's a lot of, like, hidden work that goes into this. So as an example, we've built our own inference engine. It's pretty important because we think that these interactive real-time models are going to need to run in a pretty different way. So having the ability to have your own engine that is not designed for LLMs, it's designed for these, this new class of models is very important. So a lot of foundational work, firstly, we kind of laid the groundwork on that. Secondly, obviously releasing models that are, um, super high quality, very fast for, You know, text to speech, um, speech to text, uh, being able to build, uh, uh, rea…

AI assessment note: “a lot of our progress in the last couple of years has been”

Q So building the company, what part of your journey it was clear to you that you want to build a company which is focused on research?

A Yeah, it's interesting because like, I didn't want to do anything except research for a while. Um, you know, when I was in my third or fourth year, I think I had a conversation with my advisor about graduating. And, um, And I think I, I sort of actually said that I wanted to stay longer to do the PhD, which seems strange, but I think I felt like I was learning a lot. Um, and I like research. You know, I think research is great as a intellectual pursuit. I think it's fun to do research. You know, it's exciting. You are discovering new things. You're trying to push the field forward. I think it's just enjoyable, right? And I think doing research in academia is super fun. Actually. So I just wanted to do more of that for a while. Uh, but eventually I think what we realized is we made enough progress where, um, it didn't feel as valuable to do the N plus one-eth academic paper.

AI assessment note: “eventually I think what we realized is we made enough progress”

Q And, and today, If any industry points to being one of the, the highest consumers of Cartisha, what would that industry be?

A So we work with a lot of folks across, um, uh, I would say Healthcare, FinServe, regulated industries is where we see a lot of, uh, use cases and, uh, volume. I think, um, you know, we're a, we're an interesting company in the sense that because we're an enabler, uh, you cannot build voice agents without the voice models. So, so we kind of plug into everybody in terms of the stack. Um, and so, but these are the areas where we see a lot of the use cases, I think, that are really driving a lot of volume and traffic. Um, I think that every week we see, see new use cases pop up. So I think that's what keeps it interesting because there's a huge amount of experimentation happening. I think the cool thing is there's the experimentation, and then there's the stuff that's going to production. And both are, like, pretty crazy right now. But the experimentation is quite interesting because, as I said, like, people are just thinking, like, oh, I have this problem. Can I, can I use voice to actually make this more frictionless from an experience? For example, you could think about things like internal tools, right? Like, How can I give my employees access to more information? Again, a really nice place for you to use voice as an interaction, ah, interface, so that they can actually access answers as, as easily as possible. So, like, dropping the friction with which people can access inform…

AI assessment note: “I would say Healthcare, FinServe, regulated industries is where we see a lot”

Q So in your case, how did you measure that, uh, you had product market fit and when, at what point in time you crossed PMF?

A Yeah, I think, um, you know, you, you start to have, uh, customers and then you realize that, uh, you, um, Start to get, like, repeatability across customers, right? I think that's one of the key things. Um, people come to you and they, um, already know about your product, they know what you can do for them, and, and it becomes a lot easier to, um, for, to, to get customers in the first place, right? So I think there's that, you know, sort of like natural cadence to this, and I think, uh, especially for a business that's, like, Model oriented. You know, there's a certain, you know, simplicity to it, right? That I think is quite nice. Um, so, so that's one part of it. I think, um, we also started to see, obviously, like, um, uh, You know, consumption, volume, scale on top of our platform, like how much do people actually rely on us for their production workloads, right? Because ultimately, like for us, what matters is the consumption that is going through our systems at scale, uh, for customers globally. Um, and so we're seeing customers that are starting to go towards pretty insane volumes now on our, on top of us, like anywhere To like, um, order one billion minutes, right? Getting to that scale a year for one customer. And, um, that's pretty insane, right? Um, the amount of, um, conversations that are happening through Cartesia and the diversity of use cases has just gone up.…

AI assessment note: “consumption, volume, scale on top of our platform, like how much do people actually rely”

Q When you are four founders, how do you bring alignment between all of you to focus on a single or maybe two directions at max at the same time?

A I think we're, uh, as founders, we A, are like super honest. Um, I think we, Try to communicate in a way that's super transparent, all the time actually. I don't know what it's like, because we never had other people in the room when we were doing these things, but I think we debate pretty vigorously. But also we have like a pretty high trust amongst the founders, like super high. So we're able to actually have, I think, pretty fast alignment on most things, and where we debate, we debate vigorously. I think it's just about being Uh, actually pretty close to all of my co-founders and actually like having pretty high trust there in the relationship where you can like say things to them and they can say things to you. And like you don't take it personally and everybody's there to win. And, um, that's pretty important, I think. And a lot of companies, I think where they struggle is that the founders don't, you know, sort of like have full alignment. I think we're lucky Uh, that we have, uh, a founding team that has like, uh, the humility to align, right? They can set aside their egos, uh, to, to get on the same page to ultimately move the company forward.

AI assessment note: “we have like a pretty high trust amongst the founders... pretty fast alignment”