[MUSIC] >> Welcome to Category Visionaries, the show dedicated to exploring exciting visions for the future from the founders or in the front lines building it. In each episode, we'll speak with a visionary founder who's building a new category or reimagining an existing one. We'll learn about the problem they solve, how their technology works, and unpack their vision for the future. I'm your host, Brett Stapper, CEO of Frontlines Media. Now, let's dive right into today's episode. [MUSIC] >> Hey, everyone, and welcome back to Category Visionaries. Today, we're speaking with Demetrius, CEO and co-founder of Intelligentsia AI, an AI drug development platform that's raised 15.5 million in funding. Demetrius, how are you? >> New one. Thanks for having me, Brett. >> No problem. We're just joking there in that pre-interview. Your last name is a bit hard to say, and I didn't want to insult you to kick things off with our conversation, so could you go in to say your first name and last name just so we know how it's properly said? >> Absolutely. It's Demetrius Kaltzas. I've been living in the state the last 10, 12 years, but I grew up in trees and that comes sometimes with hard names. I co-founder and CEO to Intelligentsia AI. We're a pharma tech company, pharma being pharma supercars, no agriculture, living in New York, spending a lot of time in trees where we have our major R&D hub. Also around Europe with new customers. I've lived actually in four continents. In the US, Europe where I grew up, I've lived in Africa, working in the social sector and I've lived also in Singapore and the Middle East. >> Did you start your career as a lawyer? I think I saw that on LinkedIn. >> I did, yeah. I started as a lawyer. I have a non-linear career path, and often I joke that I'm the least scientific person on an art team. So I started my career as a lawyer. I've done a bit of engineering, so I dropped out from the engineering school. Then I joined the consulting board with my Kinsi. I did my MBA, I didn't say that and that's the year in Singapore. I went back, actually I went to Africa to do some social sector work. Then I went back to consulting. I worked for a couple of years around Europe, the UK, Switzerland, Germany, the Middle East, and then I moved to the US. It's an interesting story actually how I moved to the US because at the time I had co-found an art company that was exporting spin-out spies and sea spies to New York delis, and drink traditional spies. I had a co-founder who was fully based in New York, and another one was based in Europe and I was a part-time. I got myself a project with my Kinsi, somewhere in Pennsylvania, and I would spend my weekdays outside field in King of Prussia, and then my weekends in New York, knocking on the doors of delis and child companies, people to give it a try with our drink traditional spies. Talk to me about the early days of the company. Understand it was founded in 2017. Take us back to 2017. In 2017, there's a bit of what people say, you know, it's all about timing. So I was with my Kinsi, my Kinsi was experimenting with big data and AI, and they had what they called new ventures. And at some point, I didn't want to consult them anymore. And I was offered the opportunity to move into new ventures and actually build the third culture from a suit color and the both track discovery and track development. And that was, I don't know, a wow moment. It was early days, AI, that was 2015-16. And I was building a team of data scientists and software engineers and we're out there discussing with the industry and collecting ideas on what could be done better with AI. And yeah, one of those ideas actually came the course, what we do at telecentsia AI. And my kids within a half-incisional patients or risk appetite work on this. And that was a bridge to common. So summer to fall, 2017, I left my Kinsi and with my co-founder, we started telecentsia AI. Yeah, happy to share more of the early days stories. Yeah, what were those early days like for you? Or maybe let's narrow it down to the first six months. What were the first six months like? No, sometimes I make the metaphor of it's like being in love, like falling in love where everything is frozen and you're very optimistic. And somehow you have a big disregard for risk, which helps a lot, very creative days. And, you know, also solving in days. Oh, very scrappy days, I would say. Our share of data science was the first person showing the company beyond me and my co-founder. She literally used sleep on my sofa in a shoe box apartment in New York. So, you know, in NSF, where you are, people talk about working out the garage and that's how they started the company. New York is working out of your sofa bed. So it was very scrappy, it was very kind of butterflies in the stomach, a type of feeling like everything is possible. And, you know, let's create something, let's up in the world. It was also tight. You know, we didn't have any source of income as a company, we're literally bootstrapping the whole thing. And I was dipping into my savings. I never looked back. So I think with the right thing, I would love to leave those days. And sometimes I hear from other founders that they relish those early days of falling in love. Yeah, what I often do is I try to leave it by maintaining some of the elements, some of these early day elements and culture in the company. I think there's something so magical about the early days of a venture, you know, like that sense of urgency and the fun, you know, everything's fun in those early days as you're first building something and you're so excited and things just move so fast. I feel like going from zero to one really is the most enjoyable part of the journey for many founders. It was, it wasn't many ways. Still, what I've come to appreciate is the evolution. You have to reinvent yourself. Which means it never gets boring. The shift is that, again, back to metaphor of falling in love. At some point, it feels like, okay, now you deliberate the baby and suddenly you have responsibility and you know that the baby stays up at night and groups and you get sick and you have to take care of it. So what changes over time for me is the sense of responsibility and that's limiting in many ways but it helps people mature and that's a good thing. - The baby analogy is very personal. I just had our first baby or my wife had our first baby. - Oh wow. - Last week, so that's my hit song. - Congrats and the best of luck. - Thank you, thank you. - How did you land your first paying customer? That's something else that every founder struggles with. You have an idea, it's innovative, it's exciting, it's cool but getting someone to actually give you money for it is not easy to do when you're a startup. So how did you pull that off? And what stage in the journey was it? Was that six months after launching? Was it 12 months? Was it 24 months? When did you start landing that paying customers? - Yeah, so we started for 2017 and we beat on MVP, right? I mean, many people come up with an idea and they start sharing it with the world and trying to secure a contract and it works quite often, then they build. We started product first and for many years, actually we have retained that money but we built something, we had the first results in sometime late spring, 2018. And I was like, wow, we sold the problem, right? We keep pushing the boundaries every day since then. But that was a moment where we started really reaching out to people in our network in life sciences and it took us actually a few months. We raised money back then and then it took us a few months and we really signed the first customer early 2018. And it was kind of a bit of a money ball moment. I don't know if you've watched the movie. - Yeah, exactly. So I was often watching these in the early days like literally with my co-founder and data scientist like sitting and watching the movie and then going to the customers was really living 'cause you're this, you're this choppy young guy who speaks at different languages. And people are not sure they get it, right? I mean, early days of AI and the farm mom is not as risk-prone as our industries or the innovation propends the, you know, it's very specific to creating drugs but when it comes to technology, it's unfortunately a bit backwards. It would go and knock on people's doors and say, you know, this data driven decision making has been taking place in other industries for years now. And, you know, sports and finance and so on and so forth. It was not a case in pharma. So we had to knock on a lot of doors and ultimately a major, major pharmaceutical company trust us because in the hardening, they were building these external innovation functions where it's a function where they look for new drugs from smaller companies from biotech and by default, they were building something that was forward-looking and they wanted to be impaired in Novot elements were the perfect maths for them and the two core technology and they help us actually expand on it. And that's the story of Intel's AI in the early days, not just as the first customer but the first several customers who learned a lot. We listened, we embedded feedback into our product and thus have created what I think is an industry-dink solution now. But synthesized, you know, it took some time and took a lot of faith on our, and ultimately also on our customers. That's a conservative industry for all good trees. - This show is brought to you by Frontlines Media, podcast production studio that helps B2B founders launch, manage, and grow their own podcast. Now, if you're a founder, you may be thinking, I don't have time to host a podcast. I've got a company to build. Well, that's exactly what we built our service to do. You show up and host and we handle literally everything else. To set up a call to discuss launching your own podcast, visit frontlines.io/podcast. Now back to today's episode. - Yeah, it's one of those industries where I hope it's conservative. I think there's some industries where you think it's conservative and slow, but I think in this industry, you want it to be conservative. - Exactly. It's highly regulated for all the right trees. And yeah, people are highly sophisticated and very careful on how they do things for all the good trees. - How did the conversation around AI change for you post chat GPT? So chat GPT really came out into the world in what November, 2022? What were conversations like before that? And then what were conversations like after? And the reason that I ask is a lot of AI founders that I've had on or AI builders that I've had on, they say that was a critical moment that really did change things. Was that something similar for you? - No problem, the watershed moment in our space in pharma was COVID when it comes to, you know, appending the paradigm of how people do drug development and drug discovery. It's a space where on average it takes about 10 years for a drug that is in clinical trials, eventually to make it to the patients, to make it to market. And during COVID, it was shown that, whoa, it can be done much faster. You can accelerate many parts of the process. So that's where the whole industry moved actually its attention to AI and digital and started investing in the space. That's also where fans and investors started spending money on the space. Now with chat GPT, what happened is that it toolboxards all these. It kind of accelerated something that already started being in motion during COVID. And such a bit is not necessarily always a positive impact, right? In the sense that there's a lot of promise, there's a lot also of noise. So there are pharmaceutical companies actually are kind of setting down or setting out such GPT from their organizations. Because they're like, again, we cannot trust it yet. So don't use it. Literally they have guidelines. There are people not use GPT. And sometimes this flows also into the perception of other AI solutions. So always get the question, OK, why should we trust you? Yes, AI is promise. Yes, we see the potential, but is it mature? And maybe that's for another question later on. But that's I think how we stand out also in the space. And from your perspective, how would you summarize kind of the sentiment around AI right now? Is everyone you convinced that it's going to be big in the future? It's just not ready yet. But what's the general perception that they do have in the market? I was saying with someone who stays in the set, there will be another dot com. It may be very exaggerated, right? My sentiment is the world is changing. There will be more and more AI embedded in the workflows of pharmaceutical companies in our industry and more broadly across industries. As with any prevailing new technology, you know, it will go through its waves and has been going through its waves for the last, I guess, 25, 30 years. AI is not a new thing. It has experienced its waves. I think, yeah, it will, at some point, will mature. The challenge is for, to get the right use cases and convince people for each one of those use cases, the technology is mature enough. Don't see it as a magic wand. And definitely, there are use cases with some maturity and there are many others with much less maturity. - You know, another thing that AI founders have told me is that now in the last year or so, the last two years, there's just a lot more competition. There's a lot more people who are trying to bring AI solutions to market. What are you doing to rise above all that noise that's out there and really capture the attention of the market? - You know, when we start, we're the first AI company in our space and then we've seen more talking about it. And usually there were larger organizations who overlaid AI or, in some case, advanced analytics layer into their offerings. Over time, what I have observed is that, again and again, people who use both or multiple solutions, you know, the turn teller, okay, you're not really competitors because you do actual AI, you go deep where some of the cases, it's a touch for AI. So I think people, there's a lot of branding and marketing about AI. And people start getting more and more savvy on, you know, what is actually AI that goes deep and what is not and what is high quality AI and what is not. In our case, I guess most industries, high quality AI is the one that relies on high quality data, which is not a given, that's usually the toughest challenge to do some decent AI. And in our case, again, in our industry, experimental data goes a long way. People appreciate it, they dislike black boxes. These are highly sophisticated users who want to understand and actually embed AI into their own pattern recognition into their own decision making. So yes, there's some more abundance of AI and people have to cut through the noise and that applies to customers, that applies to investors as well. Ultimately, it increases the burden of proving that you are the better solution, but that makes you stronger and has made us, for instance, stronger. We have definitely more nuance, the more mature messaging now in the market. Another implication of what you're saying is that a lot of money flow flowing to the space. And at some point likely there will be some consolidation. So there are companies out there who cannot necessarily continue as standalone companies because they haven't necessarily found the appropriate thought to market motion and don't generate enough shipping. I think that'll be another byproduct of all this AI momentum. - What about the market category that you're in? Is it a drug development platform, a clinical development platform? How do you think about your market category? - Now let me discuss a bit what we do and why. So we're in drug development. I mean, drug development on average takes about 10 years for any new therapy to draw to patients. So go through all the clinical trials that are required and it costs a lot of money in the hardest of millions if not billions. And what people often don't realize is that most therapies don't make the market. The clinical trials fail. Like 8, 5, 9% of the drugs or the programs fail. Now there is a lot of risk in the space. That's the elephant in the room. And the way the industry has been historically but this only approach in that risk is quite suboptimal which is shocking because it's an industry where science is very, very sophisticated and people are very, very sophisticated. So you have these startups where science is great, season making sciences, suboptimal is not evolved now. So how people approach trees? So this only they look at benchmarks. Breast cancer, face to clinical development, 15%. That's our baseline. They have calls with experts. The pros call the expert in station and they call late the opinions. And if you're a large pharma, you have fantastic internal data. You have trade data science things to stage. It's a limited view. It's a timely vision. It tells us I was the first company actually to apply AI, my semi-learning into better assessing the probability of success for new therapies. And again, the idea here is there's this elephant in the room and someone has to go after that. The risk is at the core of track development. If you can understand it better, then you can actually mitigate it better. And that means you can bring therapies faster market. You can make better decisions on accelerating as well as what is accelerating or things and spending your funds more mindfully. And there are cases like they're fantastic, life saving therapies in the market. At some point we're safe. And people often don't realize this, but some of the major blog passage talks at some point someone had deprioritized them. We show people make better decisions how to qualify a track, how to understand the risk associated with the track development and how to make better decisions. We're the first company who actually took a bit, AI and data-driven approach could work in this space. - This show is brought to you by the Global Talent Co, a marketing leader's best friend in these times of budget cuts and efficient growth. We help marketing leaders find, hire, vet and manage amazing marketing talent for 50 to 70% less than their US and European counterparts. To book a free consultation, visit globaltalent.co. - From a go-to-market perspective, what's the most important lesson that you've learned? - Listen to your customer, again and again. When we start, we are submitting then and we hope that AI would apply. And that's a bit of art, you have better a lot of information and then you create something. And these were the very creative days. And we came up with an approach that is not necessarily the one that was used in the space. It actually was very different and much more effective. But a great solution is not enough. Ultimately, any company wants to have input, right? You won't have customers, you won't have users, you won't have to move the middle in your space. And obviously that's only possible if people use you. You know, one of the key learnings has been again again. You know, you build something great, that's the core. Then you need to find how to embed in the workflow of users, how to build the right functionalities around this, how to, you know, move yourself a bit from being a purist, if you will, to what the users exactly need, how they work nowadays. And that's how you ensure a higher adoption. And that's what we have done, until this AI, listening to our partners, our users, our customers, learning from them and evolving our technology accordingly. - What about fundraising? As I mentioned in the intro, 15 and a half million to date, what's that journey been like for you? And most importantly, what have you learned? - A lot of learnings, sometimes I joke, fans is our track in a good way, some of you need, survive, especially in the early days, especially if you build technology as we do, we have a patent on our technology. That's pretty unique for an AI component in our space. It's a part of the technology. For that, you need investment. It's built the right data in investment. So I'm very grateful we had the opportunity to pay this money. At the same time, I call it a drug because you can also become dependent on this. You can get into a cycle where you always need more money, not only to grow and build more fantastic functionalities and add more BBP all to your team so that you take your product more customers, but sometimes also to stay afloat. Right? And then let's face it, most of the companies that go into this VC funding cycle, eventually, some extent, they become dependent. You learn to operate an environment where there always tends to be more money than you need. And then, you know, you just want to start getting pressed and you go raise more money. I think that a lot about the potential of these days is getting the cash flow positive state while we retain high growth. And, yeah, from my point of view, that's a very nice and maybe, yeah, maybe more shelf environment property. Most likely we'll raise money again in the future when we need the money. Right now, some extent aren't these funded by our customers. The commercial got market, you know, most of them and all of these is still maturing or keep finding things and learning. I don't feel like if we had a lot more money throwing than the commercial engine would necessarily get better results. Sometimes it's the opposite. If you haven't fully tracked out some things and start spending money in, you know, the wrong process, the wrong direction. Yeah, I guess we'll synthesize this. Money, VC or external money is required for most companies that won't be paying technology. It's fantastic that this money is available. You have to be very careful with your partners in the sense that it's not just funds, those people bring in. Actually, it's a marriage. Our investors are on our board and it's great that we have those people on our board and we're aligned on the 70s and direction. But again, that's not a given. You bring a business partner into the company or fundraising. Also, as with any partners in life, be personal or professional, you have to know a lot of those. You have to look at many potential companies and understand their goals, understand their values and make sure there's some alignment. Again, it's not just raising money. The successful relationships are based on common value system and on some common direction. That's important. What else? It's hard, takes time. Ideally, you have people in the company who can support you at least with some of those aspects. Takes preparation. Similarly, as you go sell the product, the platform to customers, you sell the company to some investors. Yeah, that's my main learnings, but don't be overly dependent. People spend a lot of time and energy on this and be selective. The partners that you want in the company on the board. Super useful and super actionable advice there and a lot of founders are fundraising now and finding it to be very challenging. So that's definitely helpful insights. Now, final question since we're over on time and I know it's late there in Greece. I don't want to keep you too late on a Friday. Let's end with a question here on the vision. Can you paint a picture for us of the high-level vision for the next three to five years? What is the company going to look like and what's your impact going to be? That's a great question. You know, I'll start with the impact on the space rather than the company. 'Cause in major reason why we start the company is to ultimately, you know, have some impact and help try better outcomes for patients worldwide. So my vision is strike development landscape gets more effective, more productive. It has been on historical low in terms to IRR, on R&D and it's historical low and it's not sustainable. So I do hope that with our technology, we can help bring better therapies, patients faster, help increase the productivity in the space. I discussed earlier and prioritize some of the trade drugs that are being sold right now and come back. So to get there, the company, the way I see benefit a lot by being recognized as the cold starter in our space. Again, we have been pioneering in space. We are having the first move, it's not easy. In many ways, it comes with some challenges, but I think increasing will get recognized as, you know, the compass out there. When people think risk, they start thinking more and more of Intel Gencia. I always see increasing our user, actually food stream from large pharmaceuticals, meat cup and smaller pharma, as well as the financial markets. So people on the sell side and the buy side. People who write reports like do the research as well as people allocate beds in the space. In 35 years, I would love to be broadly recognized as game changer as this reference point. The goals are in the space. I would love to have full therapeutic recoveries in what we do. I will start the scenario, we'll start with oncology, cancer therapies and we have been expanding in knowledge inflammation, neurology and social force. You know, in the time horizon, you offered a lot to have full coverage and a lot to expand also in Asia. We're increasingly there's very interesting activity, especially in China, but also Japan and South Korea traditionally. Keep learning from our customers. Keep building adults and functionalities and normally have an impact in our space. Now about 100 people strong, company interdisciplinary probably will keep throwing and probably that means good returns also for our shareholders, investors, management team. But ultimately, you don't sweat over building a company primarily for this. Now it's often a common place saying, you know, it's a vision driven company, an impact driven company. There's a lot of truth behind that. Building a company is a soldering experience in many ways. After you dedicate a lot, you have to sacrifice a lot. There is a ways to make living. What I see in our companies, you know, most of us have gone through either directly or entirely some healthcare like event in our lives. I have, and many people are looking out, but we are fighting to wait for better outcomes for patients out there. And that would be my vision in three to five years, you know. - Amazing. I'd love the vision and I've really loved this conversation. I've definitely learned a lot. I know the audience is going to learn a lot from you as well. Before we wrap up here, if there's any founders that are listening in that want to follow along with their journey, where should they go? - Connect on LinkedIn. Visit our website. (speaks in foreign language) I always love connecting with like-minded people, sharing experiences, learning from others. - Amazing. Well, thank you again for taking the time. - Great. Thank you for hosting me. (speaks in foreign language) And congrats on the baby. - Thank you. - Thank you. - Bye. (upbeat music) - This episode of Category Visionaries is brought to you by Frontlines Media, Silicon Valley's leading podcast production studio. If you're a B2B founder looking for help launching and growing your own podcast, visit frontlines.io/podcast. And for the latest episode, search for Category Visionaries on your podcast platform of choice. Thanks for listening and we'll catch you on the next episode. (upbeat music) (upbeat music) (upbeat music) (upbeat music)
