[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 Daniela Grazi, CEO of AxialN, a AI platform that's raised 5.5 million in funding. Daniela, how's it going? >> Good, good. Thanks, Brett, for having me. >> Yeah, no problem at all. Let's go and just kick off on the quick summary of who you are and a bit more about your background. >> Yeah, sure. Well, I'm something engineering but training. I've got stuck in the love of AI and finance from pretty early age. I started programming at when I was eight, and one of my first programs was actually a simulation of a stock market, so that was probably scary seeing that a year old. But anyway, led me to then studies, software engineering, but then while I was university, I found my first company to commercialize one of the software that me and one of my friends developed kind of a standard story. Finished university, then dedicating myself to that company with the long-term idea to invest a lot in research and development at the crossing between AI and financial markets, which has been my two passions. And we did just that, that led us to then start AxialN in 2016, when we kind of saw a big opportunity coming in, which was the destructive growth of deep learning and machine learning, advanced machine learning techniques at the time, which were mainly used for computer vision and kind of problems. What we saw was that they could have tremendous impact, also in financial markets, of course, and especially in trying to predict or make sense of financial markets. But you couldn't simply take what you use in computer vision and use that. It's a way more complex problem. So we saw the opportunity to build a new kind of foundational technology specialized and designed for applying, employing deep learning and advanced machine learning techniques, specifically for financial time series, particular financial markets, and focus on getting the most of it without falling into the traditional traps of overfitting or data leakage, the usual stuff that happens when you try to use machine learning complex stuff. Now we are a 30 people company, we're basically Moderna, which is Northern Italy. It's mainly known for brands, such as Ferrari, Maserati, the ceramic industry, and the food, of course. So we are trying every day to make it known for AI. But actually, this is even not that far from reality in the sense that we are lucky enough for this area to be incredibly engineering-focused, due to the long-term presence of this type of high-quality brands, in the automotive and ceramics industry. So that really pushed the whole environment to be incredibly ahead in terms of academia and research in software engineering, which led to artificial intelligence that came back from the AI winter in the last 10 years. So now, Moderna is actually one of the central vaccines for AI in Europe. Now, AI is obviously everywhere today. What was it? November 2022 in ChetchBT really came out. The world. Now it seems like a big deal. It's here to stay. It's not going anywhere. Was it so obvious to the market in 2016? Take us back eight years ago. What were those conversations like when you were talking about AI with customers, with investors, and just other folks back then? Yeah, well, the first question we always got was very simple. What is AI? And what AI means in your name? Are you calling yourself something AI, right? So it was completely a different world back at the time. And we were just coming out from the so-called AI winter, where not many of us remember that, but the AI was hyped also probably 30 years ago in the 80s and 90s. He had some hype cycles as well in the past, when unfortunately for that time, the computational power and the techniques weren't up to the task to maintain the promises and the imagination of many technologists of the time. So all AI concepts fell back into a sort of winter for several years. In 2014, 2015 new techniques came up. And in that moment, they merged and combined with the incredibly increased computational power that was appearing, which is, funnily enough, it actually was appearing mainly driven by graphics and video games, needs in terms of graphic computation, that also combined with increased amount of data, generally available to companies, and made suddenly all this possible. So suddenly it was really possible to extract a lot of value using machine learning techniques from large amounts of data. So when we went out at that time, it was really early, and I remember going into some financial companies where they were saying, yeah, well, we know AI, we use machine learning, we use neural networks, and we were asking them, yeah, but okay, neural networks are from the 70s. Which kind of neural networks are you using? And they were telling us, yeah, well, two neurons, four neurons, and it was a shock for me because deep learning, machine learning, we were talking about hundreds of neurons, several layers, so incredibly more complex systems. And so we had to recover the destruction and disillusionment that the industry had from previous hypes in AI. And that was just the first obstacle. And actually, I think that things really changed, just as you've mentioned in November 22, when touchy TV appeared, yeah, that really changed people's mind. And what did that feel like for you in November 2022? Did you realize that at the time, in the week after it was released, did you say, okay, wow, if things are about to change, this is about to become big, and our business is maybe going to get a little bit easier? Like, was it obvious to you right away? No, it wasn't obvious because at the time, it wasn't like it is big, even trajectory 3.5 was good, but it wasn't to the level of the new version, which is really another level. So it doesn't really hit the point where I would have had a disruptive impact in the perception of people. That, for me, became apparent later, which I think was made last year, since I ended up, when other specific application strategy in the form of agents starts to come up, because that really demonstrates to me the point where that reach in terms of, let's say, emulating human reasoning, which is, in my mind, also the threshold that it has to surpass for people to get really engaged in it, and to start even treating it differently, which means, at least for our business, then retrospect, also the fact that suddenly AI started becoming something that is not simply found in movies, it's not something that you hear about, it's something that you can touch, you can talk to it, that really changed the dynamics of understanding of what an AI is. And that we saw in the company last but in the following months, so I would say in the last 12 months, we saw really a change in tone, where before we found skepticism, we now found different emotions, such as even curiosity, but also, well, fear sometimes, in terms of what can this lead to. But all these kind of new emotions that we're finding in the market, when we talked about AI, are actually way more conducting to being open to innovation. When it comes to building trust with customers, how did you build trust? If you're selling into financial services, to asset managers, to hedge funds, I have to imagine that this is high stakes. Something goes wrong, the technology gets something wrong, that can cost them a lot of money. What do you do to hold that trust that they're okay to take that risk with the startup? Well, when I started with that scenario, a very successful ex-starter founder, who became a millionaire, told me, "Yeah, you are getting into a 10-year business. This will not be a short ride." And he was right, I didn't understand it at the time, but he was right exactly because of the challenges that you were mentioning. In the financial sector, the stakes are high, and in large institutions, sometimes it's better for the people working in it, not to make mistakes rather than go beyond expectations in terms of performance. So they are generally very, when they have to adopt, or they evaluate adopting new technologies and innovation. So in our case, this meant hitting on two main elements. One is technological, scientific, rigorousness, because obviously you need to be really solid on what you do from a technological standpoint. The whole process that you employ, the crunch you build has to be real. You cannot really sell smoke, okay? Because, yeah, if you sell smoke and you ride the hype, then you may have short-term success, but then if your reputation gets ahead, you're done. So this is something that we realized from the beginning, and you would not be even in our nature to fake into it, and you make it, right? But we had to make it before really be sure and confident enough to go out. And that's what, and the second is start to build a track record as soon as possible. So even starting small, collaborating a lot with the customers so that they get comfortable with your expertise, with your skills, with your professionalism, and work with the customers who create the first application of the technology in the form of solution, which then becomes a product, and that has been our way, and it has worked. 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. When it comes to marketing this product, what are some of the approaches you've taken to marketing, and what's the general marketing philosophy? So our sector is hitting things that complex sale with a long cycle, and so you don't have a quick feedback in terms of your marketing activities, because it takes quite some time to acquire the lead and then to move it through the pipeline, etc. So it's a slow process. It is important to, in my opinion, and this is what we did to work on two areas in a very balanced way, on one side, the positioning of the company, which in our case, has always been tied to strong professionalism and scientific rigorousness. So the customers need to feel that you are sounding what you say about an advanced technology that they may not know yet or even be scared of. So positioning in a certain way to ensure that also the positioning matches how you present stuff, then directly to clients as a solid and robust potential provider. And then the lead generation, it comes in several ways. In our sector, it is important. You cannot really get a lot of traction if you're no one and you are not known in the business. And this is obviously a problem for our startups. So you need to be in touch and have already connected and important players of the field in your team to generate the connections and networking, then lead you into the circle, let's say, in our case, that meant participating to, well, at the very beginning, an acceleration program run by ING in Amsterdam, which obviously got, in the end, ING supporting us from a fundraising perspective and then like vouching for us somehow in terms of bringing us into the financial world, then the same we did with Unicredit, which came on board as a second-large investor in our company. So that really helps. And apart from shareholders and investors, advisors really play a role. I think it is very important for a startup to get sort of sponsorship, let's say, from relevant person in the industry they're selling to. And the advisor type of collaboration is often very useful in that respect. In our case, later on, and we have as advisors, people like Gary Chopropka, who's president of the World Quant, and it's been Goldman Sachs, co-head of Quant. So big career or a mass photos out here in Italy, who has been president and CEO of several very large and very important asset management companies. So these kind of people really help you in getting the pedigree and somehow simplifying your marketing effort. I'm sure in the early days, there wasn't a lot of competition talking about AI. But I'd have to imagine there's probably a little bit more noise now around AI. I would guess that maybe some competitors have popped up. I'm sure their technologies nowhere near as good as yours. But I would imagine probably some competition. How are you thinking about competition and how are you positioned in the market? So yes, actually to say that the competition on the general AI level starts to come up quite early on in our journey. We have differentiated profile from the beginning, focusing strictly on not being a consultancy company, doing consultancy work, even if we develop a proprietary core technology. Trying to focus on a specific segment of the market, starting from investment management, and also specifically in one part of the investment process, which is, in our case, the addition of alpha, so outperformance to the investment strategies. And differentiating ourselves also, because given that focus, we accrued very specific knowledge and expertise in that segment, which is very helpful, not just in the creation of the value and the product, but also in terms of positioning, because we now, when we find competitors or other companies, like pitching, maybe the same prospect, they often are not as focused as us, not as technology and value focused as us, and not scientifically sound and with a long track record as us. So being focused from the beginning of the company, right, on a very specific problem, a very specific part of the value chain of the clients, and getting clients early on, creating an interaction and track record, that is what differentiates us today. And now, I always say that actually today, the biggest problem from us in terms of competition are not other companies, but are not other companies that are serious player in Syria, but more the, let's say the smoke, again, AI smoke that is out there, because now every company says they are doing some kind of AI, so that really makes the message more difficult to come out clear, because you sometimes are greeted with even a little slice of skepticism that comes from hearing everybody saying they're accusing AI, so you have to differentiate them yourself in the depth of your knowledge and technology, but that's the hardest bit, I guess, just, I mean, finding yourself with people that have accrued a small skepticism, not in AI, but in providers of AI solutions, because they are hearing everybody saying they're doing AI. What about fundraising lessons? What did you learn about fundraising throughout this journey? Fundraising, well, first, it is true that it's easier to get fundraising, to get funds and investments in certain part of the world, rather than than others. It's true that being Europe is way more difficult than being in the US, it's true that being in Italy is particularly difficult, so it is important to think about it, also in terms of incorporation and the company structure, because some company structure are easier to invest in rather than other, especially in southern Europe, so that's the first lesson, be very mindful in how you set up your company, where do you incorporate it, etc. That is important, because that really makes a difference in how easy you will get money afterwards, then it is important to, I know it's something that almost Patricia, but it's important for startups, especially at a very early stage, not to judge potential investors just by the money that they are providing to the company, because especially the first ones are the ones that can make or break, not just in the short term, but especially in the medium alone term, if you get investors at the beginning that get inevitably a larger share, because they are investors, but you got them just because they're to provide the biggest back for your, or their back, but then they are not providing you with any additional value, any additional networking, reputation, or on the contrary, they get to be very intrusive, etc. Then you're really setting self up for a difficult journey, so really do your due diligence on the investors, not just from the foremost in point, but also in terms of their reputation, their views, and how they will provide additional value to your company, how they want to be involved, this is everything, all this is very, very important to have it a few years from the beginning. Final question for you, let's zoom out three to five years into the future. What's the big picture vision look like? Well, for me, in the end, what we do is providing an AI component to the investment process that adds on alpha, so performance to the strategies of our clients, to the investment strategies of our clients. When we started, there was practically no one using AI, there were obviously quantitative approaches to investment management. For me, in five years, AI would be an essential and core component of any investment strategy, not just the quant-based ones, but also discretionary ones, simply because if you won't have it, you will start from the back, from the beginning. It would not just be a matter of having AI to get an AI, it would be a matter of having AI to stay in the game. This will happen very early, but we will see the full-blown effects of this in five years. It's the same as in other areas, other industries, like we are seeing with ChargePT, now for some areas, it's like big edge to being able to implement correctly at an answer. You can get big cost savings, increased user interactions, and satisfaction, but yet also for that area in five years, that would be the base from where you start. It would just be an edge that someone has. Amazing, I love it. What we are up on time here, so we're going to have to wrap. Before we do, if there's any founders that are listening in that want to follow on with your journey, where should they go? Well, they should go to our website, which is axion.ai, A-X-Y-O-N-D-A-I, and look for us on LinkedIn. Amazing. Well, thank you so much for taking the time to chat with us today, and especially given that it's now 7 p.m. in Australia. We appreciate it, and I hope you have a great weekend. Great, thanks for having me, and have a great weekend, you too. 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) [MUSIC PLAYING]
