Good morning, everybody. Welcome to the 51st JPMorgan TMT conference. I'm Mark Murphy from JPMorgan. Just a quick reminder that to those online that you can submit questions through the webcast as well. With me this morning, very happy to have Founder, Chairman, and CEO of Altair Engineering, James Scapa. James, great to have you with us. Good morning. James, I guess for those who are less familiar with the Altair story, would you be able to quickly provide a short overview of the business? Of course. Thanks to those of you who made it at 8:00 A.M in the morning here. Sure. I founded the company 38 years ago in 1985 in Detroit, which is not a usual place to found a business. We were initially a consulting company, primarily focused on numerical methods using computers to solve structural engineering problems. Very, very quickly began developing our own software, primarily for building models and visualizing results. We had a very significant success with that product. It was called HyperMesh. From there, we went international with the product and began investing in further products, primarily in mechanical simulation, so fluid mechanics, motion, system modeling, those sorts of things. Through the years, we have added a lot of electronics simulation. We've also, in the early 2000s, began investing in high-performance computing, workload and workflow management, so determining the scheduling primarily of when and where jobs are running in HPC hardware. Over the last, 12 or so years, we have invested in data analytics and data science. Our, our customer base, as you can imagine, is, it's a very, very large base of very large companies, very global. We're about a third, a third, a third, across the three regions. We have customers across manufacturing and auto, aero, ship design, technology, consumer electronics. We also have a significant customer base in pharmaceuticals and energy, and banking as well, especially banking because of the data analytics and to some extent, the HPC work. Nice. That's our business. Thanks, James. Very comprehensive. Yeah. I think you touched on the data analytics point there and, you know, analytics, AI, all of that sort of stuff that's been, you know, a key topic, and it's especially topical in 2023. You've got AI and analytics offerings, made the RapidMiner acquisition recently, and you also embed AI throughout the product suite. What's kind of your key takeaways about AI, how it's gonna be used, and how Altair is poised to drive usage as well? Yeah. The AI term is very hot right now, obviously with OpenAI and the battles between Google and Microsoft and such. You know, we have a very broad array of technology and data analytics from data preparation, data science, data visualization, real-time visualization. We've been investing in that direction actually really long time. A lot of algorithms in our software for many, many years that are sort of hidden under the covers or even available through some of our products like HyperStudy. You'll find AI and machine learning kind of embedded in many of the products. Where I see things kind of going when you think about the whole ChatGPT- Mm-hmm ... technology, we're really exploring and investing pretty significantly on the user experience side. Applying this technology to, sort of augment the historical, you know, mouse point and click and select menus and all of that to have different ways, if you will, of interacting with the problem that you're trying to solve. We're starting to see some really exciting results actually in our lab, if you will. We think we're gonna release some things very soon. You know, I think that this technology is going to affect user experience very, very significantly. It's also going to affect design itself- Mm-hmm ... because I think you will, you will be able to sit and say, "Design me something like this or build me something like that," and then iterate on that sort of with the mouse, but also in, you know, in different ways. Mm-hmm. You're gonna see more and more automation around, you know, making decisions for, you know, items that are in service. The operational decisions that you see. Of course, you have, you know, automated driving, but you're gonna have across everything this sort of control. Yeah. No, that's tremendous. Mm-hmm. I guess you know, there's been a long history of innovation, and you've got other technologies which are super exciting as well. I think you've been having good traction with, SimSolid, for instance. Could you maybe talk a little bit about that? About SimSolid? Sure. SimSolid is one of many, many products in our portfolio. Probably at this point, still the fastest growing product in the portfolio. It's a technology that initially really focuses on the structural engineering space. In that world, the way that you do structural engineering is you take a CAD model, you build a mathematical model, a finite element model, typically, and then you solve that. It's a big system of matrices, and you solve the matrix. With SimSolid, you don't need that intermediate step. You literally take your CAD model in and there's a huge amount of intelligence in the software, and it's very robust, and you basically are saying, "Here are my loads, here are my constraints, solve it." The solve times are astounding. It's magical, actually. It's, it's not at a level where it's going to displace all of our traditional technologies, but particularly in the upfront design phases where you have designers and even engineers who, you know, need to solve problems, it is just a game-changing technology. We are seeing, you know, this huge take up. We had a win at Airbus, which was a very critical win. We have tons of automotive and aerospace companies using it, but this was a very critical success that we had recently and sort of helps us to build on top of everything else. Yeah. Very exciting. We are, you know, investing in SimSolid to add lots of additional, solver types. Mm-hmm ... including some areas of electronics and all of that. There's a lot coming, I think, in that space. Super impressive. Mm-hmm. I guess, you know, running along with the theme of making stuff easier, for end users and everything like that, what do you think about, like, democratization of technology and convergence of technology and, you know, that's clearly something that Altair has been driving at. What are your key takeaways around those themes? Yeah. We're pouring investment. SimSolid is in the same vein, of course. Yeah. We are pouring investment into user experiences for sure and tools and technologies for conceptual design. Then democratization, even in the, you know, data science world. If you look at the RapidMiner product, it really spans across different levels of users. It's a very user-friendly product. It's a beautiful piece of technology, but someone who's not a Python Programmer can build a model pretty easily using RapidMiner, but also someone who is a very sophisticated Python Programmer can interact in that world in a very, very nice way. You know, we're trying to address across all those users, make these technologies accessible so that you can do fun and exciting things. Yeah. Speaking of the RapidMiner acquisition, that's one that's perhaps your most recent sizable acquisition. How has that been going? That kind of puts a little bit of a capstone on the analytics and platform that you have. Sure. Yeah ... is, you know. Fair ... questionable. Yeah ... but in terms of what Altair does. Yes ... which is primarily tech ends. It was kind of the capstone piece. It's true. We've made actually four, but one was very tiny and years back, but three significant acquisitions in the data analytics space. We came to that market investing on our own. Ultimately acquiring. We acquired Datawatch, which brought us Monarch and the Angoss technology for data science and decision tree technology, and Panopticon, which is a really fantastic product for data visualization. We acquired a company out of the U.K. with about 100 people, 600 customers. Very, very serious company, which had developed a SAS language compiler and a whole execution environment. If you know the market of data analytics, and this is really why I pursued this, that market has a lot of sort of ants running around and one mountain, which is SAS, which is, you know, whatever, $3.5 billion in revenue. Every major corporation has thousands of SAS language codes in their environments typically. It's a very powerful language and they had a sort of monopoly on the space, and they had been very litigious- Mm ...suing this company in the U.K. We took a chance and acquired them. Every litigation up until that moment had gone against SAS. In fact, since just over that last year, we've had two victories now, and I think it's quite closed at this point. Particularly three weeks ago, we had the appeals court in the Eastern District of Texas, side in our favor that. Yeah ... you know, the SAS language is not a proprietary language. Now we sit with a great piece of technology that's very unique. That, you know, customers can leverage to sort of support their legacy technology. We can help them convert part of it to Python. They can move to new and very modern architectures. We run this technology across mainframes all the way to, you know, smaller infrastructure and in the cloud as well. Then finally, we looked at RapidMiner primarily for two reasons. It gave us a real depth in data science. I mean, this is really arguably the first player in data science 20 years ago. Ingo, the founder, is just a very well-known person. They had over 1 million downloads of the software across the world through the years. They had just built a beautiful cloud platform, and we were working on our own, but I think it would've taken us two or three years to get to where they were. That product really brought us kinda the elements that we needed, plus a lot more expertise to bring everything together under this platform, and that's what we've done. It's really proven to be really a very, very good acquisition for us. Every customer is very engaged. People are impressed. When we benchmark, we generally win. It's quite a great acquisition. Great. Awesome to hear. I think you touched earlier a little on end markets, and, you know, historically, previously, it was quite auto and aero heavy, but particularly with the data analytics platform and stuff like that, you've branched out. I think maybe technology, financial services are certainly of increasing importance. Maybe touch a little bit on how you see the end markets and how that mix shift has happened over time. Yeah, sure. automotive is still the largest vertical. It's in the 30%. banking, financial services, aerospace, and technology sort of follow, kind of equal in terms of their dimension. They are, you know, the meat of our business. We have a very broad-based business, of course. You know, they're all growing pretty well. Even automotive continues to grow. People are often surprised by that. As we enter, you know, data analytics, we have a lot of customers that are interested in. You know, there's a lot of trust in Altair. Mm-hmm. As they wanna apply new technologies, there's a relationship that's built over many years, and our business model is extremely strong as well. Yeah. Not sure I'm answering your question there, so sorry about that. No, no. That's all great. You mentioned the business model. Mm-hmm. Obviously, that's the units-based. Mm-hmm. Business model that you guys have built up. Maybe touch very briefly on the units model for those who are not as familiar with it. Sure. There's a history to the business model. I got a lot of patents around it. There's a couple of companies who have embraced it. Essentially, we had one product with great success that went back. That was HyperMesh that I mentioned earlier. We had created a second very exciting product called OptiStruct, which was a commercialization of a technology from the University of Michigan for topology optimization, creating these very organic shapes for structures. It's a beautiful technology that's used throughout industry now. We had a third product as well that was quite nice. Selling those additional products was very arduous. Go into an account that's already using HyperMesh, you have to go through a very arduous sales cycle to look at a very innovative product like OptiStruct. I created this concept where we converted all the customers to what we called at the time HyperWorks Units, but today are called Altair Units. Every product in our portfolio, we only had three at the time, drew units from the pool, and while you were using the product, and then put the units back in the pool when you were done running. Suddenly, I made OptiStruct, and at that time MotionView, available to all my customers. That created a very quick take-up of OptiStruct, which was a fantastic technology. Through the years, we have grown out that portfolio. We have, you know, almost 100 products of our own which are available to customers, including all the data science products. I also added this concept, patents again, where you could bring in third-party software, and that third-party software could be licensed with our units model and available to those customers as well. We have, I don't know, over 200 applications now. Every fatigue analysis software, and I mean, You know, we live in a fractured world of engineering. Suddenly, all those products are available to customers, and it's very sticky. We have, you know, I don't even know what the average is today, but it's probably approaching 20. 20 applications on average per customer that are used. It's a kind of Netflix model. Mm-hmm. -right? Where, you know, you may use this product, but your wife may use those, and, you know, your kids might use something different. Across the enterprise. Now, as we're bringing some really, you know, top-class data analytics and data science tools, we are beginning to see take up even in our traditional customers, and we're moving the data analytics traditional customers, like banking customers, to the units model. As we've grown out the portfolio there, it's beginning to get some traction. Initially, the sales people that came in from sort of this foreign world of data analytics, which was a very different mentality and culture, I'll say, than our world, today they bleed Altair, all those guys. They love the model, and they completely get it, and the customers are getting it now too. It's quite powerful. Yeah. Yeah. Very good. Mm-hmm ... naturally lends itself to a lots of resiliency and ease of expansion. Mm-hmm with customers. I guess maybe to flow on to a little bit on the financial side. You had a nice Q1 above expectations, even with the macro backdrop. Investors are clearly interested in profitability nowadays. How do you think about Number one, the macro environment, and Number two, the balance of investing for innovation and growth? When, when does it make sense for that versus driving the bottom line? Okay. Very different question. Yes. I mean, we are very, very committed to continuing to grow the bottom line. Mm-hmm ... if you will. Mostly we see that for us it's all about execution at this point. Yeah. We're very focused on very strong execution. We feel, you know, we had a commitment to 2023 that we made a few years ago, get to 20%. On a very good path there, and we don't see ourselves stopping. Yeah. We see ourselves continuing to grow that, you know, adjusted EBITDA line, and I don't see anything in our way. Coming to the economy. Yeah. Let me just say that we see the importance of a balance between growth and growth of profitability. Yeah. I could hire another 100 sales guys, and I can grow faster, which you know, you see in certain companies. That's not our, you know, mode of operating. Mm-hmm. We're sort of steady as you go and have always been in a, you know, annual license mode as well. Yeah ... in terms of how we do our sales. Coming to the macro, I mean, this is not the greatest year. Mm-hmm. I've been around 38 years, so I have been through a lot, and I've been in Detroit a lot of those years. I live in the Bay now, but you know, I spent most of the years in Detroit. I've seen a lot of ups and downs. This is not the worst year. Mm-hmm. It's not the greatest year. It's a year which requires execution and basically fighting. I think our business model in many ways, really starts to shine in these kind of moments when customers are looking at cost savings. It surprises me because I tend to be the more pessimistic. It surprises me, our business continues to be relatively solid. All of the renewals are there's just no risk around renewals as far as we see. There's not delays, customers are certainly concerned. I see the automotive industry, which I get asked a lot of questions about. You know, there's still really good demand for cars. Yeah. Their pricing is holding up. They have their challenges. They all seem to know how to manage their own budget, so you see them trimming and cutting costs and all that. That is not really significantly affecting us. Engineering is a relatively small percentage of their investment, and it's so critical to be innovative, especially in a moment where they have a lot of competition. That's just not a big concern. Aerospace and defense is a very strong market right now. Yeah ... because of concerns, you know, geopolitical concerns. You're seeing more investments than ever before. So that's good. For us, the banking market is new for me. Mm-hmm. I think as we're changing business model in there, the units model, we have a lot of opportunity. The RapidMiner product really was kind of transformational for us in terms of positioning. You know, we had all these other important elements. We had success as point solutions in a lot of these accounts, but we're really converting them to more enterprise-level sales, and building relationships. I mean, it's not the greatest economy, but it's not 2008 or 2009, at least not at this moment. Yeah. Anything can happen, yeah, as you well know. Yeah. Yeah. Yeah. What other factors do you kind of keep an eye on from a macro perspective, whether that's FX or... Mm-hmm ... other elements like that, given, you know, global business? FX has certainly been a big factor in the last couple of years for us. That's definitely much more of a Matthew question, my CFO. Sure. But it has been, you know. Not great for us in the last couple of years. It's been good for us in other years as well. I don't see that. I'm not paying a lot of attention there, although perhaps I should pay more. Yeah, what else do you want me to cover there? Sorry. No, no. That's all right. Okay. Maybe you might just pause there for any questions from the audience. Okay, yep, hand up, and we'll have a microphone that will be coming round as well. Can you introduce yourself too? Thank you. Hi, I'm Robert Lutts from Cabot Wealth Management in Sale. Would you share with us, how the automotive industry is using your products, particularly in light of the transition that they're going through, changing to a whole new platform? Is that creating a lot of new business for you? Or what is it doing to your business? Thanks for the question. Historically, Altair's solution set into the automotive industry was not heavily into the powertrain area, quite honestly. We did have one product which was used for modeling powertrains, but it's still used for modeling electronic powertrains actually. For us, there's just been a lot of. You know, I went through a transition of acquiring and building out an electronics portfolio, and so we have very strong tools for electric motor design, battery design, you know, all of the elements that are different in electric. If anything, I think it's expanded our opportunities, 'cause we really were not doing as much in traditional, you know, IC engine design. I think that, you know, that whole transformation in general has been a good thing for us because it's created a kind of very competitive atmosphere. First of all, you have a lot of new players that are all my customers too. The traditional players have been playing some catch up. You know, they've had a tough situation because they have to support their legacy platforms and make this migration and a lot of tough management decisions there. I think today when I look at that market, you know, the new players are starting to feel some strain, the traditional guys are starting to get their act together. It doesn't really change our business that much. You know, If anything, it's been a little bit expanded, I would say. Yeah, I mean, we work with all of them and across their enterprises. What we see more and more is system-level modeling becoming, you know, important, and it's another area we have poured investment in with no one noticing. I have probably 20 years of investing in a solution for math and systems modeling. It's all heavily used for digital twin, and it's really coming to the fore now. Everywhere we look, there are digital twin opportunities that are, that are, you know, opening up, which are really exciting and interesting and they kind of sync together with all the data analytics side of the business as well. Really good moment. Yep. Thank you. Anything else from the audience at this stage? No. All right. I guess, thinking through, you know, data analytics and engineering simulation, all of those being clearly very compute-intensive, functions. You mentioned earlier, you know, Altair clearly has got capabilities around HPC. Have you seen an increase in that area of the business as people get focused on AI analytics and those elements? Or given the strength in simulation, is that something that's been just consistently strong throughout? We are, I guess, arguably the leading player for HPC scheduling and such. The part of the reason I started investing there is I was going to supercomputing conferences, and we were seeing a lot more data science and data analytics playing out there. For us, we've made several acquisitions in that space. We started in the early 2000s. We spun a team of nine people out of NASA Ames. It was zero revenue. Computing was changing from very specialized hardware to commodity clusters, what we see today in these server farms. Where and when all the jobs run, you know, for people in HPC, we used to have something called NQE and NQS that would sort of drive, you know, the jobs running. Mostly in the old days, you would say, "I've got my job to run. It's a crash simulation. It will run on that computer." The computers were all named after Star Wars characters and whatever, and you didn't really know. You know, there's a big queue over here, but this computer is empty, and you would still stupidly put your job over there. Well, with the scheduling software, you really, you know, maximize the utilization. It's very clever technology actually. It's quite deep in terms of how it, you know, fits jobs in and all of that. With data science and data analytics, coming back to your question, sorry. I thought I'd give some background. It is more and more important. When we acquired... I tell this story, when I acquired RapidMiner, the largest customer, which I think is gonna be this amazing lighthouse customer for us, you know, they've implemented something like 2,000 machine learning models, and they wanna scale it out. The big boss there wanted to speak with me. He was worried. Right. Altair, you know, is acquiring this company, you know, let me talk to him. Their big concern is scaling. You know, scaling all this out. The technology's used across all their departments, sales, finance, you know, engineering. There were seven departments he listed for me. They were worried about scaling, and scaling is really actually all about HPC scheduling actually. Within RapidMiner, they have developed their own scheduling technology. I kind of have another scheduling technology. It's very apparent that the depth of our expertise in that space is clear to them now too, you know, really brings a unique power. I talk a lot about convergence, and it's kind of a marketing message, but it's real as well. I mean, there really is a convergence, between all these things, simulation, HPC and data science. Yeah. In fact, last night there was a question. I was in a different dinner and the, I don't know what his title is, the Chief of Strategy for Microsoft was attending. Mm-hmm. He was asked about AI and. Mm-hmm ... you know, "Aren't you running out of data?" I thought that his answer would be. No one asked me, I just stayed quiet. I thought his answer would be. It was a little. He talked a bit about that. To us, we see the data coming not just from a lot of sensors, you know. Mm-hmm Which is a really important element and part of why our real-time data technologies is relevant, not just in banking on trader desks, but in engineering. It's gonna be synthetic data as well. Running simulations, generating synthetic data is gonna be super relevant in that space as well. Yeah. So... I'll just see if there's one more question. No. No more questions. Otherwise, I think we're just about out of time. All right. Thanks, Jim. Well, thank you very much, Mark. Yeah. Appreciate it. Thank you all.