Good morning, everybody. It's a great video that the marketing team put together, especially the first three seconds that you didn't hear the sound for. I'm super sorry, guys. I'm Jim Scapa, and I'm the Founder and CEO of Altair. For those of you who don't know me, I think most of you do, actually. And it's my pleasure to welcome you here to our third Investor Day. We had one shortly after the IPO, I think, in 2018 in Detroit, and then we did a virtual one during the pandemic. That was super complicated and challenging, but I think came off very, very nicely. And then here we are today, and it's nice to be here in sort of the heart of Silicon Valley, because Altair's business continues to evolve more and more towards this convergence of mechanical and electronics. So today, first of all, safe harbor statement. So we're gonna make some forward-looking statements here today, and obviously, some of these things may not come to pass due to risks that we can see and some that we don't even see. So please bear with all of that. So today I've got. By the way, I am the master of ceremonies, as you can see. And today I have with me some of my executive team. I'm gonna give kind of this overarching talk, and talk about where the business is going and how we're investing and where we see the market going. And a little bit just to give a sense of why we're successful, because I think we are very successful. Sam Mahalingam is our supreme CTO. We used to have three CTOs, and Sam now has been elevated to oversee everything and is gonna give a deeper talk into the products and the technology and where we're going with that. Amy Messano, who joined me shortly after the IPO, as the Chief Marketing Officer, is gonna talk about the marketing strategy that we bring. She's done a phenomenal job since coming on board to really transform marketing for Altair. Stephanie Buckner, who oversees the entire field organization, so that's sales and technical support, and also corporate development, is gonna talk about the go-to-market strategy and how we're scaling the business. And then finally, Matt, who I think most of you know, joined me three years ago, and as the CFO, and Matt is gonna talk about the financial performance and, of course, where we see things going over the next few years. So, one other piece of housekeeping I'll say is that we're gonna publish all these slides electronically right after the event. So right at the close of the event, you'll find them online. And let's see, what else? And for Q&A, I'd really appreciate it if you'd leave the Q&A to the end so that we can sort of keep to timing and go forward. So with that, I will begin and just try and give that overview of the business. A little bit of the philosophy for the business for me is really we've come into markets where there were already a lot of established players, typically. And when you do that in technology, you have to be thinking with a mindset of disrupting, and you disrupt with basically technology and innovation, and that is the essence of Altair. So the business today... I'm used to looking here, by the way, 'cause I didn't have these when I looked at it first, and I'm struggling where to look. The business was founded in 1985, so it's 39 years now. And I think it's still very youthful. I like to think I'm still youthful, by the way, and I feel very energetic. But we IPO'd in 2017. Actually, a couple of the bankers that worked with me on the IPO are here today, so thanks for coming, guys. And very successful IPO. We've grown pretty tremendously since then. And then, if you look at the business today, it's probably doubled in size since that time to over $600 million and $129 million of adjusted EBITDA. So the vision for the company really from the very beginning is to apply math, you know, mathematics and algorithms, basically what we call now computational intelligence, to drive innovation, to drive decision making for our customers, to create a more connected, safe, and sustainable future. And the focus today, which has really been evolving, is on really these four areas. So the first is digital enterprises. You know, most companies today are really trying to transform themselves. Everybody talks about this, but fundamentally, what they're doing is trying to collect and aggregate and create smart data across their enterprises and across all the processes in their enterprises. So, you know, all the way through the product life cycle, to organize that data and then have access to that data in intelligent ways, so that they can make decisions, so that they can optimize products, so that they can basically run their businesses very, very efficiently.... The second area, and Altair is very invested in that with a lot of solutions that we'll talk about. The second is AI-powered engineering and business. And we were pretty early, I think, to begin investing in technology for data analytics and data science and looking at how this can be applied. We're trying to apply it really throughout all of our products and throughout all of the solutions that we're trying to bring to customers, both on the engineering side as well as for business. This is just a huge focus for us now. AI and simulation-driven design and optimization. We are extremely early, I would say, with this idea that simulation is not just there to validate designs, but actually, we really were thinking about this idea of running multiple simulations with parametric studies, with optimization technology, with DOEs, to really enhance design, to optimize designs, and of course, now adding the AI technology into that whole mix to really enhance that whole direction. And then going back early on, and it's through a platform we call Inspire, we really are trying to transform, if you will, CAD with the next generation of CAD, with a solution we call Inspire, where that product is core, kind of bringing together simulation, AI, human creativity, of course, in the design process. But the simulations are performance simulations as well as manufacturing simulations, all wrapped with a lot of optimization and AI to almost synthesize designs. And that technology continues to evolve. As you look at it year by year, it's got four different geometry engines that all work in concert. And it's just coming fast and furious with a lot of amazing technology that customers are every year embracing more and more and sort of transforming that whole world of conceptual design. And then finally, the sort of fundamentals of our business is this broad portfolio of simulation for mechanical and electronic systems design. And I think we have the broadest and deepest portfolio in the market, frankly. So the addressable market for Altair... Sorry, let me do this for a minute. The addressable market for Altair, I've tried to portray here in a table where you have basically the solution areas that we play in, which is simulation, HPC scheduling, and workflow design, and data and AI, and then across the different vertical markets that we play in, which is manufacturing, life sciences, energy and semis, and then banking and financial services. And the company started in 1985 in Detroit, in the automotive market, focused on mechanical CAE. Then we've grown, basically moving across, weaving across all the different markets, vertical markets in manufacturing. Then in 2003, we were seeing an evolution in computing that was really happening, where it was moving away from high-performance, specialized computers to commodity clusters. And with that, you needed the ability to schedule jobs running very, very efficiently. So we spun a team of nine people and a technology out of NASA here in Mountain View, California, and we—zero revenue, by the way, at the time, and we began cross-selling that into our manufacturing customers. It grew tremendously. We have probably 80% or 85% market share in manufacturing, but it also took us into, and a lot of people don't understand that, it took us into almost every pharma customer, you know, life science companies like Genentech and those kind of guys, all the energy companies that are doing, you know, oil reservoir simulation and into the semiconductor market, because every company that's doing Synopsys or Cadence, you know, or Mentor types of simulations is needing to schedule those, those jobs are very, very expensive. They need to be scheduled very efficiently, and the workflows need to be scheduled very efficiently. And we bring best-in-class technology for that. And then, of course, surprise, we were able to cross-sell simulation back into those accounts as well. Not in every account and not every product, but quite a few. And it might even surprise you in some of the acquisitions that we made, that we tuck in through the years, kind of fit into these different markets. And it's sort of what's informed our decisions in terms of a lot of these acquisitions. And then beginning in the late, you know, around 2008, 2010, we started to invest in data analytics. Believe it or not, a lot of people don't know that. And we actually made one acquisition in 2008, a small acquisition out of Italy. Made a lot of mistakes, but we began to learn that market because we were beginning to think about this convergence. And then after the IPO, we acquired a company in 2018 called Datawatch, another public company. I got roundly criticized for this, with three amazing assets, one for data preparation, one for data science, and one for real-time data visualization... And, you know, those products basically have thousands of customers, but most of those customers were in banking and financial services. But we've been cross-selling these products with increasing success, by the way, more and more success. As I look at the wins, and we're a very communicative company inside of Altair, I see more and more of our wins that are happening in manufacturing have some aspect of the AI and data technology as part of them. And the same is happening in life science, by the way. Then finally, as you might expect, there's a big play for high-performance computing in the banking and financial services market, and our products are more and more being aligned so that they can compete in that arena as well. Meeting CIOs, I just met with the two CIOs of one of the banks, a couple of weeks ago. We're really beginning to see more and more opportunity with those products in the banking and financial services market. So this kind of lays out where we play and how it's evolved, and I thought it would be useful to do this. The total TAM, which is $40 billion, Matt's going to come up and drill into, you know, how those numbers are arrived at. But I think it's, it's sort of instructive to just see how this, how this TAM sort of builds from my point of view. And today, the customers, you know, we have over 16,000 customers across the world, and they're across all these different vertical markets. You know, when we say automotive, there's probably 2,800 different individual, what we call automotive accounts. Aerospace, huge number of customers, very, very big market, but financial services as well. Matt's going to show you, we don't disclose percentages around different verticals, but he's going to give you a little flavor in his talk of how it-- how that lays out with a pie chart. But technology, very, very big market for us now. Those are really the big four for us. And then energy, civil, heavy equipment, life and earth sciences, we just talked about pharma and life sciences, and material suppliers are really big for us as well. So what differentiates us as a company, I think, are these four things, and I've talked about these, you know, over the years. The first is culture, and everybody talks about culture, or maybe some don't. But the culture is really powerful in Altair. It's a culture sort of built on very broad, honest communications and a very entrepreneurial spirit across the enterprise. Everybody's thinking about how to win and communicating about customers and accounts and technology and competitors. And everyone is just laser-focused on winning and on innovating, and I think that culture is really just key for why we continue to succeed. The second is the business model, which we brought to market, you know, 25 years ago or so. And I think that business model of units is very, very important for how we're able to continue to acquire, integrate so quickly and, and really get our customers to embrace us, and we become so sticky within these accounts. The third is open solutions. You know, if you go back 30 or 40 years ago, and unfortunately, I do, you see that most companies had embraced this idea of open solutions. The idea that, you know, if I have 10 different products in my enterprise, they should all work together, you should be able to pass data between them. A lot of companies and a lot of our competitors have sort of gone away from that, trying to create these walled gardens where they're protecting, you know, their turf. But Altair has really remained true to this original concept of open solutions. We think customers want to use best-in-class tools, and even as we moved into data, you know, we didn't support Tableau, you know, because we have Pano. No, no. If you want to use Tableau after you've done some data science work, go use it. I just need to make my products better than the others, and I need to make my business model attractive enough that you want to use my products instead of theirs. And then finally, I think we really have kind of invented this idea. Everybody's chasing it now in the last year, especially since ChatGPT, of really marrying data science with rocket science. We'll talk a little further about that as well. The culture, I mentioned it before, it's 40 years of really focusing on innovation. We envision the future, we seek original ideas. We're always looking at what's coming out of universities, what's coming out of new small companies. I often get bankers telling me, "Did you hear about this?" Or my investors telling me, "Did you hear about that?" Yes, I heard about all of them. It's very rare that you've told me something that we don't know because we have, you know, over 3,000 eyes and ears that are all tuned to bring in new ideas, new technologies, and we look at it. We're very, very aggressive in evaluating technology. We can develop a lot of things internally, but we also recognize you go faster and we bring the member. You know, there's a lot of acqui-hires that go on with what we do. So you see, we spent almost 35% last year in R&D, which is a lot, and we'll probably start to taper it down, but it's a very, very important part of what we do. And then finally, we're just constantly experimenting, and we're failing sometimes. We'll try a new idea within the business model. Oops, that didn't work quite right. We'll take it back, or we'll make a different change, or we're experimenting with something with AI within one of our solvers. Does that work? Does it not work? And then move on to something else. So the second is the units model, we think, just revolutionizes access to software. It's a kind of Netflix model. We have some customers that are using almost 50 of our products and partner products, but it's mostly ours, usually, in these accounts. And the power of being able to access all the products, you know, you buy one product from us, and suddenly you have access to this broad solution set. It's relatively low friction to expand and to cross-sell. So as I bring data into these accounts, and now the data products, oh, I can run RapidMiner next to HyperMesh? Yes. And guess what? Here's some solutions that we can already show you, because we're starting to have success in all these manufacturing accounts. So we're starting to know, oh, this is what works in automotive. This is what's been working in, in aerospace, and we can repeat these successes. And they're singles, you know? They're not all these home runs. It's a lot of singles and doubles, but we're also starting to see some home runs as well. And one thing about it, it, it delivers high value for customers, but as our pricing power grows, as a customer, it's so sticky when they're using 50 products, right? It's very sticky. And so we're continuing to edge up where we see... And make-- You know, we're tuning the model all the time to increase our revenues, to make sure that we're getting the right-- And we have to focus on that. We have to focus on discounting as well, but it's going in the right direction here. So we're a very acquisitive company, and it's a lot of, you know, founder-led technology companies that we've acquired. Most of the founders are still with me, by the way. I love founders because they're usually best-in-class technologists, and they understand the whole point of building a business and being entrepreneurial, so they're very powerful in our culture. But we've acquired 53 companies through the years. All the way from the very beginning, we were acquiring stuff, and we've spent about... It's not a precise number. It's $644, and the first two we weren't 100% sure, but it's about $650 million in money that we've expended towards these M&A transactions. And you can see it's across a very broad array. More recently, it's more electronics and more data science, but we're still doing some basic things that go back to structures. I just acquired that Genesis technology, which is the leading technology for optimization. So data science and rocket science, Sam is going to come up and drill into the products, but essentially, there's four platforms that we really bring to market. Altair One is our cloud innovation gateway. HyperWorks is for design and simulation, and it's really... These things are all really evolving into platforms, by the way. I know everybody wants to talk about platforms, but this is real. You know, shared, you know, back-end data model, shared APIs in Python, you know, brand-new user experiences. We're able to transfer data between, and models between all these different applications, and people can build their own applications within our environment now, and that is really when you begin to be a platform. Not just customers, not just ourselves, but third-party software applications are now coming to us interested in developing in our solution, and that's when you become a platform, in my opinion. RapidMiner is the solution, the platform for data science and data analytics, and HPC Works is the platform for high-performance computing. So Stephanie's going to show a ton of examples of customers, but I have the fun one. We have been working with America's Cup boats for since 1995, actually, with PACT 95, and it's super fun. I get to go to these races usually, and it's a lot of fun, and they're so competitive. But we're working with American Magic this year, and really, significantly a part of the team, actually. So we've got a video about that, and I think it's kind of fun to see. ... What makes the partnership between Altair and American Magic, I think, at its heart is some really important common values, in particular, innovation and excellence. We are innovating every day in terms of how we design the boat, the technology in the boat, and how we sail the boat. Both organizations are focused at being the best in the world at what they do. American Magic decided to work with Altair for two reasons. One is their best-in-the-world technology in the areas of high-performance computing, advanced analytics, and advanced simulation. In addition to that, we got a very strong message that the entire organization was supporting us. Working with American Magic, it's really exciting because they've embraced us completely. It's a place where you literally have the need to apply the latest and greatest innovation if you want to get that little edge on your competition. That is pushing us to do our best work, and I think pushing them as well to really create a great product. I think what's unique about Altair is their ability to come in and help us solve an incredibly complex problem, and we see them as such a valuable partner in this competition and our development. They bring solutions that are very, very science-based, and developing these flying machines requires that type of thinking. We, at the same time, see the spirit that these guys have to really get better, to really win. I think combining it with what we bring together in terms of technology and culture makes it just a great partnership. Technology has been at the heart of the America's Cup since its inception, and it's been on the cutting edge of many technology improvements. We're using all of the capabilities from Altair to help us get that competitive edge. We really think it's gonna be key for our ability to win the cup and bring it back home to the U.S. It's been really exciting to see American Magic and Altair, and how we've blended the two technologies, and the impressive accuracy that Altair is providing to us is just awesome. It's the oldest trophy in sport, and by many estimations, it's the most difficult to win. Winning the America's Cup and bring it back home to the United States, it would be a tremendous achievement. We're getting the chance to be a part of something sort of bigger than ourselves, and being a part of it is, is what gives me a thrill. So by the way, these, these races, the, the races actually happen, I think, in late September or October. For those of you who don't pay attention to that, I think it's, it's pretty cool, and kind of a, a big deal to watch. We hope these guys do well, but there's a lot of factors. The very first time that we were involved, with PAC 95, we used our topology optimization technology. It was very early days of that kind of technology, and we designed a new keel. It was radical, and they had to hide it whenever they were putting the boat in and out of the water. Our boat didn't have Dennis Conner, who many of you may know, is very active in that area. There were two American boats that year, and when he was going to the finals, he went, and he took our boat instead. So the engineering of our boat won. We didn't win in the end, but he recognized the superiority of that technology. So that's really exciting. We didn't have a great crew that year, at least I didn't think so, but we had a good boat. So yeah. So finally, I just wanna talk a little bit about this idea of convergence. We've been talking about convergence of, you know, data science and rocket science for a while now, but there's convergence between simulation, HPC, and data science, but also convergence around mechanical design and electronics. And I think, you know, all this convergence is sort of driving disruption, and that's when we see opportunities to really enter markets and make a difference. And so we see it in really three areas right now. One is in simulation, where AI is just becoming pervasive, and we are applying this technology throughout our products, and with all of our customers. The second is smart data, and I talked about the digital enterprise, but we're really helping to accumulate smart data, and it's really fundamental for customers and these digital threads, which enable AI throughout the enterprise. And finally, GPU acceleration and HPC cloud computing are really affording on all of us the ability to be able to do the training that we're trying to do with AI and the rapid scaling that you need to do in order to do that work, because it's very hard for, you know, on-prem compute resources to really support, especially creating these foundational models. So what's Altair doing to drive innovation and simulation? We've been working on physics-informed neural networks for years, not since ChatGPT came out, but for years. And we've had a lot of—There's a lot of startups in this space, by the way, and we've looked at acquiring some of them. But turns out, and many customers agree, that we've developed the best-in-class technology, and we're continuing to advance it, and it's, it's steeped deeply in a lot of our products. So that's super cool. Second is state-of-the-art optimization. I talked a little bit about optimization. You know, we, we have invested hard in optimization from the very beginning, and we even acquired, you know, Gary Vanderplaats's company, which was the only serious competitor that we had in the world of optimization, frankly. It was a little-known company, and I never understood why nobody else acquired them, and I finally did. And we brought the team together with our own team, some great technology, but some, some really great technologists, particularly in the nonlinear realm. And then AI and simulation-driven design. I spoke about that earlier. This whole idea of not just validating designs, but actually synthesizing designs with AI and simulation. Then we see it driving disruption in data science as well. Obviously, generative AI and smart data, again, are really creating this opportunity, I think. GPUs and specialized hardware Accelerators that, you know, companies like Google and others, and Groq and, you know, there's all kinds of stuff coming now. And again, HPC cloud computing. What is Altair doing in the realm of data science? Well, you know, we could see that there's this huge base of legacy data science technology that's out in the market, and it's primarily SAS language-based code. It's across all of banking. It's across all of the healthcare pharma market. It's huge. And it's even in companies like Ford and, you know, GM, and everybody's using SAS. And so we went out, and we acquired the World Programming technology, which brought us really a phenomenal team, by the way, this brilliant team of people in the U.K., but also a world-class code for compiling and executing and even the interactive work for SAS language work. And then we've been integrating it in order to be able to develop multi-language solutions with Python, R in the healthcare pharma market, and SAS. So you can call SAS functions. You know, everyone wants to go to Python, but actually, SAS is a very, very strong solution for statistics and very, very high performance, large scale datasets, which is not always gonna go best using R or Python. So a mixed language future is really the way towards modernization, we think. Second is democratizing data science with no-code and GenAI, and the RapidMiner platform is a true no-code platform. It's all drag and drop, and now we've put a GenAI front end on it. And even this last release, we've added more fine-tuning to it to, you know, reduce the hallucination effects of what's happening. But all of that democratization, together with being able to deliver to coders, is. A lot of companies have tons of coders, and they don't wanna do just, you know, no-code development. And then finally, leveraging HPC to operationalize at scale. When I acquired RapidMiner, a German company, the largest customer is a material company, huge company, 400 manufacturing plants, eight different departments that they were developing. They had, like, 3,000 models that they had already built using RapidMiner that they wanted to operationalize. The biggest challenge was scaling them on their HPC systems, believe it or not. You know, we are best in class, arguably, at basically scheduling and managing workflows in HPC. So it may not be obvious, these, this cross, you know, this cross-dependency that we've been building through the years of accumulating expertise and technology and integrating it, that really differentiates where we sit. Then finally, I wanna talk about electronics, because electronics is going through a huge convergence as well. Things are moving from what used to be all 2D, got, you know, bigger and bigger, more and more, you know, transistors on every chip, and it was basically these huge, linear optimization problems to do the, the place and route. But now it's moving to chiplets that are not all 3 nanometers. They can be, you know, bigger. For example, it's cheaper to make them, you can make them in more plants, and you can stack them and connect them into these 3D IC types of architectures. But what changes now is with that world, you know, some of the things that were really critical in the 2D world still remain important, but maybe in some ways less so. What becomes really important is being able to deal with the 3D stuff of mechanics, right? Mixed with electromagnetics. The second thing is just AI with traditional optimization is gonna be more and more important, and you can see how much we've been investing there. And then finally, we see emerging technologies like quantum and photonics. You may have seen that Altair made a minority investment in a quantum company that's a software company, and in a photonics company that is making the laser, basically, in photonics. A lot of those investments are actually for us to learn about these markets, to work with, you know, researchers in these spaces so that we can build solutions that help them to develop the products that they're trying to develop. So what are we doing in the world of electronics? We're trying to build out, and I think we're a long way along the way now, a best-in-class, 0D to 3D, multidisciplinary modeling and visualization solution. Single environment to develop and validate next generation PCB and 3D IC designs. You know, these very large, complex systems in electronics are really gonna need this type of environment, not the environment that was there before, where it was just coding and then, you know, some schematics, and then you go right to place and route. New user experiences from code to schematics to full 3D visualization, supporting requirements. You know, we have a whole requirements management solution coming. All the data management, actually, too, smart data, digital twins with Twin Activate, optimization, and AI. And we're supporting developing electronic solutions, not just ICs, it's antennas, sensors, electric motors, batteries, even the manufacturing of batteries. We're probably world-class in simulating for the manufacturer. Most of the, you know, Asian battery manufacturers are working with us on how they're simulating the manufacturing processes. HPC scheduling and workflow dependency management. But today, I gave a little hint in my earnings call that I was gonna talk about something new. And what's new is that we've been working the last 2.5 years on SimSolid. And SimSolid, many of you know, 'cause you're always asking me, "Well, how much revenue does it drive?" But SimSolid has really taken the world by storm. We have over 3,000 customers, every aerospace and automotive company. It's waving across, and it's growing super fast. It is really kind of changing the market pretty dramatically for traditional simulation. But in order to do electronics, we had to solve some very key problems. You have very thin layers that you have to deal with. You have to deal with complex solutions, so real and imaginary numbers, you know, going back to your math classes. There's a number of things that we had to really solve, and we think we've solved all of the R&D that we need to do there. So we're now at a moment where basically, in a couple of months, we're gonna release first version of SimSolid for electronics, which will handle structures and thermal, and pretty sophisticated thermal, by the way. We're talking transient, nonlinear. And then soon, we expect that we'll be able to do full-wave electromagnetics. But let me go through this slide a little bit because I think it's important. What's revolutionary here is that you go directly from ECAD to simulation without the traditional time-consuming meshing. So companies that are just now thinking, "Oh, well, we'll add meshing capabilities," you're not gonna need meshing capabilities in the future. Huge models, I'm talking huge, with absolute resolution, no simplifications. So when you do the simulations today in electronics, there's a lot of simplifications that go on, and homogenizations, and you're, you know, every time you're doing that, you're getting farther and farther from the right answer, and it takes a lot of time to do these models. This literally is just going ECAD to the solution. It's the reason it's revolutionized in auto and aero, and I think it's gonna really make a dramatic impact here in electronics. And it's enormous dimensional disparities. I think this is cool to talk about because we're talking about from meters to nanometers in the same model. You can't do that kind of stuff with finite elements. This is a big deal. I know you're all, you know, business guys. And then structural, thermal, and soon, and we already have results, full-wave electromagnetics. So we're super excited about this, and, and I think that... I don't wanna overstate it, but I think it's, it's a pretty important news here. So finally, I just wanna close by saying I think we're pretty uniquely positioned, the only company really bringing it all together. We've made huge investments over the last 15 years, and I think the solutions that we have, the market is actually really ready to embrace. So I think we're just in a great spot. So thank you very much. And next up is Sam Mahalingam. So Sam has been with me for 25 years and has been doing a phenomenal job. Please come up, Sam. Thank you, Jim. A very good morning to ladies and gentlemen here, and I'm Sam Mahalingam, the Chief Technology Officer. I joined Altair back in 1994 in India, and it's been an exciting journey to be associated with Altair as part of the tremendous growth that we have had. It's started. Yes. So, building on Jim's insights from Disrupt to Win with our key differentiation in terms of open solution and the convergence of data science and rocket science, today, I'm going to explore how to harness the power of computational intelligence that Jim spoke about at every stage of our customers' digital engineering journey, unlocking and accelerating innovation and success for our customers. Altair goes beyond the ordinary and focuses on not just the profits for our customers, but we partner with our customers to achieve engineering brilliance by pushing boundaries with cutting-edge technology to design and develop highly engineered products. We also slash the development time and the cost to reach customers faster with undefined speed to market. Finally, we are driving innovation and achieving lasting success for our customers through sustainable growth. Altair isn't just a platform or a technology platform company, but we are truly a partner for progress for our customers. So Jim shared Altair's vision, and I'm going to talk about how we are going to activate this vision by bringing data science and rocket science together into computational innovation intelligence. As I took charge of being the single CTO for Altair, my overarching vision that I set forth for the product organization was to transform the product organization into a platform and solution-centric organization. And I'm extremely happy to say that we have realized this vision by releasing four compelling technology platforms. And we have also built a lot of bespoke solutions on top of this platform. And just to name a few, like, for example, last year, we released HyperMesh CFD, which is an end-to-end solution for computational fluid dynamics. It's a single pane of glass solution, so you don't have to switch between multiple applications in order to achieve your CFD workflows. And we have also released one other solution on top of this platform, which is the HyperMesh NVH for the noise, vibration, and harshness, handling both low frequency and high frequency. Again, a single pane of glass, bespoke solution, where you don't have to use multiple applications as such. So this is how we are bringing solutions, and we are aligning solutions to the vertical markets that we are focusing on. I'm going to talk a little bit more about these four compelling platforms. HyperWorks is our design and simulation platform to design and validate products virtually. Then we have the RapidMiner platform, which is our data analytics and AI platform for building smarter decision support solutions. And then the HPC Works platform is our high-performance computing and cloud platform. This is to harness the power of high-performance compute resources as well as cloud scalability. And then finally, Altair One is our cloud innovation gateway to accelerate our customers' digital transformation journey. So these are the four platforms that I'm going to double-click into each one of these platforms to give you a little bit more insights in terms of how these platforms are helping our customers. Altair One is our one-stop shop for unlocking the full potential of a digital enterprise. We provide effortless access to simulation and data analytics tools, anytime, anywhere, by creating a truly dynamic and a collaborative environment to accelerate innovation. What we are also doing as part of Altair One is we are managing your entire engineering data, and we are tracking that data throughout the entire product life cycle, ensuring complete transparency with unified digital thread. And then finally, enterprises can transparently manage and scale compute, both on-premises as well as on the cloud, by optimizing cost and efficiency. This is truly a hybrid compute powerhouse that we are providing to our customers. We are making it easy for our customers to deploy the Altair One platform on-premises, on their own cloud, any, any of the cloud, because we are cloud neutral, and they can also deploy this platform and leverage it on Altair Cloud as well. So we are providing all the implementation models, right from a private cloud to a hybrid cloud to a public cloud. Altair HyperWorks is our platform for design, for simulation-driven design innovation. Then the current challenges for our customers is using different tools for different engineering disciplines that are siloed and that are not integrated. Altair HyperWorks bridges the different engineering disciplines, that is mechanics, electronics, math, and system modeling, all in a single environment. We are also providing powerful design, modeling, meshing, and post-processing tools, and we are connecting all of these tools for open and native mechanical CAD, electronics CAD, and to several different solver solutions or solver interfaces out there. We are supporting advanced simulation to provide access to high-fidelity, multi-physics simulation solvers, as well as to analyze all of these designs under real-world conditions, optimizing performance and manufacturability. What we are also doing with HyperWorks, with our HyperWorks platform, is we are integrating it with cutting-edge AI. I'm going to talk about this a little bit more in the future. And optimizing... And optimization is we are integrating with optimization because we are the leaders in optimization, and then high-performance computing for faster and smarter design decisions as such. The way we are building our solutions on top of our platform, on top of our application-friendly framework, we are exposing the same framework to our customers for customization and extension as well. And we are providing the customization and extension interface, both with, both with Python as well as with C/C++ development as such. And also, finally, it is a modern user experience that Jim spoke about. We are also providing game-changing, high-performance graphics engine, both for 2D and 3D visualization, in order to build your own applications, as well as a common backend data model, where the data can be exchanged between all of the different applications so that you can provide solutions which are single pane of glass, without having to switch between many different applications in order to achieve your end-to-end workflow. There's a lot of interest from third-party companies to sort of build these design applications on top of our HyperWorks platform, and we are seeing more and more of this interest coming in, both to sort of leverage our technology framework as well as our business models in order to deploy these applications into the market. We all know the challenges of implementing AI in the enterprises because the data is siloed.... The data quality is not that great. There is a skill gap in order to bring in AI and embed AI into the engineering processes, as well as the need for computational resources and integration with existing enterprise systems. We have been investing in data analytics and AI, and RapidMiner is our AI-driven innovation platform. The platform bridges the skill gap by empowering users of all skill levels to build data science workflows using generative, visual, coding and automated tools, all in a single environment. We are also moving from big data to smart data, the platform provides effortless extraction of data and preparation of data from any source, including reports and PDFs, unstructured, semi-structured, and structured data, and hence improving the data quality for the downstream AI workflows. This is what we call as a smart data. We are also breathing new life into the existing analytics environments, like SAS language and Python, by providing mixed-mode AI pipelining. Our customers can build on top of our application development framework, real-time, sub-second streaming applications, batch applications, where your AI models are deployed on the edge as well as in the cloud, in order to make intelligent inferences, as well as business intelligence applications. You can deploy these applications either on your desktop or on your public cloud as well. Finally, customers are faced with the challenge of analyzing the oceans of data in building AI models for making smarter decisions. For this, you really need a high-performance computing cluster, as well as a scalable compute backbone. Altair HPCWorks is our platform for compute-driven innovation. It is a compute powerhouse to harness the speed and efficiency of high-performance cloud computing. Our best-in-class scheduler, like Jim spoke about, optimizes dependencies and workflow, ensuring smooth execution of even the most intricate workflows, both for the data science world, as well as for the electronic design automation world. We are providing scalability for all kinds of challenging workload with Altair HPCWorks. We support massively parallel workloads for the MCAE world, mechanical CAE world. We are supporting high-throughput workloads for the electronic design automation world, as well as for the financial services world. And finally, IO-intensive workloads are being supported as well for the AI workloads as such. We are also providing rich set of tools to access, control, and optimize your HPC resources, both on-premise as well as on the cloud. And in order to foster innovation, collaboration is a must among engineers and designers. With our secure remote visualization technology, teams are able to collaborate and analyze graphically without having... regardless of where they are working from, without having to email their analysis to one another. And finally, we are providing observability at all levels and for all resources, including the allocation of expensive software licenses in the EDA world for optimal performance and cost management. Jim spoke about the focus areas. These are the four focus areas that we are trying to help our customers with, and I want to talk about how our technology platforms are really helping our customers, and how our technology platforms are aiding our customers to sort of mature themselves into a digital enterprise. For us, empowering the businesses to thrive in the digital age with data-driven insights and connected ecosystems, so that they can truly mature into a digital enterprise, that's how we are helping our customers with our technology platform. And also, to unlock new possibilities for our customers, we are powering the engineering and the AI, and the AI processes with artificial intelligence. And then to design and optimize products virtually, we are equipping our customers with simulation-driven design and optimization tools. And as the products are maturing to be electromechanical systems, we provide tools and expertise for designing and integrating complex mechanical and electronic systems as such. And let's go a little deeper into each one of these areas. Many organizations struggle on their journey towards becoming a digital enterprise because the engineering data is trapped in silos. The virtual models that they create are isolated and lie in the analyst's desktop, and the compute resources are scattered as well as scarce. So organizations can break down the data silos, can connect the virtual models, okay, optimize the compute infrastructure, and digitize their key engineering processes by leveraging Altair One, which is our gateway for our customers to mature into a digital enterprise. Our approach to digital engineering is to provide tools to build the One Total Twin throughout the product, life cycle, right from requirements to retirement. We are also providing One Traceable Thread, which is the seamless flow of data across all of the, digital twins that you are building, across all stages of the product life cycle, right from concept to retirement. And finally, all the models that you have created and all the data that is captured into a single source of truth for better decision-making and performance management as such. So imagine simulating an entire product, not just parts, in real time. The One Total Twin merges multidisciplinary simulation with digital twins, allowing you to model really complex systems from single component to a full system of systems. This, and we are also simulating across disciplines by seamlessly integrating mechanical, electrical, and other engineering domains in order to provide a holistic view for our customers when they are designing these products and validating these products. And finally, you can deploy the One Total Twin throughout the product life cycle for design and manufacturing and operations. This powerful platform empowers you to optimize performance, predict behavior, and make smarter decisions throughout the product life cycle. In this video, what you really saw was Cimbali, a commercial coffee brewing machine manufacturing company, leverage this platform to design and optimize their product. Digital thread is a concept in product life cycle, where it refers to the seamless flow of data across all stages of your product's life. Imagine a world where simulation, testing, and requirements all work together seamlessly. Our open and vendor-agnostic digital thread technology combines requirements, virtual models, simulation results data, test data, and operational data all in a single environment to provide a holistic view for informed decisions throughout the product life cycle. It also connects the existing enterprise data stores for long-term traceability as well as knowledge sharing. The application of digital twin and digital thread together helps formalize for our customers design to design, to develop, to deliver, to operate, and to sustain the cyber-physical systems that they are deploying into the market, as well as the practice of integrating digital twins and digital thread helps feed all the knowledge collected via model-based approaches to feed into generative AI for improved design as well as for efficient operations. So the next focus area for us was AI-powered engineering and business. Jumping into AI-powered engineering isn't a cakewalk, because the team needs to learn new skills in order to embed AI into the engineering processes. Like I mentioned before, the biggest hurdles lie in the data, the engineering data that is siloed, which is incomplete, which is of low quality, and to a large extent, the engineering data is not even digitalized. What we really need is a, is a data backbone that is essential for building an AI enterprise, especially for engineering. We understand the simulation engineering data really, really well, and this digitalized engineering data is helping our customers to build foundational engineering AI models for the engineering world. We are also augmenting, embedding, and enabling AI within the engineering processes. By augmentation, I am emphasizing on the collaborative nature of the human-AI interaction. By embedding, I'm really talking about the seamless integration of the AI models into our design tools, so that the end users can directly leverage the AI models instead of the physics-based solvers, right from within their design tools. Finally, we are providing all the enablement tools in order to build these engineering AI models, leveraging their own company-specific data in a very easy fashion, so that they can deploy these AI models into their design tools as well. We are also providing, like Jim mentioned about, we are also easily constructing generative AI applications by taking the foundational models that are out there and then fine-tuning these models with your own enterprise data. We recently released the GenAI extension as part of the Altair AI Studio. We are also providing enablement tools within the design tools so that you take our foundational engineering AI models that we are providing to the customers, and they can easily train it with their own local data and make sure that this particular AI model is further published into the design tools that they are using. Our RapidMiner platform is also seamlessly connecting to our HyperWorks platform in order to bring the computational engineering data from the engineering world into building AI workflows, and we are the only company that can do both these things under a single roof. So there has been a paradigm shift from human-assisted computing to the era of intelligent problem-solving with artificial intelligence. And then I'm going to talk about how we have come up with our Physics AI, which is our geometric deep learning foundational models that we have built, and we have seamlessly embedded it into our design tools. So this is truly boosting the performance for physics-based simulation. The analyst, as I mentioned before, can further train and fine-tune these Physics AI models, foundational models that we are providing as part of our design tools with their own company-specific data in order to improve the accuracy further with their own engineering data. And finally, Physics AI is significantly outpacing traditional physics-based simulations, leading to high-quality design because you are able to explore a lot more designs in the same amount of time. And this is... This truly shows our commitment to simulation-assisted AI.... We are also boosting our solvers, the performance of our solvers, by embedding neural nets right into our solvers using a unique AI simulation approach. And what we- and the way how we are doing it is without disrupting the end-user workflow, so that you are still simulating using the traditional physics-based solvers, and then you are reducing the time to respond, still maintaining the accuracy. And that is primarily what you are seeing in that video, where this is providing tremendous speed ups. I also mentioned to you in terms of what are the enablement tools that we are providing in order to build these Physics AI models. Like, in this particular image, what you see there is, as part of our Altair HyperWorks design tool, you are able to take our Physics AI foundational model, use your own data to sort of train that model, and further validate your new designs using that Physics AI model. So that is seamlessly integrated into our design tool itself, so our users don't have to go to another tool to sort of build this AI model and then deploy it within their design tool. Secondly, if you have enormous amount of enterprise data and you want to train and fine-tune that foundational Physics AI model that we are providing, you can actually use our Altair Design AI, which is deployed on the cloud, and you can use the scale of the cloud in order to train your model and further use that model within your design tool. And finally, with Altair AI Studio, we are empowering all the users of all skill levels to build these AI models, leveraging generative, visual, coding, and automated tools, all available under a single pane of glass with Altair AI Studio. When I talk about AI augmentation, for me, AI augmentation is a two-way street. What I mean by that is human expertise needs to be augmented to AI, and AI needs to augment human expertise. So here, what I'm trying to show on the left-hand side, you see the manual and the labor-intensive process. And this process is non-repetitive because an expert has to go and visually inspect every design and every simulation result. Whereas on the right-hand side, I'm showing you an AI process that is augmented with human expertise, which is using clustering, classification, and optimization. This is a repeatable and a robust process and does not require much of expert labor. Then you take the classification data, you inject it into a one-shot optimizer in order to meet all your subjective design criteria that was evaluated by the human expert in the past, alongside the objective key performance indicators. And here, what we are truly trying to show is, BMW, our customer, used our expert emulation layer, and our expert emulation AI is augmented with human expertise in order to solve their crashworthiness optimization. And we here are talking about how AI is being augmented in order to automate the end-user workflows. And we are eliminating repeated non-value-added tasks, such as searching for similar parts within a model, clustering the similar parts within a model, or classifying the parts when you bring in a new model to make sure that you can provide perfect structure and organization to your modeling environment. What we are also introducing is Aquila, our AI modeling assistant, that is going to aid the end users for faster model building. Simulation-driven design and optimization is an extremely powerful approach, but our customers are faced with several challenges that can hinder the successful adoption or implementation of simulation-driven design. Some of these challenges are: building high-fidelity models takes enormous amount of time and significantly needs a lot more compute power. Choosing the right optimization algorithm for the right problem can be challenging. And effective simulation-driven design and optimization needs true collaboration across multiple engineering disciplines. We, being the leaders in generative design and optimization, our customers leave the job of picking the right optimization algorithm for the right problem to our tools, and they have received significant. I mean, they have, they have optimized their designs in a very significant fashion. No other company has the depth and breadth of the manufacturing process simulation like we have. We cover injection molding, we cover stamping, we cover casting, we cover extrusion, we cover reactive foaming, as well as 3D printing, both selective and binder sintering. And our platform takes these post-manufacturing after-effects, then it will feed it into a structural model in order to build accurate and realistic digital twin representations. To build the high-fidelity models in a very easy and a fast fashion, Jim spoke about this, our core geometry framework now accommodates every type of representation, right from ordinary to extremely complex structures. We support parametric surfaces and solids, we support facets, we support PolyNURBS, and now we are also supporting implicit geometry. So here, I'm going to present to you as to how our customers are adopting simulation-driven design and optimization, leveraging the end-to-end workflow that we are providing all the way from geometry to manufacturing. So Altair Inspire seamlessly blends the familiar CAD tools with powerful simulation capabilities. You can start with a sketch, you can build geometry, and you can even import existing CAD models for immediate analysis. We also go beyond the traditional CAD limitations. As I spoke about a few minutes ago, Inspire now offers advanced geometry creation options, including PolyNURBS and implicit modeling, allowing you to create complex shapes for innovative designs that can be used for 3D printing. You can optimize your design for performance because Inspire offers structural, fluid, and motion simulations, providing valuable insights early in your design stage. And then you can automatically generate optimized designs based on your performance and manufacturing constraints. Inspire can handle everything from stiffness and weight optimization to 3D printing and casting considerations. And also, what we are providing to our customers is they can now manufacture these parts with absolute confidence because they are avoiding all the costly mistakes with our built-in manufacturing simulations, that's part of Altair Inspire. SimSolid is integrated into Inspire to further supercharge the simulations and drive the adoption of simulation-driven design and optimization. Imagine analyzing a complex design without meshing, without simplification and approximation. This truly provides 25x-100x time compared to traditional simulation methods, and Jim spoke about this. And SimSolid makes this possible by directly working on full-fidelity CAD, okay? Even on intricate PCBs as well as ICs, which has several, several, several parts without simplifying it. SimSolid is available on the desktop as well as it is available on the cloud, and it goes beyond simulation because it is so fast, we are now able to synthesize engineering data, use that engineering data to train and fine-tune a Physics AI model, which can further be used in order to evaluate your designs in a much, much faster fashion. So its power extends beyond the speed that we are talking about. SimSolid can tackle extremely large assemblies with thousands of parts, even on a standard laptop. You don't really need high-performance computing clusters in order to evaluate this extremely complex design, and that's the kind of speed-up as well as the efficiency in resources that it's providing. And it boasts broad range of physics, right from... Including advanced options like Jim spoke about, which is nonlinear analysis as well as fatigue. And now we are bringing the power of SimSolid to the electronics world, and Jim spoke about this in detail, so I'm not going to touch on that a lot more. But this is truly transformative because it is now catering to structural, thermal, and even full-wave electromagnetics as such. So lastly, the last focus area for us was about mechanical and electronic system design. Mechatronics is the fusion of mechanics and electronics, and it is truly providing a promise for building amazing products and deploying these products into the world. While this is exciting, there's a lot of challenges that our customers face in terms of designing these mechatronics systems. So one of the challenges that our customers face is combining the expertise in mechanical and electrical software, and control systems really require a strong communication and collaboration among these various engineering disciplines. Today, there is also a tools divide. What I mean by tools divide is the mechanical design tools are independent of the electronic design tools, and we truly need to bridge the divide between these two tools. So imagine seamlessly merging mechanical and electronic systems into intelligent, connected, smart, connected products. We are bridging the gap between the mechanical and the electronic design tools by creating a single ecosystem, right from concept all the way to embedded software that is going to be deployed onto the chip as such. We are also integrating the physical, the logical, the functional domains for truly a holistic view, a holistic design approach with our One Total Twin and One Traceable Thread and one source of truth technology, all on top of the Altair One platform. We are truly boosting the EDA design efficiency and performance, with our cutting-edge computing and scheduling. Especially as part of Altair HPCWorks, we have the Accelerator tool that is primarily used by electronic design automation companies in order to schedule the high-throughput workloads that they have. FlowTracer, which is another product as part of Altair HPCWorks, which is truly used for complex job dependencies and scheduling these complex jobs, and also providing debug ability when you are running these workflows for your electronic design automation. I want to share how we disrupted the mechanical CAE market with the most comprehensive pre and post-processing solution, and in fact, Altair HyperMesh, I call it, as a platform rather than a solution. In fact, the last release was the most compelling release in over a decade because we have reinvented it with modern user experience, and we have also and it provides unparalleled performance in order to boost the efficiency of the end-user productivity. The user-friendly, AI-powered engineering that I spoke about is dramatically reducing the modeling time in as part of the modeling assistant that we are providing and how we are augmenting AI to the end user workflows. We are also reducing the simulation time through embedded Physics AI models that I spoke about a few minutes ago, as well as design exploration. From day one, HyperMesh was designed to build and manage extremely large and complex models with a lot of assemblies, and this is truly a tool to build realistic digital twins with unparalleled performance. The open and the programmable interface that I spoke about, where we allow third-party software to integrate with our solution, and we integrate with third-party software, such as the CAD systems, as well as the several simulation solvers in the FEA, computational fluid dynamics, and multi-body dynamics. Python scripting and C++ interfaces are further allowing our end users and our customers to personalize the environment to their specific needs. Now we are bringing the same kind of transformation to the electronics world in terms of the modeling environment. So we are bridging the divide between the mechanical and the electronic tools to empower our customers to accelerate product development across all levels. When I say all levels, right from chips to boards, to systems, to system of systems. At the chip level, we are truly streamlining the design process by facilitating silicon debug tools, as well as with cutting-edge 3D IC multi-physics simulation techniques. At the board level, we are providing the PCB design for manufacturability. We are also optimizing the board behavior with signal integrity, with power integrity, and with our thermal simulations. And then finally, we are improving the reliability of the board by preventing thermal stress, fatigue, and other issues through our electro-thermal mechanical simulations. At the system and the subsystem level, we have developed... You are able to use our tools to develop comprehensive system modeling, encompassing electronics, control systems, and mechatronics design and simulation. We can also integrate the development of embedded software very early in the design stage with our tools, and we are able to, like Jim mentioned, we are able to design sensors, actuators, motors, and antenna, and we can even cover communication, coverage simulation, and system of system analysis with our model-based system engineering tools as well. We are bringing all of this on a single pane of glass, leveraging our composable application development framework, and that is Altair HyperWorks, a platform for simulation-driven innovation, both for mechanical as well as the electronic system design. Finally, Altair is leading the way in terms of transforming engineering user experience. At Altair, user experience isn't an afterthought, it is a core value. For over a decade, our design team has truly put in a lot of effort in terms of democratizing by providing extremely compelling technology through efficient, through efficient workflows that are augmented by AI, through intuitive interfaces, as well as providing a unified experience to stay consistent across all of our applications. We are continuously innovating to keep the user experience at the forefront, understanding that extremely exceptional user experience is a key differentiator in the modern world. This is one of the reasons why a lot of third-party companies are trying to leverage our application development framework in order to build these design engineering tools, and they want to deploy it to the market. Finally, again, I want to emphasize on the fact that Altair isn't just a technology platform. It is a partnership for progress for our customers to mature truly into a digital enterprise, leveraging our computational innovation intelligence. Thank you for your time, and let me invite to the stage the amazing Amy Messano, our Chief Marketing Officer, to share the marketing transformations. Amy. Hi, everybody. Thank you, Sam. That was a very generous introduction. I hope you're enjoying your morning so far today. So my name's Amy Messano. I've had the honor and privilege of being the Chief Marketing and Communications Officer here at Altair for the past five years, and we really have been on a journey of transformation. So I'm going to share some of that with you. So Jim shared with you our vision, and I think I have the best job in the world because I get to help tell people about the amazing people that you've heard about, and they're so smart, it's incredible, and products which Sam just shared, and the vision. So how do we do that? We do that by getting Altair on the leaderboards. We want to establish Altair as the brand leader in simulation, high-performance computing, and AI. And we do that by focusing on three main things. So we tell the story by creating really compelling content, and we grow the brand, and we create leads for both our direct and indirect channels. So it's kind of fitting, today is the first day of spring, if you didn't know. So we have been planting a lot of seeds, and those seeds are now growing. And it's... Some of this is just, I'm so proud of the team and all that we've been able to accomplish with our partners across Altair... So when we started this journey, we knew we had to establish the brand, but also create channels to tell the story. So we focused on three channels: owned, which was things like our website, social media. Earned, so that means thought leadership, media relations, social media, analysts—sorry, excuse me, analyst relations and media relations. And then also paid, things like search engine optimization, digital ads, and events. And by the way, if you didn't listen to our Future.Industry event, which is our flagship event, that just happened two, two weeks ago, we had more than 16,000 registrations, and all of that content is available for you. There's some amazing sessions with people like Jim and Dean Kamen, professors, customers, lots of really smart people. So let's get back to what we were talking about. So organic traffic, that means people came and found us all on their own, has increased. This is from our IPO to through 2023, 207% increase, which is pretty phenomenal. Our paid traffic, that means we did direct them there through paid ads and things like that, went up to 223,000. That's in one year. Our YouTube clocked in the growth at 336%, which is pretty incredible, and that's thanks to the videos that we created that talk about everything from how our innovation works to how to and explainer videos. So our media relations, the thought leadership program that we have throughout the globe, we have landed top-tier results in Wall Street Journal, Forbes, Fortune, Inc, and lots of other global coverage outlets. So that's increased 189%. And then back to social media. So LinkedIn, this one's interesting because we grew faster than any of our competitors, so 172,000, but our growth rate was faster than any of our peers. Now on to demand generation. So as I said, we create leads for not only our direct channel, but for our indirect channel, new users, new leads. We also help expand the footprint of existing customers, and then nurture leads as they go through the, the funnel here at Altair. So again, this is from IPO to throughout 2023. On an annual basis, our marketing-generated leads increased a staggering 741%. I know it sounds fake, but it's real. And our inbound leads, which is the best kind of leads, that means they came to us, they sought, they sought us out, increased 472%. So we're really proud of that. And then we've started to think about creating users for life, and that starts in primary and elementary school. So this year, we have launched a sponsorship with FIRST Robotics. So they have, they have kids in primary school, elementary school, all the way up through high school. So because of our, our partnership and our sponsorship, we, this is really awesome, have gotten our software in every single kit that every team, the thousands and thousands of children and teens and teams that are part of FIRST Robotics, use Altair software, have the ability to use Altair software. So we also follow them into the university years. So as they go from grade school, elementary school, and enter into high school, we follow them from STEM, and then they go into college. We have relationships with more than 10,000 colleges and universities globally, in 174 countries. We have more than 200,000 users in those campuses, and we have relationships with pushing 7,000 professors and growing. So we're super proud of that. Another thing that was very awesome for us and a proud moment is that we launched scholarships for STEM students this year at Columbia and at the University of Michigan. It makes us all very proud. And then when they graduate, and they enter the world of work, they become Altair ambassadors. They use... They're really engaged in our community platform. They get to experience our continuous training through our Center of Excellence, and they're just really great Altair cheerleaders, ambassadors, and they spread the word of how great our software is. They might even be able to win an Altair Enlighten Award. Sustainability is really front and center for Nikola. It's the core of our mission. There's over 4 million diesel trucks on the roads in the U.S., and each of these trucks, on average, emits 106 metric tons of CO2 every year, and that was the problem we wanted to solve with this product. Winning the Altair Enlighten Award really is a great honor. There could be nothing better than the day after we started production, receiving such a great award. So I wanted to take a minute just to touch on sustainability. A lot of companies talk about it, but at Altair, it truly is the essence of who we are. I think everyone at the company passionately believes that our software can change the world. It can make it better, safer, and more sustainable, and that really propels us in everything that we do. They are tireless in their dedication to making the world better. And one of the things that we have been supporting for years and running is something called the Enlighten Award, and it's widely recognized as the only award in automotive for sustainability and lightweighting. So there's three, the three different areas of our software, as you've heard a lot about, but to put it simply, as a non-engineer, the simulation, lightweight vehicles and other areas, which massively reduces carbon footprint. The high-performance computing makes your compute a lot, much more, much more efficient, which takes out a lot of the energy uses because compute takes a lot of energy, so it helps reduce that. And then with our AI, it gives people the ability to make really smart decisions to reduce waste, reduce use of energy, and just make sustainable choices. So all three together is a really big impact in improving sustainability. So we don't just talk about our customers and our partners, but we really care about our people. Jim touched on our culture, but I will say, as a true believer, people live it, embrace it, we don't just talk about it, and that helps us to attract and retain the best and the brightest. So you don't have to believe me, you can believe the 19 major awards that we've gotten recently from Newsweek, Economic Times, Inc., Investor's Business Daily, Fortune, and Great Place to Work, just to name a few. So it makes us all really proud because we do embrace and believe, and live, and learn the culture. So lastly, our foundation is built. We are ready for continuous growth that's just, you know, gonna go off the Richter scale. We move fast, we evolve, we're going to continue that growth, and we look forward to double-digit growth year on year. Thank you all for your time, and it's my pleasure to introduce the very smart Stephanie Buckner, who's done an amazing job about organizing our sales force and our go-to-market through scale to help us get to the next level. So, Stephanie. Good morning, everybody. It's my pleasure to be here. I've been with Altair for 13 years, for those of you who don't know me. I came up through the partnering side of the organization, and over the last 6 or 7 years, I've run corporate development for all of our M&A activities. And about 3 years ago, I took on the technical field operations, and then just 2 years ago, a little more than 2 years ago now, I took on the entire go-to-market operations for our sales organization in addition. And so today, you know, I'm really excited to be here to talk about how I see us scaling. Sorry, I didn't realize that hadn't clicked. So scaling to the next level here. Since the IPO, as Matt's gonna talk about, we've had tremendous growth, so we've actually doubled, more than doubled our software revenue since the IPO, and we feel like we have a ton of room to grow still. Today, I wanna focus all about sort of the future of how do we plan to grow. I'm really sorry, I'm a little messed up on the monitors. So here, our go-to-market plan really encompasses these three key initiatives. So the first is all about organizing into market-focused teams with clear swim lanes. And really for us, I'm gonna dive deeper into it, but this is all about creating the focus for our high-value accounts, so that we can make sure we're putting the right energy into the right places. Second, in order to scale, we really need to increase our sales and pre-sales capacity, both from an indirect and a direct perspective. We've historically gone to market very direct, and we recognize that to get to that next level, we need to increase our indirect go-to-market, and we have been doing that since the IPO. I'm gonna talk a bit more about that as well, on the progress we've made and where we expect to get to. And then last, Jim spoke extensively on this, but we are really a land and expand type go-to-market organization, and it's all about that cross-sell that we do. And so we need to continue to cross-sell with our Altair Units model and manage our pricing and discounting. So more specifically, I wanna talk about how we see the market play out, obviously, with the pyramid on the right-hand side there, where at the top you've got enterprise accounts, and then coming down, you've got mid and small-sized organizations. We created a global verticals team, and this is organized by industry. This mainly focuses, as you can see at the top, with these enterprise accounts. And as an example for one of these is auto and aero. Gonna talk more about which ones we've selected, but we've also created a regional team that is both direct and indirect. And for those of you who don't know us, historically, we've gone to market with a regionally based model. So every country independently went to market with its own team, and here, by bringing together these teams, what we're doing is we're creating these global organizations that have expertise in specific industries, and they can leverage that information on what's successful within one automotive company, maybe in Japan, and bring that across to the U.S. organizations or, you know, a company in France and so forth. So this really allows for us to combine our expertise and focus on these high-value accounts. Not every single automotive account is in our automotive vertical. Only our high-value and strategic accounts are. So last year, we started the process from a sales go-to-market standpoint, creating these vertical teams. And these top four verticals, auto, aerospace and defense, banking, financial services, and technology, were the first vertical teams that we created. These are also our largest verticals. And then this year, we created four more vertical account teams: energy, healthcare, life sciences, heavy equipment, truck and rail, and consumer electronics. We also re-emphasized, actually, brought in some technical expertise on the defense side of our organization. So the key here for us is bringing both our talented sales team to really drive this, but also having the right industry technical experts who know in-depth what is happening with our customers, what challenges they're facing, in order to better address that. So in addition to the sales market focus teams, we restructured our technical field organization. Actually, I did this when I first took over the technical field organization three years ago. I created what we call the global technical team, and this really was about pulling together our technical experts from around the world. Instead of continuing that go-to-market strategy we've historically have with each country, we pool them together as one global team. As you can imagine, for us, if you have to have a technical expert in every single country on every single one of our products, that gets to be non-scalable, frankly. So here, by doing this, we've created tremendous operational efficiencies and reduced our cost. This is then a key aspect for us on our operational side, but it also works better for our customers as well, because no matter where they're located, they're able to get the best experts for their problems they're looking to solve, no matter where they're based and no matter where our experts are based. Then, in addition, we recognize that we need our technical resources located from a pre-sales perspective, right with our customers, so they can be hand in hand on-site with them, very often knowing what's going on with them. So we've also got a separate team that's technical account managers, mainly in the verticals, but we also have some on the regional side as well, so that they can really drive that forward. Then the third piece is we created more self-service go-to-market support that allows for our users to address their own needs directly, really just empowering them. And a big part of that is actually leveraging our AI and data technology to optimize the support and onboarding and customer experience in general. And so, again, this has really allowed us to kinda let users do a little bit more of the heavy lifting, but also give them access to what they're looking for faster. So a part of increasing our sales and pre-sales capacity is leveraging our indirect channel further. We've continued to grow our indirect channel over the last few years, as I mentioned, and here you can see that we're aiming to get to 20% or more of our software revenue coming through our indirect channel. And we're gonna do that by investing in more quality partners, investing specifically in quality partners in underperforming regions. So an example here is out of EMEA or, or Europe. We're a little underperforming from an indirect standpoint, and we're gonna enhance that. But we also need to increase the number of partners on the data side. Because it's a newer segment for us, we don't have as strong of an indirect channel there, and we can really step that up. Another big part of our indirect strategy is around the global system integrators. Again, when you look back for us, because we went to market both direct and we had our own consulting engagements that we were really driving forward, we didn't really need system integrators and their services in order to roll our platforms out and our products out. But now that we have both data and we're selling more from a platform approach, these system integrators are really important for us. And so over the last two years, we've worked hard to build these relationships, and I'm gonna talk about one example, but we are starting to really see traction now. The reason we're seeing traction here is because they're starting to recognize us as one of the best companies to partner with, because we not only bring amazing technology, but we have deep expertise. It really gets back to what Jim and Sam spoke about on data science plus rocket science. So what I wanna do now is I wanna jump in and talk about following what Jim and Sam spoke about on our focus areas. I wanna highlight customer engagements that we have and how we're actually applying what we talked about on the TAM and our cross-sell ability, because it is very real. Starting here with Leonardo. Leonardo is, for anybody who doesn't know, an extremely... It's one of the largest global aerospace and defense companies in the world. It's based in Italy. They're a phenomenal partner for us. They use over 35 of our products today, and here, what you can see they're talking about is Leonardo needs the best technology available and easily. That's why it's Altair. So they're a perfect digital enterprise example because they've worked with us to create real-time and data-driven digital closed-loop manufacturing. And diving just a bit deeper into them here. You can see their revenue growth from a historical standpoint. They've been a customer for us for actually over 15 years. They started on the simulation side, probably about 10 years ago or so. We cross-sold and added the high-performance computing engagement with them. But here you can see in 2020, Leonardo mainly was using modeling and visualization tools from Altair, along with the high-performance computing side. And then in 2021, we cross-sold data, and in 2022, we expanded the use for electromagnetics and optimization, and in 2023, we really expanded in simulation-driven design with Inspire and SimSolid, as well as the data side with the RapidMiner platform. And they're very heavy users, actually, of our IoT offering. So again, just highlighting here, you know, the TAM expansion opportunity that you're seeing for Altair and how we've evolved as a company. So you can really see, how this works, and because, as I mentioned, we're very much focused from a go-to-market standpoint on land and expand, this gives us huge growth opportunity into the future as well, because about 60% of our software revenue comes through expansion. And I should say about 60% of our new software revenue, because our renewal is extremely high. This is a major energy company, and we created a physics-based digital twin model of their wind turbine. And here, this is kind of a unique example because they were not a simulation customer for us, but the reason we won them as a customer is because of our data science plus rocket science perspective. So they're really leveraging us from a data perspective and using our digital twin model all around synthesizing data and doing predictive maintenance. But again, it's about the expertise that we bring, not just that we're a data company. And this is also a perfect example here where Tech Mahindra, we partnered with, and so here you're starting to see more of a global system integrator engagement, and we're starting to see more and more of these wins. We're super excited working with the system integrators. Here, semiconductor equipment manufacturer, where again, they created a digital twin of one of their machine chambers. What's really interesting is that their digital twin model is powered by simulation, high-performance computing, and data analytics. So we're really seeing all three legs of our business come together with this example here, and it allows them to accelerate R&D and optimize their equipment, for their customers, that their customers are actually the semiconductor companies themselves. Here is a global pharmaceutical company that's leveraging Altair SLC to run SAS language and really provide them the flexibility that they need, and they're using our simulation technology. In addition, this is a perfect one where they were a simulation customer, and we cross-sold them on the data side. So you see things going all different directions on the cross-sell, if you will. And really, we help them reduce waste to optimize for quality and efficiency. Another one here, Mabe, a global consumer electronics company. So they manufacture appliances such as washers, refrigerators, stoves. They're based out of Mexico. They again use all three of our business lines, and you can really start to see the excitement that companies are having around AI. Every conversation we're having with executives, they're always wondering what we can do from a data perspective to enhance our engagement. And so the fact that we're not just a simulation company that is dabbling on the data side, data is inherent to us. It's why the banking and financial services segment makes sense for us, because it has given us the power to be able to competently speak about how we bring data in with our customers on the simulation side. Here, we're going into the banking and financial services side. CGI, in case you're not familiar with them, they are using our data platform to discover patterns around payment integrity, which can result in millions of dollar savings for healthcare providers and patients. And what I really like about this engagement is they so recognize one of our key differentiators, which is our openness. And so the fact that they can use any language they want or no language at all, really empowers them to make the right decisions and modernize as an organization. A global electric vehicle company, here, they're using over 40 of our products. So again, I mean, a huge number of our products are across these companies, and this one is a great simulation-driven design example where...... They've got over 300 of their designers and engineers using Inspire and SimSolid and our engineering products. But the key here actually was really around SimSolid. So, you know, Jim has talked about it extensively, and here is a perfect example where as soon as we introduced SimSolid into this account, it's catching on like wildfire. It's bringing us to a different level of conversation within the customer itself, taking us more to the executive level because of the power this can do. And the fact that it's not just engineers who can use it, it's also designers, gives tremendous opportunity. So for us, this is just one of many customers where we're seeing SimSolid really catch on, but a good example here. You can see we're playing across many different places. On the battery side, I think Jim talked about how we have extensive, compelling technology for batteries, but also even just on plant operations. Autoliv, this is the world's largest automotive safety supplier, and here again, long-time HyperMesh user, cross-sold with OptiStruct, and then cross-sold on the data side with more Physics AI, RapidMiner, and we're already engaged on the digital twin side to take things further. And last here, this is my last example. ZF is a global supplier for automotive, but also for aerospace, marine, defense, rail. They're using our technology for PCB design verification, for fabrication and manufacturing. And with the amazing support from Altair, they're drastically expediting the PCB development process and enhancing its robustness. So again, our focus and my focus is on scaling Altair and taking us to that next level. You can really see we're starting to move nicely through the initiatives we've laid out. But most importantly, we're laser-focused on execution, and this is really essential for us. You're going to see it come out of us over the next few years, but this is really the key. So thank you. Up next, we've got Matt Brown, who's our Chief Financial Officer. So thank you, Matt. All right. Thank you. Thank you. All right. Hello, everybody. Thank you again for joining us. Thanks to everybody on the webcast as well. So I think we're pretty much right on time. We're going to leave about a half an hour for some Q&A here after I present. But so far, we've heard some really exciting opportunities in where this market is going. Jim talked about that. Sam spoke about how our products are being developed to address that really exciting market opportunity. Amy spoke about how we're driving forward with our brand and with lead generation. And then finally, we heard from Stephanie about how we're developing the go-to-market organization to go capitalize on that. So what I'm going to do is speak a little bit more about each of our individual TAMs. I'll dive into some of our past performance, as well as some new midterm targets for you, so we'll save that for the very end. So first, diving into the simulation and analysis market. So according to CIMdata, this market is at about $10 billion in 2024, and it's growing at a rate of about 10% annually. There are some really important market tailwinds that we're seeing in the simulation and analysis market, namely what we've spoken about earlier today already, which is this convergence of mechanical and electronic simulation. You heard a little bit about how in the past, these really were separate problems that were handled by separate tool sets. And what we're seeing now is that these tool sets are coming together as mechanical and electronic continue to be more and more intertwined. If you take the modern car, for example, whether it's an EV or internal combustion engine car, that really is a system of electronics and sensors that are working together with mechanical components to come together and get you from point A to point B. It's becoming increasingly important for these simulation tools to be able to model that together as a system. Really, really important tailwind for simulation. Another important tailwind for simulation is embedded AI and machine learning. So increasingly, this market is being driven forward by the ease of use and efficiency and additional intelligence that machine learning and artificial intelligence can bring to these solutions, which is driving forward this market and helping to accelerate it even further. Generative design and optimization continues to be very important for our customers. Understanding what constraints are on the front end, understanding that you need to solve for manufacturability, being able to come up with better designs faster in the process, results in fewer iterations when you're making those designs in an overall, improved design process. So very, very important. Continued democratization of this software. So this is a theme that we have spoken about in the past and continues to be a really, really strong driver in building momentum for simulation analysis. Really, this comes down to the fact that there are many more designers than there are highly trained engineers. So as these tools are becoming easier to use, through the help of, of artificial intelligence and machine learning, also just improved interfaces, we're seeing a greater adoption of simulation analysis early on in the process, so we expect that trend to continue. Finally, digital twin technology is a big market tailwind for simulation analysis. You know, having, you know, companies being able to simulate, and also compare simulated data with real-world data from sensors makes for a better design process, also allows our customers to do preventive maintenance and to prevent what can be really costly downtime. Moving on to high-performance computing, so the next market in which we play. Hyperion Research puts this market at about $2 billion in 2024. It's growing at a rate of about 7%. There are some meaningful tailwinds in this market as well. This is the middle market, specifically middle, middleware market, where we're doing workload and workflow management. One of the big drivers on this market is that there is just this increased demand for computational capabilities. So as customers are doing more and more virtual validation, it's becoming increasingly important to make sure that that's being done as efficiently as possible, so making the best use of compute resources is very important. Our customers are solving some of the most interesting problems in the world. Anything from climate change modeling, to drug discovery, to complex financial analysis, all of that is driving this market forward, and it's more important than ever that customers are making the best use of their compute resources in order to get this done. When we talk about some of the large and growing datasets that are out there, it really is critical that when you're doing deep learning and other types of analysis on these datasets, that you're using high-performance computing solutions in order to accelerate that discovery. So this is driving that model forward. Finally, emerging technologies. So AI, yes, that's driving that forward, as is digital twins, connected driving, many, many more tailwinds are increasing the need for high-performance computing. Okay, and then finally, in the data analytics and AI market, and specifically within analytics and BI, data science and data integration, outside market analysts combined have this market TAM at $28 billion in 2024, and growing at a rate of 17% annually. So this is obviously a big market. One of the big tailwinds on this market is just the enormous amount of data that exists. Some estimate that the amount of data that exists today will double in the next few years and will double every few years after that. You can imagine, you know, this exponential growth in data is really driving the need for customers to have a way to organize that data, to compile it, to manage it, and finally, to analyze it in order to make data-driven decisions. So this is driving this market forward in a pretty meaningful way. We have democratization of data science technologies as well as another market tailwind. So data science tools are becoming easier to use. Altair RapidMiner, for example, has many different features that make this easier to use, data prep functions, things like auto features like AutoML, auto feature recognition, auto clustering, and feature engineering. So, these are some of the tools that are important within data science capabilities that are democratizing this portion of our business as well. More and more enterprises are realizing the need to modernize and become truly digital enterprises, and are understanding the need to make data-driven decisions in order to just better inform their own capabilities moving forward. So I'm gonna recycle a slide that Jim actually showed earlier, which is where these markets are sort of coming together. We've talked about the blurring of the lines of these markets before, but I think that this slide really illustrates the point where Altair has been playing in these markets for a long time, and what we're seeing is that each are starting to accelerate and enable the other. So we have companies that are more and more interested in doing high-level simulation, which is now being accelerated with high-performance computing. All of that generates an enormous amount of data that these companies wanna make use of in order to make data-driven decisions. So that is then leading into data science. And back again, you can see that, that the feedback loop just sort of completes itself. Together, these TAMs represent, again, about $40 billion in 2024. But really, this is a situation where the combination is greater than the sum of its parts, because each is informing the other and accelerating and enabling the other. Now, this convergence is driving disruption in the markets, so AI, again, is becoming more pervasive. Smart data is enabling AI, and all of that is accelerated with high-performance computing. Altair is driving forward that innovation. And we've got physics-informed neural networks, modern optimization, and AI embedded within our tools, so really capitalizing on that. I'm gonna recycle one of my other favorite slides, and this is a slide that Stephanie had showed earlier. This really, to me, is my favorite example of what our best and most important customers are doing with our technology. So Leonardo, again, a large aerospace and defense company, has realized the power of using the entire depth and breadth of our technology. So starting in simulation and growing nicely as they adopted other tools beyond just modeling and visualization, adopting things like Feko for electromagnetics and SimSolid, OptiStruct, right? Very nice growth in simulation from 2020 through 2023. But you can see what's accelerating that growth is increasingly the adoption of high-performance computing and data analytics solutions. This is what we talk about with the cross-sell. This is a customer in which they're realizing a tremendous amount of value. They're getting way more value than we're getting, actually. But we get some of that, too, right? So that account for us has grown 50% in the last year, which is great. This is what we wanna see out of all of our enterprise clients. Let them realize the value of our full portfolio, but also that cross-sell opportunity is meaningful for us as well. So what has that cross-sell opportunity meant for us in the past? Show that a little bit, and then go into some of the future performance as well. Okay, so just a little trip down memory lane. This is what Altair looked like at the time of the IPO. So back in November 2017, the time of the IPO, we were roughly evenly split geographically in terms of our billings. About a third of our billings each in the major regions, Americas, EMEA, and APAC. We had about 5,000 customers. Software revenue, as a percentage of total, about 73%. 27% was in services and other. So that software revenue was at about $245 million. And then from a verticals perspective, that's down there at the bottom, we were most heavily concentrated in automotive. So more than half of our business concentrated in automotive, and thereafter, it was aerospace and defense, some technology, and, and so on. If you fast-forward today, we've made just an enormous amount of progress. Still about a third of our business each in Americas, EMEA, and APAC, but we've got more than 16,000 customers today. Our software revenue, as a percentage of total, is now 90%, with the remaining 10%, services and other. So software revenue, as of the last fiscal year, was $550 million, more than double the growth that we saw. It's at a 14.4% CAGR from the end of 2017 to the end of 2023. From a verticals perspective, you can see we've continued to diversify our customer base. We're now roughly a third concentrated in automotive. All the other verticals have grown. Automotive, aerospace and defense has grown, technology's grown, heavy equipment's grown, energy's grown, and we've added an entirely new vertical with BFSI. A really nice diversification now of our customer base. Our largest customer represents less than 2% of our revenue, so we're diversified across verticals, but we've got many, many customers, where we're, where we're not concentrated as well. About 93% of our revenue is, or billings, are recurring in nature. So we've got this highly repeatable annual lease subscription model, which is forming this very nice base from which to then grow and continue to expand within our customer base. Some somewhat more recent performance. The last time we were in front of you for an Investor Day was May 2021, so we had just gotten done closing out our fiscal year 2020. So I'm taking you from fiscal year 2020 through the midpoint of our guide at, in fiscal year 2024. You can see we had total revenue of about $470 million back in 2020, $392 million of which was software revenue. Adjusted EBITDA at the time was 12.2%. You can see the really nice growth that we've had since then. Midpoint of the guidance now through 2024 has us at software revenue of $605 million, total revenue of $668 million. That software growth is in the low double-digit range, but our adjusted EBITDA has grown even faster. Adjusted EBITDA now, we're expecting to be 22% at the midpoint. That's growth in adjusted EBITDA of about 26% year-over-year. Almost half of the incremental revenue growth that we've seen over this period of time has dropped down to adjusted EBITDA. We've gone from, again, from roughly 12% back in 2020, what we're expecting in 2022, at 22%. That 10 percentage point growth over that four years gets you to roughly 250 basis points of growth year after year. So really nice growth in revenue, but also in profit. That growth in profit has led to a meaningful increase in free cash flow. So again, starting back in 2020, where we had free cash flow of less than $27 million. Now fast-forward to the midpoint of the guide in 2024, we're expecting to generate $133 million of free cash flow. So, you know, roughly a 5x increase, in that span. This has left us with a really strong balance sheet position. So at the end of the last fiscal year, we ended with cash and cash equivalents of $460 million. Very low levels of debt, about $312 million, in principal balance on convertible notes, of which we expect to pay down about $82 million of that this summer. We have an untapped revolver of about $200 million, and so it puts us in a really strong position to be flexible with M&A, with opportunistic buybacks, for share repurchases. We also have capacity to raise additional funds if needed. So very strong position. Also just happy with the you know percentage of our free cash flow to EBITDA. So in the most recent year, 90% of free cash flow as a percentage of adjusted EBITDA, we expect that that trend will continue. And so the growth in adjusted EBITDA, we expect, will be reflected in the growth in free cash flow as well. So it puts us in a really positive spot for the future. Okay, so back from May of 2021, these were the results that we had shared with you in 2020, as well as the targets that we had put out for 2023. So in May of 2021, we had just closed fiscal year 2020, had 83.4% software revenue as a percentage of total. Our non-GAAP gross margins were at 74.7%, and we had adjusted EBITDA margin of 12.2%. At that time, we told you that we expected to be at 20% adjusted EBITDA margin exiting 2023. I'm happy to have reported just a few weeks ago, we exceeded those targets across the board. So 89.8% of our revenue was software revenue. Our non-GAAP gross margin was 81.8%. It's a really nice, meaningful growth there in gross margin, and adjusted EBITDA margin exceeded our goal, and we landed at 21.1%. So what's the next midterm target? The next midterm target we define at fiscal year 2026, and this is what this looks like. We expect to have about 92% of our revenue as software revenue in fiscal year 2026. We expect non-GAAP gross margin to grow to approximately 85%, and we expect adjusted EBITDA margin to be approximately 27% exiting 2026. You can see that growth in gross margin, about 300 basis points or so. That's gonna come from a mix of, a benefit of mix shift, more highly weighted towards software. It's also going to come from a benefit of having an increase on our standalone software margin, so combination there. The remaining 300 basis points we expect will come from OpEx and will be most heavily weighted towards general administrative expenses. About half of that 300 basis point growth, we expect to be in G&A. The other half of that growth, we will get a little bit from research and development and sales and marketing. We expect that we're gonna continue to invest heavily there, and that we can manage this balance of growth in on the top line with growth in our adjusted EBITDA margin. You can see from 2020 all the way to 2026, about a 15 percentage point growth in adjusted EBITDA margin over a span of six years. It's about a 250 basis point growth year on year, in adjusted EBITDA margin, which is consistent with what we've said in the past. We expect to continue to grow adjusted EBITDA margin in the range of 200-300 basis points per year. As with 2023, we look at 2026 not as the final destination. We look at this as another mile marker that we feel is important to communicate to you all, but is not the end of the road. We will continue to drive forward margin expansion beyond this. So I think that is the end of the prepared remarks. I'm gonna leave you with that. I'm gonna invite everybody up for Q&A at this point. So we'll get everybody back up here, and then we can answer all of your tough questions. How we doing on time? What time is it? Well, these don't come all the way off. Okay, so you think I'm the master of ceremonies here? Okay, I get it. We've got a couple of mics out here, and if anybody has a question... Josh, go ahead. Let me guess. I got two, but I'll, I'll start with the one that I think you want. Maybe, you know, when you think about or when you look out over the next three years, what's gonna be the biggest driver of broader adoption of your simulation tools? Is it going to be AI, or is it gonna be things like specialist technology out there? Maybe just take that one step further. How can you do both? I think Sam mentioned it. You may not have picked it up, but because SimSolid is so fast, not just fast executing, but fast to go from design to simulating, right? You're not going through this very laborious stage of building models, but it also runs very, very highly performant. We see SimSolid as the perfect solver, basically, for AI. So to do the training, we think SimSolid's gonna be a really, really big part of that. You know, to try and answer your question, I think both are gonna drive you know a lot of growth for us, to be honest with you. I think the market is continuing to be disrupted by SimSolid. These things take time, and we're adding more and more capabilities to SimSolid. Still doesn't do crash. You know, we're coming. So there's a lot of things, you know, still in the future for SimSolid. You know, it's a relatively new technology, you know, coming on the market, but it is completely transformational, I think, to the world of simulation. And then AI is obviously gonna continue to grow, and we just see a huge market opportunity, and we think we're in the lead. Maybe we're deluded, but we think we're in the lead. We've been... You know, from the time y'all threw tomatoes at us from the Datawatch days, you know, to now, I think, you know, most of the investors are, you know, basically understanding why we were making the investments we made. Yeah. You got a second question you wanna ask? I do. I'm not, I'm not gonna let Matt off the hook. Okay, go for it. But, you know, I think you've kind of definitely talked about it in the past, but maybe how do you think about kind of that rev growth or the top-line algorithm that you need, that 250 basis points? Yeah, good question. So our assumptions moving forward is that we'll be able to continue a lot of what we've been able to do in the past, which is low double-digit growth in software revenue. We don't have to imagine anything extraordinary happening in order to continue to get that margin growth. It just happens, it happens as a function of scale, and so we believe it's, it's, you know, well within, you know, our ability to continue to do a lot of what we've done here in the last few years and just continue that forward, growing top line, but being smart about where we're investing. Yeah. And I think we're pretty conservative about things. It goes all the way back. You know, we started out with an annual subscription model, and everyone was telling me, "You know, that's so dumb, Jim. You should be selling perpetual because you grow faster, and then your value is higher." And I, you know, no, I'm okay with growing a little bit slower, but knowing that there is a recurring revenue that, you know, that's attached to that. So I think it's sort of a very balanced approach to what we're doing, and I think the bottom line is gonna continue to expand. You see the 50% is dropping to the bottom line at this point. We're getting more and more efficient, and I think our go-to-market is getting really efficient as well. Other questions? Hey, Jim, with respect to your target of driving about 20% of your business through the channel, you know, one of your competitors has a relatively large channel business, and it's likely to see some disruptions over the next year or so. Just wondering if that's part of your... or you see that acquisition as maybe being a catalyst to driving your channel business? So I think there is, you know, a lot of uncertainty from that potential acquisition. We have, you know, 18 months to see if it materializes, of course. But during those 18 months, I do think there is uncertainty in the market, both for their employees as well as for customers, and we do see, and for resellers as well, because the resellers are concerned, you know, what, what's actually gonna happen. And so we think that does create a lot of opportunities for us, and we're certainly doing our best to capitalize on that, yes. Yeah, thanks for putting on a great event today. This is Matt Martino from Goldman Sachs. This is for Matt or Jim. You know, you guys provided a few examples today of some customers using 35-50 of Altair's products, which I think is really impressive, considering at the time of IPO, you disclosed average customers of around 15 applications, right? Right. To the extent possible, I'd love to get a sense of, you know, how many applications customers are using on average today, and kind of what you think the natural ceiling there is across your installed base. Maybe secondarily, Matt, for you, you know, how should we think about the split between sort of pricing leverage versus the cross-sell of new applications as it pertains to kind of the expansion aspect of your growth algorithm? Thank you. I'm gonna answer the first question by saying I never let Matt add additional metrics that we disclose, so we're not gonna tell you, you know, what the number is, you know, on average, and we're not gonna continue to track it. But it's been growing. It surprises me, actually, in some of these accounts. I think Stephanie mentioned over 35, right, at Leonardo. And we didn't know the number, actually. We looked it up to, you know, to state it here today, but it's not that uncommon, actually, in our enterprise customers. And because of data sort of driving in, it's gonna grow faster, and we're gonna bring more partners into the partner alliance on the data side as well. As we get more recognition and scale, we were just at the Gartner show, and very different Gartner show for us this year than the past. You know, basically, the customers know who we are. You know, they're beginning to recognize us as a player, and I think, you know, we're basically coming, and it's a very strong offering, and it's gonna keep getting stronger. Yeah. Yeah, from a blend of sort of usage versus pricing, the vast majority of our growth comes from the growth in usage. So, Yep ... you know, our customers are expanding. You know, some of the examples that we gave were on the cross-sell opportunity, but even just within simulation, there's an enormous opportunity for growth. We've always been really customer friendly, in terms of our price increases, really minor price increases. I expect that that'll continue to be a trend going forward. But the cross-sell opportunity, I think, is huge. I characterize it as being pretty early days for some of our largest customers to make use of the full scope of our product capabilities. So, I'm optimistic about the future for that cross-sell opportunity. One thing I'll mention, 'cause I just this morning, when I was looking at my email, there was an email asking if I can have a meeting in May with basically a pretty senior executive who's gonna be in town from one of the largest commercial aircraft companies out of Europe, who is a very, very big user of SimSolid. And And this is somebody who's two levels above the guy that we normally deal with, who's pretty high level. Okay? And, you know, in the email, he said... And he knows, and he knows all about SimSolid because it's just raging. And the example company that Stephanie used today, you can guess which electric vehicle company that might be with 300 designers, because there's many that are smaller. And, you know, they're the same thing. It's sort of starting to rage across these companies. And it's a new, you know, it's a new market that we're penetrating. You know, there's always been this history of wanting to get, you know, simulation into the designer community, which used to be a 10-to-1 ratio. It's probably less today, but it's, it, you know, it's a large opportunity, I think, and it's even coming down into the engineering, you know, side as well. In fact, though, you know, we have to look at pricing, and those are things that we are looking at to make sure that we're charging the right amount. But, you know, it's all about market share in tech and especially in software. Other questions? I think I have two questions, but- You only get one. Part A, part B. Okay. I'm not on the sell side, but I can add that. You mentioned the word discounting a couple of times. Mm-hmm. Maybe you could just talk about that in relation to pricing. And then secondly, on the indirect channel, where you mentioned your goals, who are those key kind of partners? Who are the larger partners out there in the universe that can utilize your product for the customer? You want to address that one, Steph? Yeah. So I can start with the second one first, and then we can come back maybe. So, I mean, when we talk about global system integrators, we're talking about, obviously, Tech Mahindra, but Deloitte, Accenture, LTTS. I mean, there's a large number of these companies that we're engaged with, TCS. So for us, it's about building that relationship over time and showcasing that, you know, our technology and our teams together can win. And so we're really making great progress. We've announced a large number of partnerships already. Some we are still working to prove out before we announce, but we're making great progress there. What was the second question again? It was part A again. Part A. Yeah. What was- Discounting. Oh, discounting. I'm not sure what you're asking around discounting. I mean, it's, it's something that, we're very, very attuned to. We put a pricing board in place, I don't know, 5 years ago, something like that. You know, it's something we have to pay a lot of attention to. The sales guys often want a discount, and, and if you have a customer who's had a certain level of discount, they're, they're expecting that. But as you, you know, as you, as you gain prominence within a customer, and you build relationships with those customers at senior levels, they begin to appreciate what you're bringing. And frankly speaking, I think we're very competitively priced, so we have the opportunity, you know, we have the opportunity to continue to, you know, work on that. We're still trying to provide a lot of value to customers. We're not trying to be difficult with customers, but, you know, we expect the customers to pay us for the value that, that they receive. Hi, good morning. Thanks for doing this, great information. Just two questions. One is just around the data analytics business itself. Maybe you can just clarify, where do you see sort of the best opportunities to leverage GenAI within that division that you have? And then also, can you just talk a little bit more about Physics AI and how you monetize that? I'm gonna let Sam take a shot at the Gen AI. That's always a question for me, how, where do you apply Gen AI, but go, go for it, Sam. Yeah, I think, mostly if you look at the Gen AI opportunities that we have today, it's mostly on the business side of things, because those, those are all text-based right now. And most of the foundational models that you see are mostly text-based foundational models that are out there. So the, the different functions within the enterprise, the large functions within the enterprise, and these are functions like marketing and, and, these are functions like marketing that can extremely, leverage useful and build useful Gen AI applications. And the Gen AI extension that we have provided as part of our Altair RapidMiner platform, it's making it easy for people to take any one of the foundational models, either from the big players out there or even the open source foundational models, and fine-tune it with their own data so that you can absolutely reduce the hallucinations that it's going to come out with, and making sure the IP does not go out of your four walls as well. So I think that is the place where we see a lot more Gen AI applications being developed as of now within the larger enterprises. Just to add on my side, you know, I think it's gonna play also in sort of the user experiences as well. We are already doing that with RapidMiner, where you can, you know, interact with it basically more with, you know, asking it things and getting results out of it. But I think it's gonna start to play also in the world of design, where you're sort of working in concert, traditional mouse-driven, but also asking it, build me something that is this way or that way, and sort of interacting with it in that direction. We're playing with those things already. So I think that's a good augmentation to what, I mean, what Jim was saying there is in terms of how we are augmenting AI, especially conversational. When I say conversational, it's text-based as well. Already what we are seeing as part of RapidMiner is those visual workflows that you're building today. You can actually automatically build those visual workflows by conversing and asking a question, saying that, "Build me this," and it's automatically going to build that visual workflow. As well as, as I explained, we are introducing Aquila, which is our AI chatbot for a modeling assistant, a chatbot, which is a modeling assistant, is also augmenting to our end users' modeling user experience to make sure that you can build these complex models extremely fast just through a conversational interface. I think these are the applications where we see a lot of growth within AI to start with. One thing I'd like to point out, just 'cause for me, I get excited about the fact that I have all these founders in the company. So Ingo, who is the founder of RapidMiner, is a very, very well-known, highly regarded person in the field of data science, invented many concepts that are in use in data science. RapidMiner is a 20-year-old company. It's not like 5 years ago that they started. They just couldn't really get escape velocity completely. But Ingo actually is the biggest driver around all the Gen AI stuff that we've done in RapidMiner. I mean, he's just amazing. And, you know, he's being pulled in all across the enterprise. He's a very, very creative guy. And it's just one example for me of where a founder, we have tons of them, by the way. I can point to almost every founder across my enterprise, who's just having an impact. They still have that entrepreneurial spirit. They're in an environment where, you know, they're seeing what's happening. It's hard to explain across the enterprise, so they can actually poke in and add value in very, very interesting ways. So I think it's coming. There's a lot of hype around Gen AI, too. Let's be real, and you know, we're steady as she goes, applying it, experimenting with it, and finding where we can get real value from it. Hi, I'm Ken Wong from Oppenheimer. Thank you for the presentation. I couldn't help but notice on the Leonardo account, you guys, on the slide you showed, that the analytics piece, of that account grew faster than the HPC business. Is that a trend you see across all your customers or a lot of customers? And the other thing I'd like to add is, that market does grow at twice the size of... or twice the rate of the simulation market. Would you guys, in the future, at some point, become just an analytics company if it continues to grow at that rate? I think it's all jargon to some extent. Analytics and visualization and all, I mean, it's all the same stuff we've been doing. That's why when we did you know, the Datawatch thing, I felt it was an extension of what we were already doing, you know, in many ways. But I had this discussion with Matt also just the other day, because I do think that that piece of it is, in fact, the fastest-growing piece in the future. But it's hard to actually, especially with our model, it's hard to exactly decide, you know, what's AI and what's not. We have AI sitting inside of Radioss, OptiStruct now, and inside HyperMesh. I mean, and the user doesn't even know, you know, sometimes what's happening inside. We're not always making an announcement. There's a neural net running that's, you know, helping you with this time step or whatever. So, I just think it's really sort of blending together as time goes on. Yeah. Hi, thanks for taking our questions. Going back to the tiered go-to-market motion, so it may be a question for you, Stephanie. How is this helping you guys stay competitive, maybe more downmarket, and how is this motion resonating with those maybe smaller customers, and how are they adopting products? Yeah, I mean, so for us, historically, we really didn't pay a lot of attention, if you will, to the smaller companies. I mean, we played historically very much at the high end. And this resonates very well for these smaller companies because they're looking for real value, and the units model allows for a customer to have access to a huge number of different technologies. And so, you know, we're putting more and more emphasis. So many of the new logos and the new customers we're getting are on that downstream side of the market, and they are coming through our indirect channel. So we see huge opportunity, and I think because we offer such a comprehensive solution, those smaller companies really appreciate that. And often the expertise we layer on top of that, where our team isn't just handing you a piece of software and saying, sort of, "Good luck! Hope it works." We're helping our customers through that process, and it may not be, you know, somebody technical sitting next to them. It may be through more of our community or our how-to videos and things like that, that we've done, but we are really ensuring that there's that right customer engagement and experience. Jim, we have a question from- We have another one here, but we can do one from- Dylan Becker of William Blair. Okay. So, Dylan is asking about the shift to more Altair Units, packages, use, as he called it, and really looking at that based on increased functionality of the products, increased compute intensity, increased unit draw. And the question is: How is that going, and how do you think about going forward usage being driven as these applications become, I'll use Dylan's word again, more intense? I'm sure I heard every word that you said, but I think I got most of it. So, I mean, I think the suite concept is very, very successful. But like everything else, that's why I talk about, we experiment, we tune, we make changes. So we actually have dropped a couple of the suites over the last year that we didn't see as much traction for, that didn't make sense for us. We've changed some pricing. We did some shifting around of what's available, you know, in these different packages, so we're always tuning it. But in general, I think having these suites gave us solutions at price points that are attractive, you know, to specific market sets, and it allowed us to really sort of maintain each product, still drawing the same number of units. So it's been pretty, pretty powerful and, and pretty successful, I guess, is the answer. Not sure if there was something more there. There was a question here in the audience, too. And by the way, I think Jen Ristic handed out a press release that we put out around SimSolid this, this morning while we were talking. We, we issued the press release during our, our, our meeting here. So please. Thanks for taking my question. So, like, as you said earlier, there's definitely a lot of hype around GenAI for sure. Like two days ago, NVIDIA called out some partnerships, like using their Blackwell product, Ansys and Synopsys and Cadence, and Cadence's Millennium product. And there's obviously a lot of consolidation going on in the industry as well, with BETA CAE Systems, and everyone, and your competitors are getting bigger. I was just wondering how you're planning to invest at scale and compete at scale going forward, like, your strategy around partnerships and tie-ups? Thanks. You know, I think you have to distill what's marketing and what's real. And what I find is that the customers are very good at distilling it, but, you know, sometimes the investment community and others are less, you know, capable of making, you know, that distinction, if you will. So I think, you know, what we're delivering is real technology. I think we have very deep, you know, broad technology that we've invested in for 40 years. Doesn't mean our competitors aren't, you know, doing a lot of great things, and I think they are, but I feel very, very confident about, you know, where we sit. And Altair has been a company that has danced with elephants, basically. As long as, you know, since the time of founding, there's always been much bigger players. And frankly speaking, in most cases, I've passed most of those players. I can list them out and show where they are. So, you know, we're continuing to march forward and invest, and we keep our eye on what we do, and we work closely with customers and solve problems for customers. You know, I think there's a lot of challenges for everybody, you know, large or small, frankly. Thank you. Any more questions from online? If not, is there food? There is food. Yeah, sorry, I don't mean to be in the way of you and food. You're not in my way. Yeah, two for me. One is that still today, your typical manufacturing entity is spending a pittance on simulation. So is, is the Physics AI of the Altair Design Studio enough to really move the dial on that? And then the second question was just on, the ability of SimSolid to expand to other solver types. What are the challenges from going from structural to electronics, for instance? There are a lot of technology challenges to go from structural to electronics. But it's, you know, I'm very pleased that we've been able to navigate that, and it's been really hard work for the team. So that answers that question. I'm not gonna tell you specifically what those challenges are. Maybe at a different, more, you know, technical meeting, we might wanna do that, but I'm not sure the team wants to even elaborate on that. But we're feeling great about where we are. The things we have in front of us for electromagnetics are not research. It's more engineering and basically releasing commercial software. So we're in a good place on all that, which we're very confident. Okay, what was the first question again? Oh, no- What can expand, spend in our- Oh, yeah, yeah, yeah. Physics AI. Is Physics AI gonna be the big spend? I think it's one of the spends, but, you know, AI is more than just the Physics AI. I think the Physics AI is sort of an augmentation to simulation, that, you know, it. Simulation is just going faster and has always been getting faster. And we always have the fear, if it goes faster, then they're gonna spend less. No, they just do more. I read recently how, you know, when Google came to market, there was concern. It was very, very fast, and the reason it was successful is when you put your question in, you got a very instantaneous response. And there was a lot of concern that that means there's gonna be less time spent in Google. How did that work out, right? So, you know, I think it's a little bit the same in, in our world, in the world of design and simulation. The faster you go, the more they're gonna do, the more they're gonna do, you know, AI, because it's easier to run more simulations to build these neural nets. So I think it, it's all coming together in concert. But I think it's more for us than just the Physics AI. Our RapidMiner platform is allowing customers to build solutions themselves. If they have warranty analysis things, and they have a lot of data for warranty, but they also have data from their digital twins, and they have sensor data, now they can build out solutions that help them to reduce warranty costs, and we have projects going on like that, by the way. So I think that the use of data science, everyone wants to say AI, and so do I, because it's good marketing, but the use of data science and machine learning and AI is gonna continue to grow as you have access to smart data. You're able to mix data from simulation and sensor data. And, you know, all of that, I think, is something that Altair's been sort of onto early and investing towards, and it may not have been completely apparent to the whole market. It seems like it's only in the last year that it's become apparent to my much larger competitors. But, you know, I think that's part of the game, right? You have to run a little bit faster, you have to see a little bit farther and make decisions in the context of what you're seeing out in the market. So I think there's lots of, lots and lots of room for growth, and we are seeing it in these accounts. You see, you know, many of these accounts for us, that email this morning, you know, that account has enormous growth for us, actually. And it's, and it's, it's continuing in that direction. Aerospace is a really, really big growth opportunity for us because, you know, we're, we're not anywhere near where the capacity of that market for what we bring, you know, is at. Similar with defense, by the way, we just hired... I think Stephanie mentioned, you did mention that we hired somebody in defense. I think I mentioned that, but it's out publicly. Yeah. But we hired somebody to lead our defense. Fantastic person. Interviewed a lot of people. They were all like Navy SEALs and Army Rangers, super smart guys who also understand, you know, the world of the military and the world of consuming technology, you know, through the government and defense. And, you know, there's just a really big opportunity for us that we're just beginning to tap into. Any other questions? Okay. Okay. Well, thank you all very, very much for coming. Really appreciate the-