1985, by our founder and current CEO, Jim Scapa, has led the company since its inception. We IPO'd in 2017 and currently have more than 16,000 customers, operate in a very international way, and are focused on simulation, high-performance computing, and data analytics, which we believe come together in what's known as computational intelligence for our customers. A little bit about us at IPO versus where we are today. The company has evolved pretty significantly over the past almost seven years since IPO. So back in 2017, we were roughly evenly split geographically all over the world, about 1/3, 1/3, 1/3 in the Americas, EMEA, and APAC, about 5,000 customers at the time of IPO. And from a revenue mix perspective, we had a pretty significant portion of services and other revenue. You can see here more than roughly 27% of our revenue was in services and other, while the remaining 73% was in software. From a verticals perspective, we were fairly highly concentrated in automotive. About 1/2 of our business was in automotive. The second largest vertical was in aerospace and defense. If you look at where we are today, we are still roughly evenly split geographically all over the world, but we've significantly grown our customer base, about 16,000 customers today. Our software revenues, as a proportion of total, have grown significantly. Software now represents about 90% of our business, with services and other revenue representing roughly 10%. From a verticals perspective, we've also evolved. Automotive is still a significant portion of our business, but now represents roughly 1/3 of our business, as opposed to 1/2 of our business before. We've grown meaningfully in some of the other verticals, namely in aerospace and defense, as well as adding a brand new vertical, which is banking, financial services, and insurance, which we have as a vertical now, which we just did not have when we IPO'd back in 2017. Software growth over that period of time, about 14.4% CAGR from 2017 to 2023, and continuing to grow nicely. Here's just a look to see sort of the progression of both software revenue and total revenue growth. What we're also seeing is a meaningful increase in profitability. We start with just a very stable base of revenue. We sell primarily on an annual lease subscription basis. That provides a foundation for software revenue growth. Then over time, we've been able to grow our margins. We've been concentrating on improving profitability, growing margins from roughly 12.2% back in 2020 to where we expect it to be roughly 21.6% at the midpoint is where we've guided in 2024. So very nice profitability growth and free cash flows growing in line with that growth. We've got an extremely broad base of customers. You can see a lot of familiar logos here across a number of verticals. Our customer concentration is quite low. So our largest customer represents less than 2% of our total revenue. Again, just a very nice diverse set of customers that are the top customers in their respective categories. Okay, so some of our vision, what our vision is, is to really bring computational intelligence to our customers so that they can innovate. And we think that that happens in several different ways. And those are ways that we believe are converging today. We offer solutions, we like to say data science plus rocket science. We offer solutions in the area of simulation with Altair HyperWorks, data analytics with Altair RapidMiner, and also high-performance computing solutions with Altair HPCWorks. These come together in our cloud gateway, which is Altair One. There are some themes that we think are really driving forward, providing some kind of secular tailwinds in our space. One is the concept of simulation-driven design. So bringing simulation earlier in the design process helps inform a better design. The idea of some of these simulation tools becoming democratized is something that is continuing to catch on and we believe is providing a tailwind. And you can see some of the simulation capabilities that we're bringing into our designer suite. So going from geometry to manufacturing. Another tailwind is the electrification of various systems. So we're seeing an increased amount of electrification. We are seeing an increased demand from our customers in wanting to bring simulation capabilities for electronics together with mechanical simulation in a combined toolset that becomes important for our customers in having a comprehensive simulation solution. That's something that we think is a trend that continues and will continue to move and drive forward. And then finally, another tailwind is just AI-powered engineering. We have several capabilities that we fold into not only our data analytics and data science platform, which at its core is AI-driven, also bringing AI capabilities into our simulation tools. So things like Physics AI, which is a module that allows users to run thousands of simulations that are AI-informed using past simulations as a training set. So the user can make changes to the material thickness or various load constraints, and the Physics AI module will predict how that will perform under a simulated environment. Changes can be made, design enhancements can be done, and then a final simulation run can be run on the back end for verification. That helps for a better designed product, moves faster, and ultimately results in less iterations as part of that design process. So that's another tailwind and one way that we're incorporating AI into our product set. So I don't want to take too much time. I definitely want to leave time for Q&A. I do have a video, though, that'll play here, I think, and it's just a couple of minutes. We'll see how that goes. Imagine the possibilities. All of them. Now test them. Improve them. Make them real. Impossible? Not at Altair. That's because we're in the possibility business. Since 1985, we've been quietly creating the tools that enable visionaries to transform impossible into reality at market-defining speed. Best-in-class design and simulation. Enterprise-grade data analytics and AI. Turnkey high-performance computing. Now, as the only company bringing these technologies together, seamlessly merging data science with rocket science, we're setting the pace for a bold new standard of innovation. This is the science of possibility, where we can envision a better, smarter world and then bring it to life faster than ever imagined. Possible. Cool video. Great. Good job. Good job. Right. You're obviously a software company. I feel like we should start with some technology-based questions. You talked about three broad categories of products with data and analytics, simulation, and then high-performance computing. If I then think about your TAM and the way in which you describe your TAM, like the biggest and the fastest growing opportunities in data and analytics, does your revenue exposure with your product map against that TAM? So is roughly 70% of your revenue matching the TAM exposure? And if you're underweight in data, given it's the fastest growing, what do you do to solve that? Yeah, it's a great question. Roughly 70% of our business is actually on the simulation side. That's how we got our start. The remaining roughly 30% is split between high-performance computing and data analytics. But a couple of things to note there. Number one, those lines are really, truly becoming blurred. So it's becoming more difficult to understand where simulation stops and data analytics starts. And that's why we think of these more in terms of sort of computational intelligence. But one of the areas that we think is an opportunity that Altair is uniquely positioned to succeed in is in the application of data analytics into our engineering customers. We have a very capable standalone data analytics and data science platform, Altair RapidMiner, that we sell into the largest banks and financial institutions. They're leveraging those tools to do all sorts of fascinating calculations around credit scoring and options and a number of other very important business-related activities for them on an operational side. We're able to take those capabilities, and that's sort of a proving ground. You have to be the best in order to go sell to those banks. We can take those solutions now and apply our engineering domain expertise, and it becomes a very compelling solution to the 70% of our business that is now not currently buying data analytics solutions, but is beginning to. I don't think there's any other company out there that can combine the technical capability that we have on data analytics and the domain expertise that we have in engineering in order to provide that solution to the customers where they need it most on the simulation side. Just at a very high level, if I think about the technologies, if I think about banking and finance, it's math-based algorithms that I'm focused on. If I'm thinking about engineering, it's physics, it's chemistry, it's science-based technologies. Is it simple to bring the data analytics from banking into that engineering world, or effectively, do you need a second product to be able to do that? Well, so what we want to be able to solve is our engineering customers are generating a tremendous amount of data as a result of running simulations. Being able to capture that synthetic data in our data science platform and then go make informed decisions about that data, whether that's future product development decisions or go-to-market decisions, there's an enormous amount of opportunity there. The other area where there's opportunity is in the area of digital twins, where it's very important to be able to capture real-world sensor data and marry that up to the virtual twin and be able to understand, okay, how is this product performing in a virtual setting versus in a real-world setting? What are we learning there? What kind of anomalies can we find? And can we start doing things like preventive maintenance and predictive failures, things of that nature? Those are things that Altair is just uniquely positioned to go do. And then if I think about one of the other megatrends you were talking about, it's trying to bring simulation earlier into the design process. In Europe, I look at the space through the lens of Dassault. They would have design authoring and simulation under the same roof. Do you think you need deeper expertise in design authoring to be able to maximize that opportunity? Yeah, I think our Inspire suite of products that is geared towards the designer community has so many unique and interesting and deep capabilities. We feel very good about being able to play in that space and offer our customers a solution that begins to incorporate simulation elements. One of the AI modules that I mentioned in the slides is around Physics AI, which is in our Inspire suite. That's the type of capability that makes those tools very accessible and easy to use from a design perspective. And something that we're continuing to lean into because we feel that ultimately, by bringing simulation into the design process, our customers are going to be able to design a superior product in a more efficient way in a faster period of time. You've obviously mentioned AI a few times already. All investors are focused on it at the moment. Just how significant is it in your markets? And if you think about your R&D priorities at the moment, where is AI in that priority list? Yeah, so AI is something that we've been investing in for a while, long before the sort of large language model craze, which I think is meaningful and significant. I'm all in on that. We've been investing for quite a long time in our data science platform, bringing in capabilities like no-code, low-code capabilities, and auto feature recognition and auto anomaly detection. Those are things, capabilities that we've been building for a while. Also bringing those capabilities into our simulation tools, we think is really important. It makes the products faster, easier to use, ultimately more competitive. It's a significant focus for us. One of the other big themes, I guess, across this space more broadly has been M&A. Like you've been reasonably acquisitive in the past. Across the sector, there are some bigger transactions happening and bigger transactions that nearly happened. When I think about something like Ansys, does that make any difference to you in terms of shaking out the competitive landscape and disrupting some customer conversations? Yeah, so focus on our M&A strategy first. You're right, we've been pretty acquisitive. Generally, we acquire roughly one company per quarter. And generally, the types of acquisitions that we're interested in doing are technology tuck-ins or acquihires that we feel we can relatively quickly fold into our product technology, where we've made the calculation that we can get there faster or more cost-effectively by going out and acquiring that capability. And because of our business model, we are able to relatively quickly integrate that into our product portfolio and get that into the hands of customers by way of our Altair Units business model. So we have a pretty effective track record there. We have not typically done M&A that is bringing in a lot of inorganic revenue, for example, or what some may consider to be more transformational. Typically, we're focused on more of the tech tuck-ins. Regarding sort of the discussion on a broader level, you mentioned the acquisition of Ansys by Synopsys. In listening to Synopsys speak about that acquisition and maybe some of Synopsys' peer companies talk about acquisitions they've done, it's a very familiar theme for us, actually, at Altair. We've been talking about this convergence of electronics, simulation, and mechanical simulation coming together for some time. And that's an area where we've been investing and are continuing to believe that that's going to be a tailwind for some time. So strategically, it makes all kinds of sense from an electronics perspective. From a purely mechanical perspective, we feel very strong about where we are and are continuing to add electronics capabilities as well. And I think this provides an opportunity for Altair, frankly, in a couple of different ways. Number one, it's probably highlighted Altair in a way where maybe folks that had not heard of Altair before have heard of us now. Or maybe folks that weren't paying attention to this space more generally are paying attention and understanding that there's a tremendous amount of value in the type of technology that we provide. And that's a technology that's going to be relevant and extremely important for the long into the future. So I think that's sort of benefit number one. Benefit number two is I think it's reasonable to think that there's going to be some level of disruption in our core base of customers and something that we would like to go after and capitalize on. I'm conscious of time. I want to try and squeeze in a couple of financial questions. Firstly, in terms of the overall backdrop, we've seen some more mixed messaging from software companies. There seems to be a few more calling out softer macro. Is there anything that you've seen since the start of the year that suggests that the market's changing to you? So I think generally, the macro environment, it could be better. But having said that, our core customer base is to R&D teams that are pretty deep within the organization and whose budgets are generally pretty well protected. And that's because our customers are developing products that maybe won't see the market for months or years even. And for that reason, their budgets tend to not fluctuate based on quarter to quarter or even year to year results. They tend to be a little bit more stable. So I think that provides a balance. And that gives us some hope. And then second is we're cautiously optimistic about what the back half of this year looks like and into 2025. Ultimately, we're positioning ourselves to be able to capitalize on whatever the macro environment is by focusing on our products and our customers and getting our go-to-market right so that, look, whenever the macro does turn, we can make the most of that. And then I just want to finish on just the margin profile of the company. You're targeting broadly two points a year of margin expansion, if I'm right. How do you think about that pace of margin expansion versus the need for ongoing investment in the business? And then even if I give you two points per annum of margin expansion, it still feels like you're below margin levels of the peer group. Where do you think margins can get to on a five- or seven-year view? I'll start with the latter, and then I'll work my way backwards. So we've made a lot of progress in expanding margins over the last few years, but we're not done yet. And there's nothing structurally that would prevent us from getting our adjusted EBITDA margins into the high 30s, like our nearest competitors. Essentially, it's a function of scale. So we believe that ultimately we will get there. But sort of how do we get there? The kind of building blocks there starts with software revenue. We're going to concentrate on growing our software revenue, which will grow that software revenue as a percentage of total, forcing that number higher, which has an impact on our blended gross margin mix, driving that gross margin higher. So when we think about that 200-300 basis points growth per year, which, by the way, is not the same one year to the next. That's as you step back and you look over an average period of time. About half of that growth is going to come from gross margin. We expect the other half is going to come from OpEx by getting just a little bit more efficient and by really making the best use that we can of scale. Perfect. Good. We're pretty much against time there. Thank you so much for your time. Thank you for coming. Appreciate it. Thank you.