A real pleasure to have James Scapa and Matt Brown, CEO and CFO respectively. How are you liking the conference so far? Good. Amazing, right? I mean, it's day three, and I did not kid when I said day three is the best day. You know, we got- Here we are. you guys, and you know, we got Alphabet, Microsoft. You, you're in great company, by the way. Yeah. Yeah. Yeah. Thank you. A whole bunch of other companies yet to present. So, a real delight to host you guys. And I, I'm also gonna be joined by Matt Martino on my team. We have two Matts here. So the question... I don't know. Martino, I'll just say Martino, go ahead with the question. So, a quick introduction to the Altair story, if you don't mind giving us a good thumbnail sketch, and then we can jump into some questions. That'll be a good way to get— Sure Going with people that may not be completely familiar with the Altair. Okay. I like how you interact with the audience. Thank you. Great, great skill. So I'm Jim Scapa. I'm the founder and CEO of Altair. The company was founded 38 years ago, in my twenties, and I've been running the company since. Based in Detroit, started initially as a consulting company in the area of simulation. Very quickly began developing our own software, had a very big hit with a product called HyperMesh, and then continued to reinvest, you know, through the years in a number of different software applications. Have been very, very acquisitive and acquired 51, primarily founder-led, technology companies. Have built a portfolio, both organically and through this M&A activity, initially in mechanical simulation, like structures, fluid mechanics, motion simulation, systems modeling, and then moved to electronics simulation for printed circuit board, power integrity, signal integrity, electromagnetics, high frequency, low frequency. In the early 2000s, we began investing in high-performance computing, scheduling, and workflow management. And there, the customer base, unlike simulation, which is primarily manufacturing, the customer base is manufacturing again. We're running, for example, crash simulations on massively parallel computing architectures. But we also cater to pharma, and life science, to financial services, to energy, and, and basically a lot of other vertical markets through th e HPC. So a lot of government agencies, as well, that are using computing and need to really manage where and when the jobs run, and also dependency management. And then over the last 10-15 years, we have invested in data science and data analytics. So we have a portfolio of those three main elements, and we really see a big convergence in the market between simulation, HPC, and data science. And it's quite a real trend, so- Thank you ... the business. Good introduction. Jim, can you- Let me say a couple of business-oriented things. Yeah. We are around $600 million, around 3,000 people. We're a very global company. We're in 27 countries. Revenues are very evenly distributed, about a third, a third, a third between the three major zones. Great culture in the company, actually a very technical company. And probably 15,000-20,000 customers. So Jim, you talked about the convergence of simulation, data and analytics, and high-performance computing. So can you just kind of help frame for your audience - for the audience, your vision here? You know, how these businesses ultimately tie together. How they tie together? So, initially, you know, I was really moving towards this area of data science, machine learning, data science in general, because I saw the applicability of that technology in the world of engineering- Mm-hmm ... quite frankly. And we began developing some technology on our own. We made a small acquisition out of Italy, actually, in 2008. Had a lot of failure actually with that, but we learned a great deal. As we got going farther, I realized I needed more scale, to be very honest, and so I made an acquisition after we IPO'd, of a company called Datawatch, which had three really premier pieces of technology, thousands of customers, but most of those customers were in the banking and financial services market. Mm-hmm. But a lot of expertise in the area of analytics and data science. With that acquisition, I began to sort of cross-pollinate, bring that technology in more and more to my engineering applications. But we continued to keep the team oriented towards banking and financial services, where we also sell our HPC technology, and began to focus it more and more in that direction. And then, over the last few years, we made an acquisition out of the U.K., of a company that had a SAS, S-A-S- Mm-hmm ... SAS Language Compiler and execution environment, about 600 customers, many, many major banks and insurance companies. And then we acquired RapidMiner, which is a very premier data science platform. And bringing all that together has been really, really strong for us. We reorganized going into this year and cross-pollinated some of the people... so that we have some of the historically selling to the banking and financial services market, but data science experts in our auto vertical, in our aerospace vertical, in our technology vertical, and it's really going well. We have a huge number of applications really beginning to take hold. That's really interesting. And maybe for Matt and Jim, you know, can you talk a little bit about. You know, you talked about how acquisitive Altair has historically been, and I think that feeds in nicely to kind of your broader strategy around the Altair Units. Mm. Maybe you can just talk a little bit about, you know, kind of the uniqueness of that model, and just kind of frame that for the- We're gonna let Matt handle that question. Sure, yeah. So Altair Units is- He flubbed it on a different thing. That's how I tease him. Altair Units is a great way for our customers to go across our portfolio of products and also across their user base. So it really allows for, in the case of acquisitions, Matt, like you mentioned, when we acquire a company, our, our typical company that we're acquiring is we're trying to find a really nice piece of technology where we can tuck into our portfolio and then expand usage within our user base. Altair Units allows for that to be, very frictionless because we can roll that technology out into our suites, and our customers don't have to go and, and sign up new users because we're not on a, on a named user basis. They don't have to go through procurement or legal. They can just immediately begin using it. For us, that's important because we want to encourage usage, which then helps drive expansion within our customers. So the units model was. I created the units model in 2000, and all of our engineering or manufacturing customers are acquiring these units, which allow them to run any of the applications in the portfolio. As we went into the data science and data analytics world, there they were selling more traditionally, actually, in some cases, paid up, and named user. And we've been converting, especially the enterprise-level accounts, into the units model, that they were running a product like Panopticon, which is a real-time or streamed analytics type application, or they were running Monarch for, you know, data prep, or they were running something different. Now, we can bring them together. They can run those couple of applications, plus now they can run RapidMiner, or if they're running RapidMiner, now they have access to Monarch or, or Pano. And so we're bringing that same model, which has been enormously successful on the engineering side, into sort of this traditionally, I'll say, old-fashioned oriented business model world. Mm-hmm. Same time, our engineering customers, who are running, you know, as many as 20 or 25 applications in our engineering, now have access to RapidMiner and are beginning to do, you know, really interesting applications- So Jim- -with RapidMiner ... how do you price something like this- Yeah ... How do you price a unit when that unit- Yeah is fundamentally different in terms of the value that it offers across your engineering domain versus the data science domain versus the Because we price, each product in some number of units that it's drawing from the pool. Mm-hmm. So the price per unit is, say, $650. It's a little more complicated, but just assume- Mm-hmm ... assume it's $500- Yep ... per unit. Yep. HyperMesh draws 20 units from the pool. I see. Okay. RapidMiner might draw 15. Mm-hmm. You know, a very sophisticated product might draw 50. I see. That's how we do it. Okay. Yeah. Yeah, and the suites is something else that we should touch on, too- Mm ... because the suites are important. At the highest-end suite, you have access to all of our different products. Mm-hmm. But what we found, a little over a year ago, I think now, is in order to get more value at the highest end, and to give us some flexibility at the low end for customers that maybe don't need access to all of our products, we broke into about nine different suites now. Mm-hmm. Those suites are tailored to specific exercises. Personas, yeah. That's right. Got it. Got it. the price per unit per suite- It's different ... is higher as you have access to more and more- Oh, okay ... of the applications. Got it. Yeah. The enterprise level is, like, $850. Yep. The very, very base level is, like, $100. Got it. But you only have access to three fairly basic products. Okay. Since the personas are different, so you can appropriately tailor your go-to-market to the right persona. So you're not trying to sell unconnected, populations with a connected suite, right? Yeah, that's right. Yeah. What-- Just one thing to add to that. What we were finding is that we were probably undervaluing the unit- Mm ... in our largest enterprise customers- Mm that were running the entire portfolio. Mm-hmm. We raised prices. Mm-hmm. You know, if you were using all the applications- Mm-hmm ... at the enterprise. Yep. But it was also too expensive. Mm-hmm ... to sell into the mid- to low-end market. So by creating that low-end suite with a smaller set of applications- Mm-hmm ... that are more appropriate for that customer set, that might be more indirect, for example- Yep ... we now have a more competitive price point for, for that market as well. As you take a step back, you know, you've gone through a period of acceleration. Things are working out really, really well for the company. What are the drivers that are propelling the growth in your business? At a macro level, micro level, what are the big trends that have changed in your favor? I mean, our business is all about helping customers make decisions based on mathematics, you know, algorithms, optimization technologies.... AI or machine learning, you know, technologies. So it's all algorithm-based mathematics, numerical methods on the computer, to drive decisions, basically. And I think, you know, the world is moving more and more to relying on this sort of technology to design better products, design better marketing- Mm-hmm. campaigns to, you know, design better pricing models. And so we're, we're really well situated- Mm-hmm. you know, to help our customers across, you know, most everything they wanna do. Mm-hmm. But all about data and simulation using algorithms, right? Where else could you go- All about that. - with these, these capabilities? You said marketing. I've never heard of Altair in the marketing domain. So what else could you do with your- We have customers that are applying it in marketing as well. Really? It's not our primary focus. So we've tried to stay relatively focused by verticals. Yep. So the verticals for us are mostly the historical verticals, like auto, aero- Mm-hmm. - ship design, rail, heavy machinery, you know, you know, those technology, consumer electronics, and mostly in the design world. But we have such a large install base now, banking and financial services. Mm-hmm. insurance companies, that that is also a vertical for us, and it's very substantial at this point. Hmm ... and growing. And so, you know, do we have pharma? We have a lot of pharma, actually, believe it or not. Well, how does pharma use your product? Pharma is heavily using the HPC products to do drug discovery. Mm-hmm. We are, without question, the leading player for the HPC scheduling and workflow management. But they also use our products to do modeling, you know, mixing of drugs in a pill- Mm-hmm because you wanna have a very evenly distributed formulation. Mm-hmm. Or filling a pill bottle, you know, on the manufacturing line. And so we have a lot of simulation- Hmm Believe it or not, in the pharma world. We see a lot of opportunity. We already have a lot of applications in data science as well. Mm-hmm. You know, as we move more and more with the data science applications, even into manufacturing in general- Mm-hmm We were not really playing, for example, on the manufacturing floor. But this last year, there's a tremendous number of applications where we're taking sensor data and then visualizing that sensor data with Pano, and taking that data into RapidMiner and building models to predict, you know, downtime or scrap rates or, you know, all sorts of things. Fascinating. So yeah. Matt, I just wanted to quickly pivot gears to the fiscal 2023 outlook. So you guys have put up pretty strong performance despite the weaker backdrop. But even so, the guide does call for some deceleration relative to last year. So just kind of thinking through, you know, the key aspects of your growth algorithm. You know, where are you observing, you know, the most pressure, you know, within the business today? Yeah, we knew, you know, coming into 2023, you know, if you think back to six or eight months ago and more, most folks were calling for a very difficult 2023. And, you know, there's a lot of macro headwinds and fear of a recession. And at that time, it was, well... The debate was, "Well, are we gonna have a soft landing or a hard landing?" Now I don't think anybody knows what's going on. But when we were coming into the year, we actually just continued to feel very strongly about where we were positioned, how our customers were feeling about our products, renewal opportunities, expansion opportunities. Pipeline looked strong. And so we felt very good about 2023, but still, of course, had some conservatism around how the year would play out, knowing that 2023 was not going to be like 2022, for example. So 2022, we were coming off of a year where our software revenue growth in constant currency was above 17% growth. Right. In 2023, we looked and saw what was out on the pipeline and called for about 10% growth at the midpoint in constant currency. Our feeling then is, yep, 2023 is not quite what 2022 was. As the year has... You know, we've made our way through the year, we've just reaffirmed that viewpoint and continued to see that. Ten percent growth, not so bad, in, you know, in a tough year, but not quite as good as 2022. I think the important thing for us is we've used this year as a really important building year. Understanding that this was not going to be the most spectacular year for us, we took that opportunity to do some important organizational changes. So we had some changes that we made around go-to-market, where we organized in the verticals, specifically auto and aero technology, BFSI, to help with cross-sell opportunity. Those are things that generally take a little bit of time. So we've been able to do that while still, you know, you're sort of changing the belt while the engine's still moving. That's now well behind us, and we feel very good about how those verticals are now positioned. And then at the same time, we've been making really, really important product updates, which we'll be announcing in the next couple of months in more detail. But all of that, we feel like, sets us up in just a really good position for when the economy takes a slightly better turn to really, you know, come out of the gate with a lot of force and feeling really good about where we are. ... So Matt, you said, On that, if I could- Go ahead. You know, this is a really mature company. Mm-hmm. You know, the team that I have across the world has been with me typically over 20 years. Mm. Every country manager, every sales manager, you know, the team is really mature. Probably know their families, too. We know their families as well, 'cause I hired all the- Mm-hmm ... initial country managers. In many cases, we're on the next generation. We've had retirees. Mm-hmm. There's a lot of growth from within the company, but it's a very, very strong, solid base. And so this sort of regrouping that we do, you know, the economy sort of breathes, right? Mm-hmm. It's an organism almost. Mm-hmm. This is the sort of regrouping that we've done several times. Your own ohana. Right. Yeah. So Matt, you're asking, we don't know what kind of a landing this is, a hard landing, whatever. Do you know what landing this is? Jacob, what is the land... What do you call this? It's a Software Landing. Software Landing. There you go. Software landing. Software Landing. That's a Goldman Sachs term. Okay. Nobody else can use it. Okay. So, Jim, a question for you. Yeah ... you've seen a lot of market cycles in your career. How does this cycle compare to the ones that you've seen before? Is it nearly as much better than the prior cycles, or actually just about as bad, or? I'll be honest, I've been surprised that it hasn't been a little bit worse. Actually, I predicted this to be quite a tough year. Yeah. And- You're not alone. Many people were... Yeah, see. Yeah. So I would say it's done better. Mm-hmm. I don't know who we give credit to, but- I think you should give credit to Jan Hatzius, our economist. All right. He engineered this, this Software Landing. So, I mean, I think it's been... You know, generally, it's not the most exciting. We've worked hard, actually, you know, I would say, to achieve this year. But we've also been doing this reorganizing- Mm-hmm ... through the year. Mm-hmm. You know, a lot of really important work has been done, as, as Matt said. Mm-hmm. So personally, I'm extremely optimistic about the next two years. Mm-hmm. And a lot of that is more driven around the product work- Mm-hmm ... and the pipeline building work that we've kind of done through this year, and the partnering work that we've done. We've done a lot of work partnering with resellers- Mm-hmm ... and systems integrators- Mm-hmm ... particularly really significant ones, and that traction is just starting to build. Mm-hmm. Also with the hyperscalers, you may have seen that we announced with, you know, our SLC product- Mm-hmm ... the SaaS product- Mm-hmm ... is in the Google Cloud- Mm-hmm ... marketplace now. And we have a very large pipeline of interested, you know, customers, which is exciting- How much leverage are you getting? And Google- Yeah. I'm sorry? How much leverage are you getting from Google? I mean, how... Are they able to land customers, close deals, or? What's important is that they're collaborating with us. Mm-hmm. You know, I just went to Google Next. Mm-hmm. I don't know if you go to that conference. You know, one of my board members is a pretty senior exec- Mm-hmm ... at Google. And so I would say that his insights have been- Mm ... have been pretty helpful actually- Mm-hmm ... in understanding that hyperscaler market. Oh, geez. Okay. You're getting a call. I was, yes. You don't wanna answer it? The phone is there. Wow, I don't know how to stop it. Sorry, I'll just listen to you. Yeah. No, that's, with - when you talk to partners and customers- Yeah What does next year look like for your customers? I mean, what are they planning for? What are their budgets looking like at this very early stage of the game? So, you know, what we're seeing is actually, you know, a sense that things may start to get back to normal. Mm-hmm. Mm-hmm. What I like, and this is what I talk about, I think we're at the tail end- Mm-hmm ... of what I think has been a downturn. Mm-hmm. Maybe not a really deep downturn- Mm ... but a downturn nevertheless. And what I see is that, you know, through that period, you know, customers are watching- Mm-hmm ... costs. Mm-hmm. But in the software world, there's kind of religion. Mm-hmm. And so because of religion, they might hold on to - Mm ... you know, the users hold... That we have to use this product and can't switch to the Altair product because our pricing model is very predatory. Mm-hmm. But as you come to the tail end of it, we're seeing more and more willingness, because they're really looking for how do we save money, right? Mm-hmm. And so, you know, the Altair business model and the really strong products that we bring to market- Mm-hmm ... becomes even more attractive. So we're starting to see more of that, where- Mm-hmm ... customers are saying: Mm-hmm. You know, how can we use more of your products- Mm-hmm ... and less of some of these point solutions that we had before? Mm. That usually is the tail end, and then we lift again. Yeah. I hope I'm right. No, no, you're... Yeah. Yeah. I mean, it's... And I have one more question before I turn it over to- Yeah ... Matt here. So when you look at your unit consumption, is there any trend that is discernible? Are people more willing to use up their units? 'Cause one of the trends we've seen in software land is customers underconsuming relative to their commitment. Mm. But We don't have that problem. Yeah, okay. Yeah. So- Yeah ... you know, one of the things about our units model is that the units are most often- Mm-hmm ... very fully Mm-hmm ... utilized. Mm-hmm. And so then they bump up, and that's why we have so much expansion. Mm-hmm. So, that's just not typical. Very often, other software companies, I think, will sell a bundle of stuff- Mm-hmm. and a lot of it just sits on the shelf. Yes. Yes, yeah. That's not really our model. Mm-hmm. You know? All right. I'll turn it over to Matt. Yeah, Jim, I just want to touch on competition a bit, right? So Ansys, your primary competitor, pretty significant revenue share. Despite this, Altair's found success in the market. So I, I just wanna understand- Yeah Kind of what your view is, is in terms of kinda Altair's differentiation against maybe some of the larger simulation peers, what allows you to win in this market? I mean, I think we're a significantly more technical company. Mm-hmm. I think the products are superior. You know, they may argue differently. I think our customer engagement is, is much stronger, and so customers typically appreciate our expertise and, and the way we, the way we engage with them, and I think they appreciate the products as well. Understood. And, you know, maybe just kind of pivoting to this mid-market opportunity, which you touched on earlier. You know, you've got products like SimSolid, and you're moving more and more into the indirect channel. So can you talk about the progress on this initiative more broadly and maybe the importance of partnerships to the Altair story? I think for us to get sort of to that next level, whatever that is, a billion- Mm-hmm ... $2 billion, I think we have to have a very, very strong ecosystem of partners who are, you know, working with us. And, and particularly on the data, you know, the data analytics business, it's really critical because you need partners who have domain expertise. We have tremendous domain expertise in engineering, and that really differentiates us from a ABC data analytics company. Sure. When we're competing in an engineering world, we really deeply understand their domain. But if I go to marketing, that's not a domain that we have expertise, but there are partners who do. And so, you know, if they have a group that focuses there, and they're working with Monarch and RapidMiner and, you know, Panopticon, I think it's really critical. Yeah. Oops. I know data and analytics is a big growth vector for Altair, so can you just speak about the products where you're seeing the most momentum? You talked about RapidMiner, Panopticon, but just want to get a sense of, you know, what... where the strength is in that product set, maybe the verticals where you're seeing the most strength. I'll say that the SLC product- Mm-hmm ... is very, very interesting and exciting to a lot of customers. SAS is, you know, the behemoth in the world of, of data analytics. I don't know how big they are, $3.5 billion or something a year. Almost every enterprise customer has some SAS. Mm-hmm. In general, they are interested in moving away from SAS for two primary reasons. One is the customer engagement. I don't think they appreciate working with that company. Then the other is because all the kids coming out of school are not learning SAS language, they're learning Python. It is a challenging market, so that gets us in the door very often. That's a very interesting in the door, and we have a lot of pipeline building, but it's very slow because there's a lot of misperceptions around that. The SAS language is actually, and SLC, our code, is a very, very high-performing language. You know, essentially, you're at C++ versus Python, much, much faster performance, much better performing with very, very large amounts of data. And so if you have a very large bank, maybe you guys. And by the way, you guys are a great customer for us. I don't know if I'm allowed to say that. You said it! Um- I didn't say which product, or which product you displaced to go to us. But if you, you know, if you go to any large bank, and they're thinking, "We need to get away from the, you know, SAS. We may be spending $10s of millions, believe it or not, on SAS, and we're gonna go to Python." And, you know, at some senior level, they have this idea, "We're gonna learn Python," and, you know, they're not really thinking about the performance issues, the scale issues. And what we bring that I think is really a unique offering, and there's really nobody else, is the ability to take your SAS language code, execute it in the RapidMiner, you know, you know, operationalize it in RapidMiner, and then all the new code you're developing can be in Python. Right ... or it can be- Mm-hmm ... in the RapidMiner, you know, no code, just build blocks, which turns out to be a lot of Java, mostly. Mm-hmm. or you can write more SAS, because don't forget, there's still a lot of SAS developers in the organization, and you may want to develop more code in SAS or a mixed mode because you need something to be very highly performant. You know, so you may want to mix Python and SAS. So I think we have an extremely unique offering in the market, where we can support all these different, you know, a very multi-language environment. But You know, getting that message with clarity out requires working with the systems integrators, requires working with the customers, so I think that is actually the most interesting. And RapidMiner, in general, is just a fantastic platform for data science, and so we're having a lot of success with that in our manufacturing customers, for sure. ... That's great to hear. And I, Jim, I wanna touch on digital twins quickly. You know, can we talk about where we are in terms of the adoption curve for digital twins? What are the primary use cases you're seeing from customers? And then maybe even talk about some of the less obvious use cases, like financial services, for instance, right? Which I think most folks wouldn't make that connection. So I think, you know, there's been a lot of talk about digital twins for a long time- Yeah ... and what does that mean and all of that? Right. But I think we're finally, it's somewhat surprising to me, even, to be honest with you, because it's just sort of been happening in a pretty big way. But many, many, many of our customers are coming to us wanting to build out digital twin solutions, and I think it's just a very exciting, you know, moment. Altair has in its portfolio a product that we call DT Activate, which lets you build out a system model with the electronics, with the plant model, with the controls, to basically build out a digital twin. And then you can create these reduced order models using machine learning- Mm-hmm ... or AI. Mm-hmm. The AI word has become so, you know, phrase has become so big now. And so you, you can have, you know, these models built at different levels. It can just be some mathematics, a few equations, or it can be, you know, one step up, or it can be a machine learning model, or it can be a full 3D simulation, you know, that builds out that digital twin. And then the data that's coming from sensors can also participate in, in really understanding what's happening in the field with the product, that you're trying to model. So, it, it's explosive right now, actually, believe it or not. That's great to hear. You talked about AI. I think Altair's- Ask the next question. Yeah, so this will be the last question. So I think you've talked a lot about AI. Don't think we can get out of here without speaking to it. You've always been forward-leaning in that category, but can you talk about maybe some of the opportunities to further embed AI capabilities into your product portfolio? So right now, in the traditional engineering portfolio, there are actually four different applications that use AI. The most exciting ones that I talk about is PhysicsAI. It's built right into HyperWorks now. You know, if you run a series of six or seven or whatever simulations, it generates a neural net model. The next CAD design that you bring in, you don't even have to mesh it or model it, and you just basically go: "What is, you know, display for me the stresses, the displacements, the prediction, or just make a small change to this design, and what will be the impact?" And so that rapid, you know, design cycle that you now are allowing with that PhysicsAI technology is really transformational, I think. The other one that we're doing a lot of is what we call forecasting, where, you know, you might run a traditional simulation for 10 milliseconds, and now you can run 5 milliseconds, even for something pretty nonlinear, and it'll predict, you know, the last 5 milliseconds pretty accurately. So, you know, in the engineering world, it's really beginning to take root, I think, in a big way. That's fascinating. On that note, thank you so much. Thank you.