The agents can use the virtual twin as a gym to train themselves. So they can run, in fact, millions of simulation or design experimentation and present to you, to the human, to the engineers, the proven solution. Welcome to the NVIDIA AI podcast. I'm Noah Kravitz. My guest is Nicolas Cerisier. Nicolas is vice president of the 3D experience platform R&D for Dassault Systems. We're here to talk about the next generation of agentic AI systems, including industry world models, virtual companions, and the systems that are driving them. Nicolas, welcome to the NVIDIA AI podcast. Thank you so much for taking the time to join us. Thank you, Noah, and thank you for the invitation and this opportunity to be part of this podcast. Absolutely. The pleasure is ours. So maybe we can start with you telling the audience a little bit about Dassault System, have a long running partnership with NVIDIA. So you can speak to that a little and then also to what your role is and what the 3D Experience Platform is. Okay. So I'm Nicolas Solisier. I joined Dassault System in 2004. And I'm now the vice president of 3D Experience Platform Research and Development. And you have to know that the 3D Experience Platform is really the foundation for our 12 brands at Dassault System. You know, I think the main brands, Katia, SolidWorks, Simulia, etc. And if you don't know us, we enable our customers to imagine, design, simulate, build almost everything in the world. Cars, airplanes, autonomous robots, furnitures, electronic device, therapeutics, med devices, etc. It's 400,000 customers, 45 million users, 15 million scientists and engineers all around the world using our solution every day. And in fact, we provide our customers the factories to create their virtual twins. And what is Virtual Twins? It's really the scientific multidisciplinary multiscale V plus A, virtual plus real representation of the product you want to deliver. And in fact, we enable a product to be tested in the virtual world, in the real condition, before anything exists in the real world. And so today my focus leading the 3D Express platform is really to transform our platform architecture into an agentic platform. And in fact, this is our shift from a SaaS platform, SaaS architecture, to an agent as a service platform to bring AI to all our customers. So much has happened in the world of AI in the past few years. And generative AI obviously has been this touchpoint that set off large language models and reasoning. and now we're talking about agentic systems. So let's talk about these two terms, virtual companions and industry world models. And what do those mean to Dassault in the Dassault world? How do you use them? And how are they different from the types of generative AI that people might be used to using for the past few years? Yeah. So let's start with industrial world model. Okay. Our ambition, in fact, is to build AI for industry. It's very, very, really important for us. Industry is at the core of everything we do. And for us, AI for industry relies on three core principles. It should be grounded in science. And this is what we do for more than 40 years now. We are a scientific company. We deliver modeling technologies, simulation technologies. Then it should be fueled by industry knowledge. and it should be sovereign by design from the underlying infrastructure up to the models themselves. So how is it different from a generative AI? I think a classic generative AI learns the dynamics of the world from the observation and the perception of the world. So let's imagine they can see a video of a plane. They can predict if the plane will take off. if he will fly. But in fact, they don't really know why. Because they don't have the scientific explanation and the scientific foundation to understand that. And obviously, a plane does not fly by accident. So in fact, our industry world model principles, they understand how things work. They really understand the scientific foundation. They include the scientific, physics laws of the world. The physics, engineering rules, chemistry, material science, etc. And they combine the multi-scale, multi-discipline modeling and simulation technologies we provide with AI. And the technology we are delivering, our industry world models, rely on three technical pillars. First, the industrial knowledge. Here we are talking about the standards, the regulations, the processes from the different industry we serve. And we embed the real world engineering rules so the AI will understand and will speak the language of the industry, the jargon of the industry Then the virtual world understanding the world industrial understanding Here we are delivering an ecosystem of specialized industrial AI models which operate on our virtual twins. So, the virtual and real representation of the product you deliver. Right, right. And this integrates the structure and the physics behavior. So combined with our data system modeling and simulation technologies and solvers, this is how we can ensure that the AI will be grounded in science. And last is the industrial reasoning and generation. And this is where the agentic choreography takes place. And activating the industrial knowledge and the world representation to perform the experience-based reasoning. and so about virtual companion now if in fact if the industry world model provides intelligence the virtual companion turns that intelligence into action what we mean with virtual companion is we deliver virtual companion are your co-worker right they understand your intent of course but they will reason with industry world models to orchestrate execute action in context of of your business, of your industry. So they will comply with the regulation, with your KPIs, et cetera. Sure. And they will protect your most precious IP, of course. And something important, we don't want to replace people. We want to augment people. We want to free time to people to innovate and solve problems. So a few months ago, we introduced three virtual companions. Pora, the business expert. Leo, the engineer, who solve complex engineering challenges, and Marie, the scientist, who bring deep scientific expertise. So when you're designing and deploying the virtual companions, and if we think about sort of a workforce, a virtual workforce of companions that, as you said, aren't replacing human workers, but working side by side with us, in an environment like in a manufacturing environment or industrial environment where, I think of my work in content, creating content, podcasting and writing. And if an LLM hallucinates, then hopefully I catch it and I can make the correction or maybe it inspires me to something. If a system hallucinates in an industrial environment, the consequences could be much more dire. So how do you build trust into these systems so that the people who are designing and deploying and working in these environments feel confident working alongside the virtual companions? In fact, I think the foundation for trust in our system is the scientific foundation, scientific background, then the human in the loop, because at the end human is accountable and remain in the loop. Yeah. will pose when human have to take decision at the critical milestone of the execution and something very important we deliver and I think which is unique is what we call IP LM IP lifecycle management okay and we are we enforce the lineage auditability traceability of all the interaction of AI. So we are able to know that your content has been modified through which workflow, using which, what kind of models, etc, etc. And we provide the source of trust to understand how your virtual companion behaves with your content. So NVIDIA is bringing technologies, open models, Omniverse, accelerated computing, AI physics libraries, all these technologies into the stack. How do technologies like these help enable more capable and more secure agentic workflows? Yeah. So, NVIDIA technologies, in fact, infuse in every layer of our architecture, from NVIDIA AI, who is AI factories for GPUs and computing infrastructure to NVIDIA AI, KudaX libraries, and omniverse technologies to accelerate AI training, inference, and simulation. Regarding NVIDIA AI and Adjantec, we focus on our partnership with NVIDIA on three axes. Understanding, reasoning, and execution. Understanding, we integrate NVIDIA NIMS models into our outscale Kubernetes platform. Outscale is our IIS. It's a brand from Dassault System. And we are huge fan on NIMS because it's super easy to deploy and perfect. Always glad to hear it. All our team are in love with NIMS. Awesome, love to hear it. So we leverage NVIDIA open models for multi-modality, Riva, Parse, VLM. And with Parse, we improve, for example, by 30% our document injection and throughput. Plus also some industry specific models such as BioNemo for our virtual companion, Marie, the scientist. About reasoning now we leverage NemoTron 3 Super and the reasoning performance for Aura Leo and Mari have been improved by 20 without specific optimization And this is thanks to the collaboration with NVIDIA, we shared our industrial use case and benchmark. And so we were able to iterate together and to optimize the model and the integration. And then about execution, with NVIDIA, we are continuously improving the adjantic execution, leveraging the recent announcement of AIQ Blueprint and Deep Adjant. And we are also interesting and prototyping the recent announcement of Nemo Claw, of course. And we are exploring Dynamo to optimize the GPU utilization, and Nemo Adjant Toolkit for the optimization of our adjantic workflows. Can you speak a little bit to the partnership? You've mentioned it as you've been talking, which is kind of, you know, how it got started and more kind of what it means to DeSue and what it enables you to do. In fact, for over 25 years now, as you said, the system and NVIDIA have redefined what is possible together, moving from accelerating pixels to accelerating computing and now to accelerating industrial AI. And so back in 2000, from acceleration of visualization of Katia V5, our flagship brand and app, leveraging NVIDIA GPUs, to accelerating computing for Simulia, Abacus, and Xflow, our simulation brand, with CUDA and, of course, GPUs, to accelerating and optimization rendering with IRA, RTX, and now with DLSS. And so this year, we are opening a new chapter in this story with AI and combining NVIDIA technologies within our 3D experience platform to deliver industrial AI platform to our customers. I want to ask you about open and proprietary models and running a hybrid model. And my understanding is that DeSau runs hybrid models quite a bit. Can you speak a little bit to kind of the pros and cons of each and why you go with the hybrid models so often? Yeah. So, yeah, you're right. We have a hybrid approach. Of course, we build our own models. Yes. but we want to rely on the best-in-class frontier model provided by NVIDIA such as the NemoTron, or optimize the model by NVIDIA and available through NIMS, which as I said before enable a seamless deployment it's super easy. Or we have also a partnership with other model providers such as Mistral. In fact we select our models and our partners based on the performance of the model of course but also on the about the sovereignty and the regulation constraint okay because we operate worldwide we have a customer in all industry and many customers in regulated are very sensitive industries sure so we have to comply with our own regulation and all the auditability problematic right right and so from that we we also want to calibrate the model with the customer knowledge so we inject the industry knowledge through fine-tuning or rag depending of the use case. But more generally we believe in open standards and so we embrace and we support open standards such as MCP or agent to agent. In fact it empowers our agent platform to leverage sort-party industrial system and enable, in fact, interoperable or cross-system agentic choreographies. I want to ask if we can dig in a little bit to a specific use case to kind of get a flavor for some of the things your customers are doing. Maybe if there's an example that comes to mind you could speak to that really illustrates the use of the virtual companions and the DeSoe platform. Yeah. I think one super cool example, I think, is a Leo mechanical designer. Okay. We showcase this live, this new virtual companion in our 3D experience world conference last February with Jensen attending to this conference. And so here you give Leo a 3D scan or a 2D drawing or a mesh of a part. It will activate the industry world model for design, orchestrate the AI model and the modeling and simulation solvers. And it will perform a multi-tier planning, enabling the evaluating, in fact, the mechanical interface of the part, find the physics, the kinematics, and the design rules. And at the end, it will generate the optimized design, physically aware, manufacturable, manufacture ready, and it will do it right the first time. It's a very super example. I think it really illustrates our transformation from a SaaS to an agent as a service platform. And in fact with that we are giving to our millions of designers the power to innovate faster Yeah But it not just about speed it about reliability and trust And because you know that your design works because it born from science from physics and it's augmented with your industry knowledge. Right. That change that you referenced from a SaaS company to an agent as a service company, kind of from a philosophical standpoint, I guess, or an emotional standpoint, does it feel natural? Is it a big shift? Is it just kind of part of, you know, the way of doing things to keep innovating and delivering for your customers? And so it's just kind of the natural progression of things. How do you think about it? No, it's really about, in fact, when we the rise of AI, we think ourselves, what is the deep impact of AI in what we do and what we deliver? What will be the new experience for the user? What will be the new technology? We will see the cloud code, etc. What if you apply such transformation to our industrial software, in fact? So it came from that, in fact, really. And so this is a lot of discussion and brainstorming at the system. And in fact, we don't want to add AI on top of what we do. We want to put AI at the core. And this is why we are working with NVIDIA on the default topics. What's a typical way to get started? What's the first project that a customer might typically undertake to get started with virtual companions and working with them? I think you should start from your core business and your core challenge, in fact. Right, of course. This is where you will have attention from your teams. This is where you have your knowledge, your deep knowledge and your deep know-how. And this is how you know to measure the real impact of your AI and agentic transformation. Right. And we have an example of connecting to LEO mechanical design. We are working with NAYAR. And NAYAR is one of our customers working with us on Virtual Companion. And what they are doing to do is they recreate the virtual twin of existing aircraft. It means that they are creating thousands of parts without access to the original design. So basically, they disassemble the aircraft and recreate virtually piece by piece. Wow. So, of course, with LEO, you can imagine how it changed their life, automatically generating the 3D parts from their multiple sources. That's incredible. So like everything else in technology and in AI now, virtual twins, virtual companions, simulation, in just accelerating, advancing so quickly. And obviously, agentic frameworks and models are developing just as quickly, if not faster. What's next? What's on the horizon for Dassault Systems? What are the kinds of things you're thinking about? And then if you're game to take it a step further, where do you think agentic systems and the idea of virtual coworkers is headed? Okay. First, I think the system strategy is fully aligned with the recent NVIDIA announcement about Nemo Claw, AIQ, all the agentic stuff. And the rise, in fact, of the long-running autonomous agents. And we fully agree on the associated industrial challenges, security, compliance, etc. And tomorrow, our virtual companion, Aura, Leo and Marie, we believe they will stay awake and they will continuously monitor your factory, your project execution, your supply chain in real time. And they will proactively optimize it, optimize the virtual twin without being prompt by a human. So it will create, in fact, I think a closed loop autonomy. And because of our industry world models are grounded in physics, I think the agents can use the virtual twin as a gym to train themselves. So they can run, in fact, millions of simulation or design experimentation and present to you, to the human, to the engineers, the proven solution. solution. And you just have, at the end, to validate. And from that, the virtual twin, in fact, becomes a self-evolving asset that gets smarter day after day, in fact. Nicolas, there's so much going on. For listeners who want to learn more, want to learn more about the 3D Experience platform, about Dassault's work with everything we've talked about, virtual companions and industry world models, where's a good place to go? The Dassault website, social media? Are there research papers? Where can listeners go to learn more? Mainly on the system website, 3ds.com or on our LinkedIn page. We are communicating more and more on AI. Thanks also to the NVIDIA collaboration. We are posting more and more about what we are doing. So yeah, perfect. Yeah, that's free and connect with us. Excellent. Well, Nicola, again, Congratulations on all the work and thank you for the years of collaboration with NVIDIA. Thank you. And best of luck in everything you're doing. Thank you to NVIDIA, to the team, the incredible team.
