How Dassault Systèmes Is Building AI That Understands Physics - Ep. 296
Episode
23 min
Read time
2 min
Topics
Relationships, Fundraising & VC, Leadership
AI-Generated Summary
Key Takeaways
- ✓Industry World Models vs. Generative AI: Standard generative AI predicts outcomes by observing patterns — it can predict a plane will fly but cannot explain why. Dassault's industry world models embed actual physics, chemistry, material science, and engineering laws directly into AI reasoning, ensuring outputs are scientifically valid rather than statistically plausible. Engineers should evaluate AI tools by whether they encode domain laws, not just training data.
- ✓Virtual Companion Architecture: Dassault deploys three specialized AI agents — Aura (business), Leo (engineering), Marie (science) — each reasoning through industry world models rather than general LLMs. Leo, for example, takes a 3D scan or 2D drawing, runs physics and kinematics analysis, and returns a manufacturable, optimized design. Organizations building AI workflows should consider role-specific agents over generalist models for regulated domains.
- ✓NVIDIA Integration Delivers Measurable Gains: Integrating NVIDIA NIM models improved document ingestion throughput by 30%, while Nemotron reasoning models improved performance for Aura, Leo, and Marie by 20% without task-specific optimization. Teams deploying agentic systems should benchmark NIM deployments against existing pipelines — containerized NIM models reduce integration friction significantly across Kubernetes infrastructure.
- ✓IP Lifecycle Management as Trust Infrastructure: Dassault enforces full traceability of every AI interaction through a system called IP Lifecycle Management (IPLM), logging which workflows, models, and processes modified any content. Industrial AI deployments require this audit layer — without traceable lineage, human accountability breaks down in regulated environments. Build traceability into agentic architecture from day one, not as an afterthought.
- ✓Hybrid Model Strategy for Sovereign Compliance: Dassault combines proprietary models with frontier models from NVIDIA (Nemotron via NIM) and Mistral, selecting partners based on performance and sovereignty constraints. For global industrial customers in regulated sectors, model selection must account for regional data regulations and auditability requirements. Open standards like MCP and agent-to-agent protocols enable cross-system agentic choreography without vendor lock-in.
What It Covers
Dassault Systèmes VP Nicolas Saricier explains how the company is shifting from a SaaS platform to an "agent as a service" model, deploying physics-grounded AI virtual companions named Aura, Leo, and Marie to serve 45 million engineers and scientists across regulated industries worldwide.
Key Questions Answered
- •Industry World Models vs. Generative AI: Standard generative AI predicts outcomes by observing patterns — it can predict a plane will fly but cannot explain why. Dassault's industry world models embed actual physics, chemistry, material science, and engineering laws directly into AI reasoning, ensuring outputs are scientifically valid rather than statistically plausible. Engineers should evaluate AI tools by whether they encode domain laws, not just training data.
- •Virtual Companion Architecture: Dassault deploys three specialized AI agents — Aura (business), Leo (engineering), Marie (science) — each reasoning through industry world models rather than general LLMs. Leo, for example, takes a 3D scan or 2D drawing, runs physics and kinematics analysis, and returns a manufacturable, optimized design. Organizations building AI workflows should consider role-specific agents over generalist models for regulated domains.
- •NVIDIA Integration Delivers Measurable Gains: Integrating NVIDIA NIM models improved document ingestion throughput by 30%, while Nemotron reasoning models improved performance for Aura, Leo, and Marie by 20% without task-specific optimization. Teams deploying agentic systems should benchmark NIM deployments against existing pipelines — containerized NIM models reduce integration friction significantly across Kubernetes infrastructure.
- •IP Lifecycle Management as Trust Infrastructure: Dassault enforces full traceability of every AI interaction through a system called IP Lifecycle Management (IPLM), logging which workflows, models, and processes modified any content. Industrial AI deployments require this audit layer — without traceable lineage, human accountability breaks down in regulated environments. Build traceability into agentic architecture from day one, not as an afterthought.
- •Hybrid Model Strategy for Sovereign Compliance: Dassault combines proprietary models with frontier models from NVIDIA (Nemotron via NIM) and Mistral, selecting partners based on performance and sovereignty constraints. For global industrial customers in regulated sectors, model selection must account for regional data regulations and auditability requirements. Open standards like MCP and agent-to-agent protocols enable cross-system agentic choreography without vendor lock-in.
Notable Moment
Saricier describes a customer — Nayar — that physically disassembles aircraft and uses Leo to reconstruct thousands of parts as 3D digital models without access to original design files. This reverse-engineering workflow, previously manual and time-intensive, now runs automatically from scans and drawings.
Episode Transcript
The the agents, can use a virtual twin as a gym, to train themselves. So they can run, in fact, millions of simulations or design experimentation and present, present to you, to the to the human, to the engineer, the proven solution. Welcome to the NVIDIA AI podcast. I'm Noah Kravitz. My guest is Nicolas Saricier. Nicolas is vice president of the three d experience platform r and d for TESOL 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 for the invitation and this opportunity to to be part of this, of this podcast. Absolutely. The pleasure is ours. So maybe we can start with you telling the audience a little bit about Dassault Systemes. They 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 three d experience platform is. Okay. So so I'm Nicolas Soizier. I joined Dassault Systemes in 2004, and I'm now the vice president of, Swedish Smart Platform, Research and Development. And you have to know that the Swedish Smart Platform is really the foundation for our 12 brands, at Dassault Systemes. You know, things as the main brands, CATIA, SOLIDWORKS, SIMULIA, etcetera. And if you don't know us, we enable our customers to imagine, design, simulate, build almost everything in the world. Cars, airplane, autonomous robots, furnitures, electronic device, therapeutics, med devices, etcetera. It's 400,000 customers, 45,000,000 users, 15,000,000 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, multi scale 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 exist in the real world. And so today, my focus leading the Solid X 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 to bring AI to all our customers. So much has happened in in the world of AI in the past few years. And generative AI, obviously, has been, you know, this touch point 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 and 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 …
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Books, tools, and gear mentioned in this episode
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Tools
- MarieBy guest
by Dassault Systèmes
“Dassault deploys three specialized AI agents — Aura (business), Leo (engineering), Marie (science) — each reasoning through industry world models rather than general LLMs.”
- IP Lifecycle ManagementBy guest
by Dassault Systèmes
“Dassault enforces full traceability of every AI interaction through a system called IP Lifecycle Management (IPLM), logging which workflows, models, and processes modified any content.”
- LeoBy guest
by Dassault Systèmes
“Leo, for example, takes a 3D scan or 2D drawing, runs physics and kinematics analysis, and returns a manufacturable, optimized design.”
by NVIDIA
“Nemotron reasoning models improved performance for Aura, Leo, and Marie by 20% without task-specific optimization.”
by Mistral
“Dassault combines proprietary models with frontier models from NVIDIA (Nemotron via NIM) and Mistral, selecting partners based on performance and sovereignty constraints.”
- AuraBy guest
by Dassault Systèmes
“Dassault deploys three specialized AI agents — Aura (business), Leo (engineering), Marie (science) — each reasoning through industry world models rather than general LLMs.”
by NVIDIA
“Integrating NVIDIA NIM models improved document ingestion throughput by 30%, while Nemotron reasoning models improved performance for Aura, Leo, and Marie by 20% without task-specific optimization.”
“Open standards like MCP and agent-to-agent protocols enable cross-system agentic choreography without vendor lock-in.”
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