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 engineer, the proven solution.
Welcome to the NVIDIA AI Podcast.
I'm Noah Kravitz.
My guest is Nicolas Serissier.
Nicolas is Vice President of the 3D Experience Platform R&D for Tissot 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 about.
Dassault Systèmes 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 3DEXPERIENCE platform is.
Okay.
So, I'm Nicolas Solisier.
I joined Dassault Systèmes in 2004 and I'm now the vice president of 3DEXPERIENCE platform research and development.
And you have to know that the 3D Expense Platform is really the foundation for our 12 brands at Dassault Systèmes.
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 devices, therapeutics, med devices, etc.
400000 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, 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 3DEXPRESS platform, is really to transform our platform architecture into an adjuncting platform.
In fact, this is our shift from a SaaS platform, SaaS architecture, to an adjunct as a service platform 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 start with industrial world model.
Our ambition, in fact, is to build AI for industry.
It's very, very important for us.
Industry is at the core of everything we do.
And for us, AI for industry rely 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.
Let's imagine they can see a video of a plane.
They can predict if the plane will take off, if it 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, physical 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, industrial knowledge.
Here we are talking about the standards, the regulations, the processes from the different industries 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.
Right, right.
Then, the 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.
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 adjunct choreography takes place, and activating the industrial knowledge and the world representation to perform the experience-based reasoning.
But Virtual Companion now.
In fact, if the industry world model provides the intelligence, the Virtual Companion turns that intelligence into action.
What we mean with Virtual Companion is we deliver Virtual Companion are your co-worker.
They understand your intent, of course.
But they will reason with industry world models to orchestrate, execute action in context 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.
Bora, the business expert.
Leo, the engineer who solves complex engineering challenges.
And Marie, the scientist who brings 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...
You know, I think of my work in content, creating content, podcasting and writing.
And if an LLM hallucinates, then you know.
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.
And the choreography 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 IPLM IP Lifecycle Management.
And where we enforce the lineage auditability, traceability of all the interaction of AI.
So we, 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, so we provide uh, we provide the, the source of trust to understand how your virtual companion behave 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?
So NVIDIA technologies in fact infuse in every layer of our architecture, from NVIDIA AI, with AI factories for GPUs and computing infrastructure, to NVIDIA AI CUDA X libraries, Omniverse technologies to accelerate AI training, inference and simulation.
Regarding NVIDIA AI and Adjantic, 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 Systèmes.
And we are a huge fan of NIMS because it's super easy to deploy Perfect.
Always glad to hear it.
All our team are in love with this.
Awesome.
Love to hear it.
So we leverage NVIDIA Open Models for multimodality, 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 Marie have been improved by 20.
20 percent without, without specific optimization, and this is thanks to the collaboration with nvidia.
We shared our industrial use case and benchmark, and so we 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 adjunct execution, leveraging the recent announcement of AIQ, Blueprint and DeepAgent.
And we are also interested in prototyping the recent announcement of Nemo Claw, of course.
And we are exploring Dynamo to optimize the GPU optimization and Nemo agent toolkits for the optimization of our agentic workflows.
Can you speak a little bit to the partnership?
You've mentioned it as you've been talking which kind of you know how it got started and more kind of what it means to Dassault 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 Catia 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 IRE, 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 Express 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 Dassault 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 model so often?
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, of course.
Our optimized model by NVIDIA and available through NIMS which, as I said before, enables 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 about the sovereignty and the regulation constraints.
Because we operate worldwide, We have a customer in all industry and many customers in regulated or 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 also want to calibrate the model with the customer knowledge.
So we inject the industry knowledge through fine-tuning or RAG, depending on 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 agency platform to leverage third-party industrial systems and enable, in fact, interoperable or cross-system agency 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 Dassault platform.
One super cool example, I think, is Leo Mechanical Designer.
We showcased this live, this new virtual companion, in our 3D Express World Conference last February, with Jensen attending to this conference.
And so here you give Leo a 3D scan or 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 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, manufacture ready, and it will do it right the first time.
Amazing.
It's a very super example.
Yeah.
I think it really illustrates our transformation from a SaaS to an agent as a service.
Right.
And in fact, with that, we are giving to our millions of designers the power to innovate faster.
Yeah.
But it's not just about speed.
It's about reliability and trust.
And because you know that your design works, because it is born from science, from physics, and is 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?
It's really about.
In fact, with the rise of AI, we think of 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 all 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.
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.
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.
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 part from their multiple sources.
That's incredible.
So, like everything else in technology in AI now virtual twins, virtual companions, simulation 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 co-workers 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 prompted 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 adjuncts can use a 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 engineer, the proven 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.
Nicola, there's so much going on.
For listeners who want to learn more.
Want to learn more about the 3DEXPERIENCE 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?
Uh, mainly on the system website 3dscom, or on our linkedin linkedin page where we are communicating more and more on ai, thanks also to the nvidia collaboration, we are posting more and more about, about what we are doing.
So yeah, Perfect.
Yeah, that's free and connect with us.
Excellent.
Well, Nicolas.
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, incredible team.
