Introducing Saturn.
Introducing Saturn.
A Generative World Model with physics at its core for accurate and controllable simulations.
A Generative World Model with physics at its core for accurate and controllable simulations.
Our Vision
Our Vision
The world we believe in
Our mission is to empower innovators on their quest to make autonomous systems a reality. We envision a future where these systems can tackle our most dangerous and demanding tasks. A world where a team of firefighting robots can be sent into a burning inferno, derisking human endeavours. Or, they could be used for elder care and support, to transform and improve the quality of human life
Digital vs Physical World
In recent years, we have witnessed the rapid adoption of AI for natural language tasks, warping everyday routines. The success of both open and closed source natural language models can largely be attributed to access to a massive corpus of high quality data, bootstrapped by a better understanding of scaling laws.
There have been no comparable breakthroughs in physical AI yet. The complexity of the real world creates significant challenges for autonomous systems, where data collection is both costly and time-consuming. These hurdles limit the capabilities of physical AI in real-world applications. Emerging generality is only achieved by training at scale: large models trained on a wealth of data. Which is why generating high-quality synthetic data is critical.
Simulator
However, to date, no generative model has provided the necessary performance to produce long, controllable, and temporally coherent simulations. To compensate, engineers rely on classical simulators that demand expert knowledge, custom setups and long tuning cycles. When a new simulator is released, engineers must weigh the potential benefits against the setup time, which can take months. This has led to widespread "simulator fatigue" in the industry.
What we have learned
Over the years, we’ve worked with deep learning across domains like video games, biomechanics, finance, and physics. One insight stands out: success hinges on the right inductive biases. A deep understanding of the problem amplifies the impact of data, compute power, and engineering. We've previously used these biases to teach AI to trade profitably in milliseconds within the complex financial market. Now, we're leveraging them to help AI understand and simulate the physics of our world
Saturn
Saturn is developing the first world model as a virtual simulator, powered by a physics-aware generative engine. Prompt Saturn to generate and control simulations, reducing friction with your tech stack and lowering the time-to-market. Saturn takes in multimodal sensor data, state data, and text, to produce video annotated with world-state data.
Saturn aims to become the foundational simulator behind the autonomous systems that will shape our world in the coming years—powering every step from early-stage training to real-time decision-making when deployed in the real world.
Get in touch
Where?
San Francisco, California
🤖
🌍
Our Vision
The world we believe in
Our mission is to empower innovators on their quest to make autonomous systems a reality. We envision a future where these systems can tackle our most dangerous and demanding tasks. A world where a team of firefighting robots can be sent into a burning inferno, derisking human endeavours. Or, they could be used for elder care and support, to transform and improve the quality of human life
Digital vs Physical World
In recent years, we have witnessed the rapid adoption of AI for natural language tasks, warping everyday routines. The success of both open and closed source natural language models can largely be attributed to access to a massive corpus of high quality data, bootstrapped by a better understanding of scaling laws.
There have been no comparable breakthroughs in physical AI yet. The complexity of the real world creates significant challenges for autonomous systems, where data collection is both costly and time-consuming. These hurdles limit the capabilities of physical AI in real-world applications. Emerging generality is only achieved by training at scale: large models trained on a wealth of data. Which is why generating high-quality synthetic data is critical.
Simulator
However, to date, no generative model has provided the necessary performance to produce long, controllable, and temporally coherent simulations. To compensate, engineers rely on classical simulators that demand expert knowledge, custom setups and long tuning cycles. When a new simulator is released, engineers must weigh the potential benefits against the setup time, which can take months. This has led to widespread "simulator fatigue" in the industry.
What we have learned
Over the years, we’ve worked with deep learning across domains like video games, biomechanics, finance, and physics. One insight stands out: success hinges on the right inductive biases. A deep understanding of the problem amplifies the impact of data, compute power, and engineering. We've previously used these biases to teach AI to trade profitably in milliseconds within the complex financial market. Now, we're leveraging them to help AI understand and simulate the physics of our world
Saturn
Saturn is developing the first world model as a virtual simulator, powered by a physics-aware generative engine. Prompt Saturn to generate and control simulations, reducing friction with your tech stack and lowering the time-to-market. Saturn takes in multimodal sensor data, state data, and text, to produce video annotated with world-state data.
Saturn aims to become the foundational simulator behind the autonomous systems that will shape our world in the coming years—powering every step from early-stage training to real-time decision-making when deployed in the real world.