Our AI architecture leverages Factored Embodied AI, Deep Teaching™, and Semantic Simulation to achieve production-ready systems with orders-of-magnitude less data.
We build autonomous driving systems capable of high-end Level 2+ today, while utilizing the exact same software architecture to unlock Level 3 and Level 4 capabilities as roadmaps evolve.
Our architecture is built on a "factored" approach that separates Perception (seeing the world) from Policy (deciding how to drive). By extracting the geometric structure of the world before teaching a vehicle how to drive, we replicate the human ability to generalize across new environments.
The Triple Dividend of Factored Approach:
Deep Teaching™ is our proprietary unsupervised learning method that enables the training of large-scale foundation models on massive volumes of raw, unlabeled real-world driving data, overcoming the "data wall" that limits traditional autonomous systems.
The advantages of Deep Teaching™:
To bridge the "sim-to-real" gap, we train our AI planner directly in Semantic Space. Because our perception engine already converts the world into clean geometric representations, we skip the heavy computational lift of rendering photorealistic pixels for policy training.
In this geometric view, the visual "reality gap" vanishes—a simulated lane line is mathematically identical to a real one. This allows us to train on infinite scenarios at warp speed with orders-of-magnitude less real-world driving data.
Our World Model moves beyond seeing the world to anticipating it. By understanding the "laws of physics" and human intent, it closes the loop between perception and action.
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