We're building a perception and simulation platform for autonomous systems — focused on generating and validating edge-case scenarios for vision models, powered by GPU-intensive multi-modal training and large-scale synthetic data generation.
Currently building core perception models and simulation pipeline.
We're not building a self-driving car. We're building the perception and simulation infrastructure that makes autonomous systems possible — the layer that generates, validates, and stress-tests vision models against scenarios that real-world data rarely captures.
Simulation before deployment. The rarest edge cases cannot be collected — they must be generated at scale.
Perception is the hard problem. Planning and control are increasingly commoditized. Making machines truly see is not.
Synthetic + real = robust. Fusion of generated and real-world data is the only path to reliable vision models.
Three interconnected systems — each focused on the perception and simulation layer, not the vehicle itself.
Multi-modal vision models trained to detect objects, classify road agents, and flag ambiguous scenes — with a focus on urban edge cases that standard datasets miss.
Procedural synthetic data generation at scale. We create the road conditions, weather states, and agent behaviours that real-world collection cannot reliably provide.
A structured library of high-difficulty scenarios — adversarial lighting, occlusion, atypical road users — used to stress-test and benchmark vision model robustness.
Our training pipeline is GPU-intensive by design. Synthetic data generation, multi-modal fusion, and distributed model training require serious compute — and we've architected for it from day one. This is what makes deep simulation possible at the scale edge-case coverage demands.
Forty thousand people die on roads every year in the United States alone. We believe every single one of those deaths is preventable — and we intend to prove it.
Early access for teams building autonomous systems and robotics platforms.
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