Simulate the human brain, by shifting from traditional AI labels to generic representations. Information is indexed and compressed into neural responses, with efficient data processing.
The neural network algorithms are self-learned in a dynamic environment, independent from manually labelled data and immune against data biases.
Brain-like, scenario-focused contextual adaptivity. A sparse set of resources are applied during processing, translating into superior performance and higher efficiency.
Extending perception to forces, capturing real-world interactions and dynamics. Closing the gap between perception and action, the input immediately propels the vehicle's behavior.
Routing sensor input to a handful of specialized narrow AI agents, by context.
Our ADAS solutions passed safety regulation tests with superior performance in edge cases
10x less energy consumption
Better performance at up to
40% less cost
Understands the surrounding environment and adapts in real time based on context