Qutensor's quantum-inspired compression engine brings server-grade AI to the edge — starting with real-time video intelligence, and built to power the next generation of autonomous drones, robots, and micro-computers. No cloud. No connection required.
This is our video intelligence engine running today — a live camera feed compressed, understood, and acted on entirely on-device. Zero cloud dependency, zero connectivity.
Every category we serve shares the same bottleneck: too much visual data, not enough bandwidth or power to act on it locally. We're proving the engine where that pain is sharpest today, before extending it across the rest of the platform.
Real-time compression, detection, and analysis running directly on cameras and video-capable edge hardware — no upload, no streaming costs, no round-trip.
The same compression core, extended to multi-sensor, real-time decision-making for mobile and aerial systems.
One engine, any model, any silicon — the default way AI runs outside the data center.
Edge AI has always forced an impossible trade-off: tether every device to the cloud for real inference — burning bandwidth, battery, and precious milliseconds on every decision — or strip the model down until it's too weak to matter. Qutensor removes the trade-off entirely. Our quantum-inspired algorithm restructures neural computation at the tensor level, compressing multi-GPU-class models to run natively on microcontrollers, with near-lossless accuracy and no signal required. The result: full-scale intelligence, engineered into hardware you can hold in one hand.
Bring any multi-GPU-class model — vision, language, or sensor-fusion — trained the way you already train it.
Our quantum-inspired engine restructures the tensor graph at its mathematical core — not just quantizing weights — collapsing footprint by orders of magnitude.
Ship a single runtime to Raspberry Pi, Cortex-M/A, Jetson, or custom silicon. It runs. Offline. Indefinitely.
Four capabilities. One compression engine. Zero compromises.
Zero cloud dependency, zero connectivity requirement. Models run entirely on-device — your systems keep thinking in the most remote, disconnected, or contested environments.
Full-scale AI execution on minimal silicon — Raspberry Pi, microcontrollers, and custom edge boards. If it has a processor, it can run server-class intelligence.
Compression that preserves near-lossless accuracy — inference quality that mirrors multi-GPU data center setups, without the data center.
Real-time decisions, milliseconds not round-trips. Engineered for minimal power draw — maximizing flight time, battery life, and mission duration.
Three architectures, three very different sets of constraints.
Directional positioning, not a formal benchmark. Figures vary by model, workload, and hardware — happy to walk through specifics.