Cloud-Grade Computer Vision, on a Microcontroller.
A meta-compiler and embedded orchestrator that slashes memory use by 2.5× and doubles frame rates on low-cost MCUs — automating quantization, pre-compilation, and multi-core hardware mapping in one click.
Edge AI is bottlenecked by the last mile.
Deploying advanced computer vision onto edge microcontrollers is throttled by memory limits, low frame rates, and up to 12 person-months of engineering per model port. AI teams can't bridge cloud PyTorch to constrained C/Rust hardware without sacrificing resolution or accuracy.
Key innovations & differentiators
Meta-compiler optimization
2.5× memory reduction and 2× higher FPS for the same model and hardware — no accuracy loss. Unlocks 640×640 Ultralytics YOLO on low-power MCUs with ARM Ethos NPUs.
One-click hardware export
Automates pre-compilation, quantization, and backend compiler handoffs — replacing months of manual tuning with a single automated toolchain.
Embedded orchestrator
An on-chip execution layer that coordinates complex vision pipelines in real time across heterogeneous silicon — multiple ARM CPUs plus NPU or proprietary accelerators.
Universal developer access
Native C and Rust APIs for embedded teams. Direct Python deployment for AI teams. A no-code visual workflow for domain experts — all targeting the same silicon.
2.5× less memory. 2× more frames. +10% mAP. Same silicon, same accuracy — measured against LiteRT baseline.
Built for the teams shipping vision at the edge.
Embedded software engineers, AI/ML developers, and enterprise vision-device manufacturers across robotics, smart cities, industrial automation, and security.
- Embedded teams — native C & Rust APIs
- AI teams — one-click Python to silicon
- Domain experts — visual no-code pipelines
Ready to see cloud-grade vision run on a $5 MCU?
Talk to our team about early access, benchmark data, and live demo scheduling.