Edge vision & AI boxes
Rockchip, NVIDIA Jetson and Hailo edge boxes, smart cameras and GMSL camera systems for retail analytics and industrial inspection.
Edge-vision products fail in the field for hardware reasons: under-specified compute, thermal throttling in sealed boxes, camera links that drop frames, and no plan for deployment, updates and support. Software teams discover these after the pilot is signed.
- Compute selection with numbers: model-hardware fit across RK3576, RK3588, Jetson Orin and Hailo-class accelerators.
- Camera and link design: PoE IP cameras, USB, MIPI and GMSL2/3 multi-camera boxes with hardware sync.
- Enclosure and thermal design for sealed, fanless deployment.
- Privacy-preserving on-device analytics architectures — no identifying data leaving the camera.
- Pilot builds, store and factory deployment support, and small-batch production.
What we write into the specification.
| Inference | Measured FPS and latency for your model on the target board — not TOPS |
| Thermals | Steady-state temperature in the sealed enclosure at ambient max |
| Camera link | Sustained streams with zero dropped frames over the deployment shift |
| Fleet | Remote update path, health telemetry and recovery from power loss |
Devices and parts we track for this practice
Case studies for this practice are being prepared. Ask us for references.
Rockchip or Jetson for an edge AI box?
Rockchip RK3576/RK3588 wins on cost and power for mainstream detection models with a 6 TOPS NPU; Jetson Orin wins when you need CUDA, larger models, or GMSL camera ecosystems. We benchmark your actual model on both before recommending.
Building hardware that has to learn? Talk to us.
Send a spec, a sketch, or just the problem. We reply within 24 hours (HKT), usually the same day.