The Embedded Vision Foundry
Engineering & Manufacturing Partner for Edge AI Systems.
We take AI camera products from concept to production — handling hardware design, SoC integration, firmware, and manufacturing ramp.
Built for the World's Most Ambitious AI Innovators
We serve AI startups, hardware OEMs, and product companies that need a trusted engineering partner to turn edge AI vision into shipping hardware.
AI Startups
You've validated your model. We help you productize it into a shipping AI camera platform — fast.
Hardware OEMs
You need an expert engineering partner for next-gen camera products with AI at the edge.
Global Innovators
You're building something novel. We bring the embedded systems depth to make it real.
Full-Stack Camera Hardware Engineering
We design and engineer complete AI camera platforms — from sensor selection and optics to PCB layout, enclosure, and manufacturing documentation. We don't just integrate boards; we engineer products.
- Sensor selection and characterization
- Custom PCB and carrier board design
- Optics and mechanical integration
- Thermal management for sustained inference
- EMI/EMC and environmental certifications support
- Manufacturing documentation and DFM review
Multi-Vendor SoC Expertise
We maintain deep engineering relationships and proven integration experience across the three dominant edge AI SoC platforms.
Jetson and CUDA platforms optimized for real-time vision AI inference.
Computer vision SoCs designed for AI camera platforms and embedded vision systems.
NPU-powered edge processors for efficient AI inference on embedded devices.
Take Your Model to the Edge
We migrate AI models from cloud GPU to edge NPU — preserving accuracy while dramatically reducing power, cost, and latency. We've done this across PyTorch, TensorFlow, and ONNX pipelines.
- Model quantization and pruning (INT8 / FP16)
- TensorRT and ONNX Runtime optimization
- Custom NPU kernel development (Ambarella SDK, RKNN)
- Accuracy benchmarking and regression validation
- End-to-end inference pipeline on target hardware
From Prototype to First Production Run
We support AI hardware companies through the bridge between prototype and scale. Our production capability handles runs from 10 to 5,000 units — with the engineering discipline of a manufacturing organization.
- DFM review and BOM optimization
- SMT assembly and inspection (AOI, X-ray)
- Firmware flashing and functional test
- Packaging and fulfillment
- Supply chain management for critical components
Proof in Production
Real deployments. Measured outcomes. Engineering discipline across AI camera platforms and industrial intelligence systems.
US Seed AI Camera Platform
NVIDIA + Ambarella Dual-SoC Architecture
AI Startup · United States
A US-based seed-stage AI company had a validated computer vision model running on cloud GPU and needed to productize it as a standalone edge AI camera. They had no hardware engineering team and a 6-month window to first units.
Nexilis designed a dual-SoC camera platform combining Ambarella CV72 for ISP and NVIDIA Jetson Orin NX for AI inference. We handled sensor selection, custom PCB design, thermal management, firmware, and manufacturing documentation.
First prototypes delivered in 12 weeks. Production-ready units in 22 weeks. The platform achieved real-time inference at under 15ms latency with thermal stability maintained across extended operation.
Robotics Vision Platform Stabilization
Edge Inference Optimization for Robotics Systems
Robotics OEM · Europe
A European robotics OEM was experiencing thermal throttling and inconsistent inference latency in their NVIDIA Jetson-based vision system. Field failures were occurring under sustained load conditions, with latency spikes causing robotic control issues.
Nexilis conducted a full thermal and inference audit. We redesigned the cooling architecture, migrated non-critical workloads from GPU to NPU, and optimized the TensorRT pipeline. Carrier board redesign eliminated the thermal constraint.
Latency stabilized at sub-20ms under sustained load. Thermal throttling eliminated. The client reduced field failure rate by 84% and was able to scale deployment to 3 additional product lines.