
Edge AI Vision
Edge AI Vision Systems for Automotive and Consumer Applications
Edge AI: Transforming Vision at the Source
The Power of Computer Vision in Edge AI

Real-Time Intelligence at the Edge: Smarter Cameras, Safer Systems
AI-powered vision is transforming how devices see and respond to the world — from smart doorbells and wearables to ADAS modules and driver monitoring systems. But to meet the demands of real-time processing, privacy, and bandwidth efficiency, computer vision must move from the cloud to the edge.
At Promwad, we design and integrate embedded AI vision systems that run inference locally, process video at low power, and deliver actionable insights without relying on remote servers. Real-time data processing enables instant analysis and response, critical for applications that require immediate feedback. These capabilities support real-time decision making in autonomous vehicles and safety systems, where rapid, localised processing is essential.


Our solutions demonstrate how different technologies work together, combining advanced hardware, AI algorithms, and robust software to deliver reliable and scalable edge AI vision systems. Our full-stack development spans from custom camera boards and image pipelines to deep learning models optimised for edge inference.
What We Offer
Hardware & Camera Subsystem Design
- Custom camera modules (CSI, MIPI, USB, parallel)
- ISP tuning and integration for image clarity and low-light performance
- Multi-camera synchronisation for surround view or stereoscopic vision
- Optics and enclosure design for harsh or compact environments
- Support for vehicle design through advanced simulation and testing
Embedded AI Model Deployment
- CNN/DNN model porting and optimisation (YOLO, MobileNet, etc.)
- TensorRT, ONNX Runtime, OpenVINO, and TVM deployment pipelines
- Model quantisation and pruning for real-time performance
- Post-training calibration and device-specific acceleration (GPU/NPU)
- AI model optimisation for efficient, real-time edge processing in automotive applications
Edge Platform Integration
- NVIDIA Jetson (Nano, Xavier, Orin), NXP i.MX 8, Rockchip RK3588, Intel Movidius
- Integration with Linux, Yocto, or Android-based platforms
- Custom BSPs and hardware bring-up
- Interface support: HDMI, Ethernet, CAN, GPIO, USB, SPI
- Integration with vehicle systems for design optimisation and predictive maintenance
System Software & Application Layer
- Real-time face, object, gesture, or lane detection
- Frame capture, buffer handling, and multi-threaded pipelines
- Event-based triggers, alerts, and telemetry
- Secure local storage or encrypted transmission to cloud if required
- Support for voice commands to enable in-vehicle interaction and personalisation
- Threat detection using localised data processing for enhanced security
Applications We Enable
Mobility & Urban
Traffic analytics, scooter/bike safety systems
Smart Home
AI cameras, smart doorbells, motion detection
Retail & Access
Smart kiosks, facial recognition, people counting
Consumer Devices
Wearables, fitness tracking, mobile vision
Automotive
DMS (Driver Monitoring Systems) that analyse driver behaviour to personalise in-car experiences and improve safety, ADAS, blind spot detection with advanced systems enhancing safety, occupant detection with enhanced safety for all vehicle occupants
Why Promwad
Expertise in both camera electronics and embedded AI deployment
End-to-end development: from optics and boards to software and AI
In-house teams for BSP, vision algorithms, and NPU acceleration
Flexible models: turnkey projects or integration into your team
Proven experience in automotive, consumer, and smart city sectors
Under the Hood: AI Models and Algorithms for Vision at the Edge
The effectiveness of Edge AI in automotive applications depends on the power and efficiency of its underlying AI models and algorithms. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are trained on vast amounts of visual data to enable accurate object detection, tracking, and classification. These models are optimised for edge deployment, ensuring they can deliver real-time insights without overwhelming local hardware.
Let's Build Your Intelligent Computer Vision System

Let’s bring intelligent vision to the edge — and your next product!
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