Bringing AI Intelligence to the Edge
Gemperts specialises in deploying AI models directly on edge devices — cameras, sensors, microcontrollers, and embedded systems — eliminating cloud dependency and enabling real-time, offline-capable intelligence. Our Edge AI practice covers the full model compression and deployment pipeline, from knowledge distillation and quantisation to hardware-specific optimisation using TensorRT, ONNX Runtime, OpenVINO, and TensorFlow Lite. We enable manufacturers, fleet operators, healthcare device makers, and smart city builders to run powerful AI where data is generated, not in a distant cloud.
Edge AI Services We Offer
Model Optimisation for Edge Devices
Compressing large AI models through pruning, quantisation, and knowledge distillation to run efficiently on CPUs, NPUs, and microcontrollers with strict memory budgets.
Deep Learning Model Porting
Porting trained models from PyTorch and TensorFlow to deployment-ready formats for target hardware — ensuring accuracy, latency, and power consumption targets are met.
TensorRT, ONNX RT, OpenVINO, TF Lite
Expert deployment using leading inference runtimes — optimising for NVIDIA GPUs, Intel VPUs, ARM processors, and mobile SoCs across diverse hardware platforms.
Federated Learning
Privacy-preserving distributed training that enables AI models to learn from device-local data without centralising sensitive information — ideal for healthcare and finance.
IoT & Embedded AI Integration
End-to-end integration of AI inference into IoT devices and embedded systems — from firmware development to secure OTA model update pipelines.
Edge MLOps & Model Management
Automated pipelines for monitoring edge model performance, triggering retraining on data drift, and deploying updated models to thousands of distributed devices.
Our Edge AI Engineering Process
Hardware & Constraint Analysis
We assess target hardware capabilities — compute, memory, power budget, thermal envelope — to define the model compression and runtime strategy.
Model Compression & Quantisation
We apply state-of-the-art compression techniques — INT8/FP16 quantisation, structured pruning, layer fusion — to reduce model size without accuracy loss.
Runtime Optimisation & Benchmarking
We profile inference performance on real hardware, iterate on runtime optimisations, and benchmark against latency, throughput, and accuracy targets.
Fleet Deployment & OTA Updates
We establish secure deployment pipelines that push model updates across distributed edge device fleets with rollback and versioning controls.
WHY GEMPERTS
Why Choose Our Edge AI Services
INDUSTRY IMPACT
Edge AI Across Industries
80%
Manufacturing
Reduction in inference latency for real-time defect detection by moving from cloud to on-device AI.
60%
Automotive
Improvement in ADAS reaction time with low-latency edge-deployed perception models.
50%
Healthcare
Reduction in diagnostic data upload costs by processing medical images on local hospital hardware.
45%
Retail
Improvement in smart shelf analytics accuracy using edge-based computer vision without cloud dependency.
70%
Energy
Reduction in unplanned downtime through predictive maintenance AI running on industrial IoT gateways.
55%
Smart Cities
Bandwidth savings in traffic management systems by processing camera feeds locally at the edge.
OUR REACH
Industries We Serve
Delivering intelligent technology solutions across 12+ verticals — from healthcare to hospitality.
Healthcare and Life Sciences
Retail and E-commerce
Logistics
Banking and Finance
Media and Publishing
Education
Real Estate
Automotive
Manufacturing
Telecom
Energy and Utilities
