EN DE
Free Consultation

Overview

Edge AI Solutions

Sending every frame of video or every sensor reading to the cloud is expensive, slow, and — for many German manufacturers bound by strict data protection rules — simply not viable. Edge AI moves the intelligence onto the device itself, so decisions happen in milliseconds, bandwidth stays low, and sensitive data never has to leave the factory floor unless you want it to.

We design and optimize AI models to run directly on constrained hardware: NPUs, GPUs, FPGAs, and low-power microcontrollers. That means quantization, pruning, and hardware-specific compilation, not just dropping a cloud model onto an embedded board and hoping it fits.

Technical Approach

Our work starts with model optimization: quantization, pruning, and knowledge distillation to shrink a model so it runs within the memory and power budget of an edge device, whether that is an NVIDIA Jetson, a Qualcomm QCS module, or a microcontroller with a dedicated neural processing unit. We deploy through runtimes such as TensorRT, ONNX Runtime, and TensorFlow Lite, and we build the surrounding pipeline, sensor fusion, pre processing, and post processing, so the model is one working part of a reliable system rather than an isolated demo.

Processing data where it is generated cuts bandwidth costs, reduces latency from seconds to milliseconds, and keeps sensitive data on site, which matters a great deal to German manufacturers who are cautious about sending production data to third party cloud services. Combined with the country’s mature 5G rollout, Edge AI lets factories run analytics locally while still syncing insights to central systems when it makes sense.

Many industrial Edge AI applications, quality inspection tied to safety or predictive maintenance on critical machinery, are likely to be classified as high risk systems under the EU AI Act. Following the recent AI Omnibus agreement, obligations for standalone high risk systems now apply from 2 December 2027, while AI embedded in regulated products such as machinery follows a separate timeline extending to 2 August 2028. We design Edge AI deployments with the documentation, human oversight, and risk management structure the AI Act expects, and because inference happens on device, less personal data ever needs to leave your site in the first place, which also supports GDPR data minimization.

What we deliver

  • Model optimization and quantization for NPU, GPU, and MCU targets
  • Computer vision at the edge: defect detection, quality inspection, object tracking
  • Predictive maintenance models running locally on production equipment
  • TinyML deployments for battery-powered and space-constrained devices
  • Edge-cloud hybrid architectures for when local inference and central analytics both matter

If latency, bandwidth, or data residency is holding your AI roadmap back, edge inference is usually the answer.

Let's Talk