Edge AI on Embedded Devices

Artificial intelligence directly on your equipment, without cloud

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Category
Support and customer service
Model
open, installed on your premises
Data
never leaves
Subscription
€0 per month
01Overview

AI doesn't have to live only in the cloud. For industrial applications requiring ultra-low latency, data privacy or intermittent connectivity, inference must happen locally, on the device. Wikolabs deploys AI models optimized to run directly on Raspberry Pi, NVIDIA Jetson, STM32 or your proprietary equipment, with zero network dependency.

The problem

Cloud inference involves 100–500ms latency incompatible with real-time applications. Cloud infrastructure costs accumulate at scale. Sensitive data (production images, health data) can't transit through the cloud. And without permanent network connectivity, cloud applications are fragile.

Our answer

We optimize your AI models (INT8 quantization, pruning, distillation) to fit the memory and CPU constraints of edge devices. The model is then converted to TFLite, ONNX or TensorRT, integrated into a C++/Python firmware and deployed on your equipment. Performance is validated on real hardware before delivery.

02How it works

Live in four steps.

  1. 01
    Target hardware selection

    Analysis of your constraints (power, consumption, cost, form factor) and optimal hardware selection: Raspberry Pi, Jetson Nano, Coral TPU, STM32.

  2. 02
    Model optimization

    Quantization (INT8/FP16), pruning, knowledge distillation to reduce size and accelerate inference while maintaining accuracy.

  3. 03
    Firmware integration

    Inference pipeline development in C++ or Python. Integration with inputs (camera, sensors) and outputs (GPIO, display, network).

  4. 04
    Validation & deployment

    Performance testing on real hardware (latency, consumption, accuracy). Over-the-air (OTA) deployment for updates.

03What you gain

Results you can measure.

Latency < 50ms

Local inference eliminates network latency. Decisions are made in real time, essential for control and safety applications.

Zero cloud inference cost

Once deployed on the device, each inference is free. For millions of inferences per day, the savings are considerable.

100% local data

No data leaves the device. Simplified GDPR compliance for applications processing sensitive data (health, industry, defense).

04Frequently asked

What we get asked before signing.

Which models can be embedded?
Classification models, object detection (lightweight version), lightweight NLP, anomaly detection. Model size depends on available device memory.
Does quantization degrade accuracy?
Generally less than 2% accuracy loss with well-executed INT8 quantization. We systematically validate on your real data.
How to update the model on deployed devices?
We set up an OTA (Over-The-Air) pipeline to deploy new models without physical intervention on equipment.
Is it compatible with very constrained microcontrollers (Arduino, STM32)?
Yes for very simple models via TensorFlow Lite Micro. For more complex models, Raspberry Pi Zero or Coral USB Accelerator are used.

Let's talk about your case for thirty minutes.

A conversation to understand your context, volumes and tools. You leave with a scope and an order of magnitude, no commitment.

WhatsApp
+261 38 66 261 00team@wikolabs.com