Your AI runs offline.On your hardware.Without ever sending data.

EdgeAI deploys your ML models directly to Raspberry Pi, NVIDIA Jetson and microcontrollers. Local inference, full sovereignty, network-independent availability.

inference latency
< 10ms
offline guaranteed
100%
smaller model size
75%
PoC delivered
48h

Orders of magnitude the installation aims for; they are measured on your premises during the audit, on your volumes.

EdgeAI, Offer illustration
EdgeAI. On-premise embedded AI: Raspberry Pi, Jetson, STM32
01What the agent does

Fully automated, nothing to manage.

01

INT8 / FP16 quantization

Up to 75% model size reduction with no precision loss. Supports PyTorch, TensorFlow and ONNX. Built-in CI/CD pipeline.

02

Universal multi-target export

One pipeline for Raspberry Pi, NVIDIA Jetson, STM32 and ESP32. OTA deployment across device fleets with zero service interruption.

03

Data sovereignty

Zero data transmitted off-site. 100% local inference, GDPR and industrial-grade compliant. Full availability even with no network.

02How it works

Live in days, not months.

  1. 01

    Send your hardware architecture

    Specify your targets (Jetson, RPi, STM32...) and current model. Our team evaluates feasibility in under 24h.

  2. 02

    Optimization and quantization

    EdgeAI compresses, quantizes and exports your model in the optimal format for each target. Performance tests included.

  3. 03

    Deployment and monitoring

    OTA rollout to your fleet. Real-time performance metrics. One-command rollback if needed.

03Under the hood

The pipeline behind this offer.

Predictive & IoTOpen-source ML, edge & self-hosted, no cloud required, 6 stages
Sensors
MQTTOPC-UA
Ingestion
Streaming
KafkaFlink
Time series
TS store
TimescaleDBPostgreSQL
Local training
ML model
ProphetXGBoostPyTorch
Forecast 7-30d
Prediction
PanneChurn
Alert
Action
n8nSlackSMS
04Tools we operate

We plug the agent into the tools you already use.

  • Tensorflow
  • Pytorch
  • Onnx
  • Raspberrypi
  • Nvidia

Nothing to learn: we configure and operate these connections for you.

Why now

The cloud has no business on your production line.

What it costs you today

Tuesday, 6:12 AM. The factory starts. Your quality control cameras on line 3 wait for an AWS Frankfurt cloud response to validate each part. Average latency: 340ms. On 12,000 parts/day, that's 68 minutes of lost throughput. Worse: at 2 PM the VPN drops for 8 minutes. The line stops. 1,600 parts ship uninspected. The quality lead tells you that evening: 'If we lose the network again, the German customer audits the whole batch and bills returns.' And at the ESG committee, they ask why your production data flows through AWS US, when you're a defense subcontractor.

The facts

IDC predicts 75% of enterprise data will be processed at the edge by 2027 (vs 10% in 2018). Gartner measures that edge AI inference cuts latency by 30x, cloud costs by 7x and lifts availability from 99.5% to 99.99%, 52 minutes of annual downtime instead of 43 hours. GDPR, NIS2 and industrial sovereignty requirements now make edge not optional but a regulatory obligation for 41% of B2B AI deployments.

Why us

Wikolabs installs AI systems built on open-source models, on your premises, paid once. No black box: your data, your prompts and your history stay in your infrastructure. We quote at a fixed price or on time and materials, never per ticket or per token, and we say before you sign what works and what does not yet.

What we put in place

Concretely: you send your hardware architecture, EdgeAI quantizes your model (INT8/FP16) with up to 75% size reduction at no precision loss, exports for Jetson/RPi/STM32/ESP32 and OTA-deploys to your fleet. The outcome: PoC in 48h, latency < 10ms, 100% offline, GDPR compliance guaranteed. Your production line no longer depends on AWS Frankfurt or a flaky VPN.

IoT & Edge AI

Artificial intelligence directly on your equipment, without cloud

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.

How we deploy

  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.

Concrete benefits

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).

Frequently asked questions

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.

Your model runs offline in 48h

Send us your hardware architecture. PoC in 48h. Zero cloud dependency. No commitment.

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