Artificial intelligence doesn't have to live in the cloud. Edge AI runs machine learning models directly on small devices — sensors, microcontrollers, embedded systems — where the data is generated. No internet required.
Edge AI means running artificial intelligence algorithms directly on hardware devices at the "edge" of the network, rather than sending data to a remote server for processing. Instead of a powerful GPU in a data center, the AI runs on a small, low-power chip costing a few euros — right next to the sensor that collects the data. The model analyzes data in real time, makes decisions locally, and only sends alerts or results when needed.
Sensitive data (vibrations, health signals, production metrics) stays on the device. Nothing is transmitted, nothing can be intercepted. Full GDPR compliance by design.
Decisions in under 1 millisecond. Critical for detecting machine failures, quality defects, or safety hazards before damage occurs. No network delay, no server queues.
No cloud subscriptions. No bandwidth costs. Hardware costs under 10 euros per sensor node. Scale to thousands of devices without scaling your cloud bill.
Edge AI is already transforming industries where real-time, on-device intelligence makes a measurable difference.
Vibration sensors on motors and pumps detect anomalies before breakdowns. Avoid unplanned downtime costing thousands per hour.
Inline sensors detect defective products on the production line in real time. Reject bad units instantly, reduce waste.
Soil and climate sensors make autonomous decisions about irrigation and fertilization. Works in remote fields with no connectivity.
Current and power sensors detect energy waste, anomalous consumption patterns, and equipment degradation in real time.
Occupancy and environmental sensors optimize HVAC, lighting, and security systems locally. Privacy-first, no cameras needed.
Accelerometers and biosensors on wearable devices detect falls, arrhythmias, or activity changes. Instant response, no cloud dependency.
You don't need to be an AI expert. Luviner handles the complexity so you can focus on your domain.
CSV sensor data
Neural network
Pure C code
On your MCU
No. Luviner automates the entire pipeline from data to deployment. You provide sensor data in CSV format, choose your target hardware, and Luviner does the rest — training, optimization, and code generation.
Any microcontroller with a few kilobytes of Flash and RAM. Common targets include ARM Cortex-M0/M4/M7, ESP32, RISC-V chips, and Nordic nRF series. No GPU, no OS required.
Luviner models achieve up to 98.2% accuracy on standard benchmark datasets while using only 7-32 KB of Flash memory. The key is efficient neural network architectures designed specifically for constrained devices.
If you have sensors collecting data (vibration, temperature, current, motion, sound) and need real-time decisions without cloud dependency, Edge AI is likely a fit. Manufacturing, energy, agriculture, and logistics are the most common sectors.
Models can be updated via firmware OTA or by retraining with new data on the Luviner platform. The few-shot adaptation feature also allows on-device learning with as few as 50 samples, without cloud connectivity.
Start with your sensor data. Get a running model in minutes, not months.