What is Edge AI and why does it matter?
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. With temporal neural networks, devices don't just see values: they understand how signals change over time. No internet required.
What our engine delivers.
Validated on UCI HAR public dataset — 95.0% accuracy on the standard benchmark (official split, 561 pre-computed features, 2,947 test samples). With raw 9-axis IMU data: 88% accuracy. Competitive with CNN and LSTM, deployable on any MCU.
AI that runs where the data lives.
Edge AI means running artificial intelligence directly on hardware devices at the "edge" of the network, rather than sending data to a remote server. 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. With Luviner's temporal approach, the model doesn't just read values: it understands trends, detects gradual changes, and identifies anomalies that simple threshold rules would miss. All in real time, all on-device.
Two approaches, very different tradeoffs.
Cloud AI
- Requires constant internet connection
- Latency: 100ms to seconds (round trip)
- Ongoing cloud costs (pay per inference)
- Sensitive data leaves the device
- Doesn't work offline or in remote areas
Edge AI
- Works completely offline
- Latency: under 1 millisecond
- Zero recurring costs after deployment
- Data never leaves the device
- Works anywhere — factory floor, field, deep sea
Why a neural network instead of if/else logic?
Simple threshold rules work for obvious, single-variable conditions. But real-world anomalies are rarely that simple.
Threshold Rules
- See one value at a time — no temporal context
- Miss gradual degradation and slow drifts
- Can't detect unusual combinations of normal-looking values
- Must be rewritten for every new machine or condition
- Require domain experts to define every possible failure mode
Luviner Neural Network
- Understands how signals evolve over time
- Detects slow trends and gradual degradation automatically
- Learns complex multi-signal patterns from data
- Adapts to new conditions — retrain in minutes, no rule rewriting
- Finds anomalies you didn't know to look for
Three reasons companies are moving AI to the edge.
Privacy & Security
Sensitive data (vibrations, health signals, production metrics) stays on the device. Nothing is transmitted, nothing can be intercepted. Full GDPR compliance by design.
Real-time Speed
Decisions in under 1 millisecond. Critical for detecting machine failures, quality defects, or safety hazards before damage occurs. No network delay, no server queues.
Cost Efficiency
No cloud subscriptions. No bandwidth costs. Hardware costs under 10 euros per sensor node. Scale to thousands of devices without scaling your cloud bill.
Where is Edge AI used today?
Edge AI is already transforming industries where real-time, on-device intelligence makes a measurable difference.
Predictive Maintenance
Vibration sensors on motors and pumps detect anomalies before breakdowns. Avoid unplanned downtime costing thousands per hour.
Quality Control
Inline sensors detect defective products on the production line in real time. Reject bad units instantly, reduce waste.
Smart Agriculture
Soil and climate sensors make autonomous decisions about irrigation and fertilization. Works in remote fields with no connectivity.
Energy Monitoring
Current and power sensors detect energy waste, anomalous consumption patterns, and equipment degradation in real time.
Building Automation
Occupancy and environmental sensors optimize HVAC, lighting, and security systems locally. Privacy-first, no cameras needed.
Wearables & Health
Accelerometers and biosensors on wearable devices detect falls, arrhythmias, or activity changes. Instant response, no cloud dependency.
From data to device in 4 steps.
You don't need to be an AI expert. Luviner handles the complexity so you can focus on your domain.
Upload
CSV sensor data
Train
Neural network
Export
Pure C code
Deploy
On your MCU
From CSV to compiled model in seconds
Upload your sensor data, train a neural network, and get a compiled binary ready for your microcontroller. Here's what the engine output looks like:
Beyond classification.
Built-in tools for the hardest real-world deployment challenges — from anomaly detection to automatic hardware optimization.
Anomaly Detection
Your sensor learns what "normal" looks like and detects faults automatically — without any labeled fault data. The AI identifies WHICH sensor caused the anomaly and tells you HOW SURE it is. Low confidence? It flags for manual review instead of triggering a false alarm.
AutoML for MCU
Specify your chip and memory constraints. Luviner automatically finds the neural network architecture that maximizes accuracy within your hardware budget.
On-Device Adaptation
Deployed models improve with ~50 new samples, directly on the chip. No retraining from scratch. No cloud connection needed.
Model Compression
Need to fit on a tiny chip? Luviner transfers knowledge from a large model to a compact one — achieving accuracy impossible with direct training alone.
Drift Detection
Your device monitors incoming data and signals when it no longer matches the training distribution. Triggers retraining alerts automatically — no cloud needed.
Brain-Like Efficiency
Your device anticipates the next sensor reading, activates only the neurons that matter, and sleeps the rest. Anomalies are detected instantly as prediction failures. The model continuously improves on-device — no cloud, no retraining, no labeled data.
Predictive Maintenance
The AI doesn't just detect faults — it predicts them before they happen. Simulates future sensor behavior and alerts you hours or days in advance. Install, wait 5 minutes, and get zero-config anomaly detection with failure forecasting.
See it in action.
Real-time motor fault detection running on a simulated ESP32. Click Play to watch.
Interactive simulation — runs directly in your browser, no install needed. Takes ~30 seconds.
Common questions about Edge AI.
Do I need machine learning expertise?
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.
What hardware can Edge AI run on?
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.
How accurate are models on tiny hardware?
Luviner models achieve up to 99.6% accuracy on industrial benchmarks (CWRU) and 96% average on sklearn datasets, while using only 7-65 KB of Flash memory. The key is efficient neural network architectures designed specifically for constrained devices.
Is Edge AI suitable for my company?
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.
What about model updates?
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.
Why not just use simple threshold rules (if temperature > 80, alarm)?
Threshold rules only see one value at a time and can't understand context. A real anomaly is often a combination of signals changing together over time — something no set of manual rules can fully capture. Luviner's neural network learns what 'normal' looks like from your data, understands temporal patterns, and detects novel anomalies automatically — including ones you've never seen before. It also adapts to new conditions without rewriting rules.
Ready to bring AI to your devices?
Start with your sensor data. Get a running model in minutes, not months.