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Proprietary AI Engines — One Platform

We didn’t build a tool.
We found something.

Proprietary neural engines. They learn without a cloud, self-organize, and survive destruction. From microcontroller to production server. Up to 99.6% accuracy, models under 20 KB.

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The Paradigm

For a decade, AI has meant one thing: massive models, in the cloud, requiring GPUs, connectivity, and dependency. But the real world is made of millions of distributed, autonomous devices with no server in sight. What was needed wasn’t a better tool — it was a different principle.

The Technology
13 specialized engines. One workflow. Verifiable results.

Each engine is optimized for a signal type. Tested on industry-standard datasets. 399 automated tests.

13 neural architectures

Each engine is designed for a signal type: vibration, current, temperature, audio, time series. AutoML selects the best one for your data.

Industry dataset benchmarks

Tested on CWRU bearing fault detection, UCI HAR, NASA TSAD, and standard sklearn datasets. Published and reproducible results.

From CSV to C code

Upload data, train the model, export C code ready for microcontroller. No ML expertise required.

Local inference

No cloud dependency. The model runs where data matters — on the sensor, on the gateway, on the production line.

Mesh architecture

Independent nodes that collaborate. No single point of failure. Native horizontal scaling.

399 automated tests

Comprehensive test suite across every engine and component. Every release is automatically verified before deployment.

13 engines. Industry datasets. Exportable C code. All verifiable.

399 automated tests. Reproducible benchmarks on public datasets.

Vivi
ViviThe Engine Family
A family of engines. The right AI for every task.

Base engines included in every plan. Premium engines for advanced workloads. AutoML tests them all against your data and hardware, then delivers the best one.

One engine per problem

Vibrations, audio, IMU, time series — each of the 13 engines is optimized for a specific signal type. AutoML picks the best one automatically.

From 19 KB to 140 KB Flash

The lightest engine fits on a Cortex-M0. The most accurate achieves 99.6% on bearing fault detection. Choose your trade-off.

Pure NumPy, zero frameworks

No TensorFlow, no PyTorch, no ONNX runtime. Train on your laptop, deploy on a microcontroller. The generated C code has zero dependencies.

13
Specialized engines
99.6%
Max accuracy
<11KB
Smallest model
12+
MCU profiles
6
Industry benchmarks
0
External dependencies

See all 13 engines →

Products
Three products. One engine family. One platform.
The same science, three doors into three different markets.
Vivi

Vivi Engines

The Foundation

Proprietary engines behind Edge AI, Lynx, and Ward. AutoML picks the best one for your use case.

Ward

Available now

Zero-config bot detection for APIs and websites. Learns normal traffic patterns, blocks bots in 0.23ms. Deploy with one Docker command.

Bot Detection Zero Config On-Premise

Lynx Monitoring

Available now

Self-learning monitoring agent for servers and Docker containers. Detects anomalies in real time without manual thresholds.

Anomaly Detection Docker Mesh Consensus

Edge AI

Available now

Ultra-compact neural networks for IoT devices. Train with CSV, deploy as C code on ARM Cortex, ESP32, RISC-V.

MCU <11KB Flash 95%+ Accuracy
The Practical Problem
Deploying AI where it matters is still too hard

ML on microcontrollers is a dependency nightmare

TensorFlow Lite needs a Python toolchain, CMSIS-NN headers, and a week of integration work — before you’ve written a single line of inference logic.

Monitoring tools don’t learn — they threshold

You set 200 manual thresholds. Half fire false positives at 3am. The real anomaly — the slow memory leak before the crash — gets buried.

The alternatives cost too much for what they do

A dedicated ML engineer costs $100K+/year. Datadog runs $23/server/month. Building in-house takes 3–6 months. You ship a product, not an AI team.

Mesh Intelligence
MeshCross-Product
Mesh Intelligence: your sensors collaborate.

Sensors share neural states and collaborate — no cloud, no central server. Byzantine-tolerant consensus, self-healing, on-field learning. Works across all Luviner products.

+14.5%
accuracy vs solo nodes
24B
per message
€2
per node
0
cloud dependencies
399
tests passing
4
topologies supported

The mesh works fully offline. Optionally, one node acts as a gateway and forwards alerts to your dashboard via WiFi or LoRa — only results, never raw data.

✓ Tamper-resistant: compromised nodes are excluded automatically
✓ Self-healing: the network reconfigures when a node fails
✓ Multi-hop: information reaches the entire network
✓ Smart sharing: each node learns what information matters
✓ On-field learning: the swarm improves without retraining
✓ MCU-ready: all features compile to pure C firmware automatically
6 ENTERPRISE FEATURES
Neural Consciousness
AI That Explains Its Decisions

Every anomaly detection comes with a complete explanation: which neurons fired, why, and what to do. Self-healing engines that monitor their own health and signal when they need retraining.

48
Neurons — all visible
100%
Decisions traceable
€2
Chip cost
0
Cloud required
50
Tests passing
<11KB
Consciousness on MCU
✓ Causal tracing: know exactly which neurons caused every alert
✓ Self-healing: the engine detects its own degradation and signals when to retrain
✓ State space visualization: see the AI brain thinking in real-time
✓ Concept drift detection: automatic detection when the world changes
✓ EU AI Act compliant: full audit trail for every decision
✓ Runs on MCU: explainability on a 2 EUR chip, zero cloud
✓ Automated actions: anomaly score triggers alerts, maintenance, or shutdown automatically
✓ Input protection: rejects corrupted sensor data before it reaches the engine
✓ Neural debugger: step through neurons one by one, set breakpoints, replay events
✓ Threat memory: remembers past dangerous patterns and reacts instantly when they reappear
✓ Long-term memory: recalls similar events from weeks or months ago with context and outcome
✓ Context-aware: automatically adjusts sensitivity for night shifts, maintenance windows, or post-incident
EXPLAINABLE AI ON EDGE
Multimodal Intelligence
Multiple Sensors, One Brain

Combine vibration, temperature, current, audio, and any sensor type into a single AI model that runs on a microcontroller. Each sensor gets its own encoder. Cross-modal attention finds correlations human operators miss.

3+
Sensor modalities (expandable)
100%
Fault detection rate
<2KB
Multimodal overhead (Q8)
2%
False alarm rate
13
Compatible engines
O(n)
Fusion complexity
✓ Fuse vibration + temperature + current + any sensor in one model
✓ Per-modality root cause: instantly know which sensor detected the fault
✓ Sensor failure tolerance: the model keeps working when a sensor goes offline
✓ Cross-modal attention: finds correlations between different sensor types automatically
✓ Per-modality encoders: FFT for vibration, statistics for temperature, passthrough for digital signals
✓ Distributed multimodal: one mesh node per sensor, consensus across modalities
✓ Real-time streaming: continuous multimodal inference with persistent neural state
✓ C-exportable: multimodal layer adds less than 2 KB to the firmware
MULTIMODAL AI ON EDGE
New Capabilities
Audio, Motion, and Privacy-Preserving Learning

Three new sensor modalities. All running on commodity microcontrollers. All compiling to pure C under 60 KB.

Audio Classification

<60 KB flash

Classify sounds on a $2 chip. Mel spectrogram processing, real-time streaming, 13 engines available. Industrial sound monitoring, machine health via audio, environmental classification.

✓ Industrial fan anomaly detection: AUC 0.749 (DCASE 2020)
✓ Real-time streaming — classifies as audio arrives
✓ All 13 Vivi engines support audio input

Gesture & Activity Recognition

95% UCI HAR

Classify gestures and activities from a 6-axis IMU. 51 features extracted automatically. 95% accuracy on the UCI HAR benchmark. Wearables, robotics, gesture control.

✓ 6-axis IMU: accelerometer + gyroscope, 51 features
✓ 95.0% accuracy on UCI HAR benchmark (6 activities, official split)
✓ Under 20 KB flash — runs on ARM Cortex-M0

Federated Learning with Privacy

Zero raw data shared

Train models across distributed devices without sharing raw data. Each device learns locally, shares only model updates. Privacy-preserving by design.

✓ FedAvg and FedProx aggregation strategies
✓ Gaussian differential privacy — configurable epsilon
✓ Byzantine-robust: faulty devices cannot corrupt the model

Benchmarks →

Compatibility
Every architecture. One platform.
ARM Cortex-M0 ARM Cortex-M3 ARM Cortex-M4 ARM Cortex-M7 ARM Cortex-M33 ESP32 ESP32-S3 RISC-V
Use cases
Built for the real world.
On-device intelligence for industries where latency, privacy, and power matter.

Predictive Maintenance

Detect machine failures before they happen. Vibration, temperature, current sensors — all processed on-chip.

Wearables

Gesture recognition, activity tracking, heart rate classification. On-device, no cloud dependency.

Medical Devices

ECG arrhythmia detection, SpO2 monitoring, real-time diagnostics directly on the chip.

Bot Detection

Zero-config API protection. Ward learns normal traffic and blocks bots in 0.23ms. On-premise, no signatures needed.
FAQ
Frequently Asked Questions
Yes. Your sensor data is used exclusively to train your model and is never shared with third parties or used to train our own models. All connections are encrypted with TLS, and you can delete your data at any time. See our Privacy Policy for details.
Absolutely. The compiled binary runs 100% on-device with zero cloud dependency. Once flashed on your microcontroller, it needs no internet connection, no API calls, and no external libraries. Pure C, completely self-contained.
On benchmark datasets we achieve 96% average accuracy across sklearn benchmarks (Iris, Wine, Cancer, Digits) and up to 99.6% on CWRU bearing fault detection. On the UCI HAR standard benchmark (561 pre-computed features) we achieve 95%. Real-world results depend on your data quality and the complexity of your classification task. You can train and evaluate for free on the Explorer plan before committing.
Advanced hardware-level copy protection ensures your model runs only on registered devices. Each compiled binary is uniquely bound to your authorized hardware. Unauthorized devices cannot execute the model.
Binaries you've already compiled and deployed continue to work indefinitely — they're standalone and don't phone home. You just lose the ability to train new models and compile new binaries. Your account reverts to the free Explorer plan.
No. Upload a CSV with your sensor readings and labels, click Train, and Luviner handles everything — feature extraction, network architecture, training, quantization, and C code generation. The entire process takes minutes.
Yes. AutoML for MCU lets you specify your target hardware (e.g. Cortex-M0 with 32 KB Flash and 8 KB RAM) and Luviner automatically finds the neural network architecture that maximizes accuracy while fitting within your memory budget. 12+ MCU profiles are pre-configured, or you can set custom Flash/RAM limits.
Vivi is Luviner’s family of proprietary neural network engines — the foundation that powers all our products. Each engine is specialized for different workloads: from ultra-compact models for the smallest chips to deep temporal architectures for complex signal patterns. AutoML automatically selects the best engine for your data and hardware. Edge AI uses them to compile models for microcontrollers; Lynx uses them for real-time server anomaly detection; Ward uses them for bot detection; Mesh Intelligence uses them for distributed sensor collaboration. One engine family, four products, one platform.
Mesh Intelligence lets multiple devices share neural states over a lightweight protocol — no cloud, no central server. Nodes collaborate to improve accuracy by up to +14.5% vs solo nodes, tolerate faults automatically, and self-heal when a node drops. Works fully offline with an optional gateway for dashboard alerts.
Threshold rules (if temperature > 80, trigger alarm) only see one value at one instant. They miss gradual degradation, unusual signal combinations, and context-dependent patterns. Luviner's engine learns what 'normal' looks like from your data, understands how signals evolve over time, and detects anomalies you couldn't write rules for — all in under 20 KB on the chip, with no manual tuning.
From the Blog
Technical deep dives.
Mar 22, 2026

How Temporal Hierarchy Detects What Single Models Miss

Real-world systems have events at different speeds. Our hierarchical mesh uses different temporal sensitivities — fast s...

 Mar 18, 2026

Enterprise-Grade Mesh: 5 Features That Make Distributed AI Production-Ready

Tamper resistance, self-healing, multi-hop reach, intelligent sharing, and on-field learning — all running on 2 EUR micr...

 Mar 14, 2026

Mesh Intelligence: When Your Sensors Form a Distributed Nervous System

Each sensor has its own brain. They share neural states over a 24-byte mesh protocol. Together, they classify what no si...
Read all articles →
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Limited program

We are looking for a small group of manufacturers to validate Luviner in production.

The founding cohort gets one full year of Luviner free, with all engines and embedded export. In exchange, three written check-ins and a case study if it works. Applications close May 31, 2026.

See the program →

Built from scratch by two founders. Zero outside investment. Multiple proprietary engines. 399 tests passing.

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