We build the tools that let engineers deploy neural networks on microcontrollers — without writing a single line of ML code.
Every year, billions of microcontrollers ship inside industrial machines, medical devices, and consumer products. Almost none of them run AI — not because the hardware can't handle it, but because the tooling doesn't exist. Luviner changes that. We give hardware teams a way to go from sensor data to a compiled, hardware-locked AI binary in minutes. No ML expertise. No cloud dependency. No vendor lock-in. Just pure C code that runs on a €2 chip.
Real-time decisions belong where the data is — on the device. No latency, no connectivity issues, no privacy concerns.
Your models run only on your hardware. UID binding and digital watermarks ensure your AI stays yours.
Upload CSV. Train. Download binary. Three steps, no PhD required. We handle the complexity so you don't have to.
Luviner is built on a proprietary neural network architecture designed for microcontrollers. Models are smaller, faster, and more energy-efficient than traditional deep learning.
Luviner is an independent software company based in Italy, focused on making Edge AI accessible to every hardware team in the world. We work with manufacturers, OEMs, and system integrators across predictive maintenance, medical devices, and industrial IoT.
Engineer with a PhD in Applied Acoustics. Leads strategy, business development, and partnerships — bridging deep domain expertise in sensor data and signal processing with Luviner's mission to make Edge AI accessible.
Software developer and founder of Docfire. Built Luviner to make Edge AI accessible to every hardware team — no ML expertise required, just upload your data and deploy.
The Edge AI market is projected to reach $38.9 billion by 2030. Luviner is building the developer platform that makes deploying AI on microcontrollers radically simple — a segment with massive demand and no dominant player yet.
Edge AI and TinyML are among the fastest-growing segments in tech. Industrial IoT alone is a $500B+ market increasingly demanding on-device intelligence.
Proprietary neural network engine with unique capabilities: temporal processing, on-device learning, and model compression — all in pure C for any MCU.
SaaS model with low infrastructure costs. No GPU clusters needed for inference. Revenue scales with customer deployments, not compute spend.