We build the engines and the tools that let engineers deploy neural networks on microcontrollers, monitor servers, detect bots, and connect devices — four products, one engine family, one platform.
AI belongs where the data is — on the device. Whether it’s a €2 microcontroller inside a machine, a production server running Docker, or a mesh of sensors collaborating without a cloud. Luviner builds the engines and the tools that make this possible. Proprietary architectures power four products: Edge AI for microcontrollers, Lynx for server monitoring, Ward for bot detection, and Mesh Intelligence for distributed collaboration. AutoML matches the best engine to your data. No ML expertise required. No cloud dependency. No vendor lock-in.
Real-time decisions belong where the data is — on a microcontroller, on your server, or across a sensor mesh. 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. Or install one Docker command and monitor your servers. We handle the complexity so you don’t have to.
Everything starts with the Vivi engine family — proprietary neural network architectures designed for commodity microcontrollers. They power four products: Edge AI (compiled binaries under 20 KB for any MCU), Lynx (self-learning server monitoring with zero manual thresholds), Ward (zero-config bot detection in 0.23ms), and Mesh Intelligence (distributed sensor collaboration at 24 bytes per message, no cloud required). One engine family, four products, one platform.
Everything Luviner does starts here. Vivi is a family of proprietary neural network engines built for constrained environments. Unlike traditional ML that sees data as static snapshots, our engines understand how signals evolve over time — detecting gradual degradation, unusual patterns, and novel anomalies that threshold rules and conventional models miss. Each engine is specialized for different workloads. AutoML selects the best one for your data and hardware.
Jessica and Filippo have been working together since 2013 — from Derved (a design studio) to Luviner. Three projects in thirteen years. Each one taught them something: ideas without business skills fail, physical products don’t scale without manufacturing, jumping between projects kills momentum, and a focused product works. Docfire, their automated layout tool, still runs today. Luviner is where everything converges.
Engineer with a PhD in Applied Acoustics. Has been part of every project since Derved (2013). Drives strategic decisions, client relationships, and business development at Luviner. Bridges the gap between deep technology and market needs.
Full-stack developer (Python, PHP, C) and founder of Docfire. Built Luviner’s AI engines, the SaaS platform, the mesh protocol, and 399 automated tests — from scratch, with zero external investment. 15 years of entrepreneurial attempts, from MIDI controllers to design studios to edge AI. Active freelancer in graphic design, video production, and Laravel/Magento development.
The Edge AI market is projected to reach $38.9 billion by 2030, and AI-driven monitoring is growing at 30%+ CAGR. Luviner addresses both with proprietary engines and four products — a platform play in a fragmented market with 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 engines purpose-built for commodity microcontrollers. Models understand signal dynamics, detect anomalies without labeled data, and collaborate across mesh networks. AutoML selects the best engine automatically. All in pure C, under 20 KB, for any MCU.
SaaS model with low infrastructure costs. No GPU clusters needed for inference. Revenue scales with customer deployments, not compute spend.