Our Mission

RailMind builds edge-native AI that works without the cloud. We believe industrial intelligence should run where the data lives — on the machine, at the edge, in real time.

Leadership

Jianwei Lou — Founder & CEO

Jianwei Lou is a researcher and engineer with deep expertise in self-organizing neural architectures and industrial edge computing. He founded RailMind to bring a fundamentally new approach to AI — one that requires no training data, no cloud, and no GPU.

Stage

RailMind GmbH (i.G.) is a pre-seed stage company preparing for incorporation in Germany. Raising a seed round to fund productization, first industrial pilots, and ASIC tape-out decision.

Intellectual Property

Proprietary gradient-free architecture with provisional patent filed. Four algorithmic innovations protected by semantic security boundary. The core inference engine, adaptive learning dynamics, and edge runtime are not open source.

Market Opportunity

Beachhead: Edge AI in Predictive Maintenance (~$4B TAM, 2026). Industrial sensors are doubling every two years, but existing solutions require cloud connectivity. Expansion path: autonomous systems, structural health monitoring, and chip IP licensing (ARM-style royalty model).

Business Model

Phase 1: Engine IP license to system integrators (sensor manufacturers, edge hardware vendors). Phase 2: ASIC/FPGA tape-out for royalty model. Positioned as embedded engine IP — not a platform. Think ARM, not Augury.

Technical Moat

Hundreds of thousands of controlled experiment runs across an extensive experiment registry with falsification criteria and sham controls. Non-gradient paradigm — orthogonal to the entire deep learning ecosystem. Provisional patent filed with 4 protected algorithmic innovations. Estimated 2–3 year replication barrier for a well-funded lab.

Location

Neuss, Germany — in the industrial heartland of North Rhine-Westphalia, with proximity to major manufacturing and logistics hubs.