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 is a pre-seed stage venture operating under an established German holding structure. Raising a seed round to fund productization, first industrial pilots, and ASIC tape-out decision.

Intellectual Property

Proprietary architecture operating without global backpropagation. RailMind has filed three provisional patent applications relating to the architecture. 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

Over 1.8 million controlled experiment runs across an extensive experiment registry with falsification criteria and sham controls. Online learning paradigm without end-to-end backpropagation — orthogonal to the offline deep learning ecosystem. Three provisional patent applications filed. Estimated 2–3 year replication barrier for a well-funded lab.

Location

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