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 gradient-free architecture protected by two provisional patent applications covering 53 claims across core engine dynamics, multi-engine composition, LLM integration, and edge deployment. 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.