RailMind
— Adaptive Edge AI Engine
No NPU, no cloud, no training data. An online-adaptive engine with no end-to-end backpropagation, validated on 35+ public datasets across 19 domains, powering industrial predictive maintenance, real-time video intelligence, and beyond.
What is RailMind?
RailMind is an on-device adaptive engine — no end-to-end backpropagation, zero gradients at deployment. It eliminates cloud connectivity, training data, and GPU hardware. Born from a dissipative-cognition research program and distilled into a deployable substrate, it detects anomalies, flags distribution drift, and continuously adapts online.
Core Principles
Four foundational principles drive RailMind's architecture.
No NPU Needed
Runs on standard ARM Cortex-M processors. Zero hardware upgrade cost — existing industrial MCU gains top-tier AI capability through firmware update alone. SIL-Ready architecture.
1000-Step Rapid Convergence
No weeks of training. The engine establishes a physical baseline in just 1000 sampling steps. A 5Hz device is ready in 200 seconds; a high-speed line in under 1 second.
Hybrid Edge Architecture
MCU handles microsecond perception, MPU handles decision-making. Sensor data is transformed into actionable maintenance recommendations — not just fault codes, but guidance on what to fix.
Performance
Controlled experiment runs across multiple research lines
Across 19 domains. Each domain modelled and validated independently; we do not assume transfer between domains.
Pre-training, labeled data, or end-to-end backpropagation required
Live Demo
RailMind in action across rail, wind, marine, and industrial scenarios — edge-deployed predictive maintenance running on real production equipment.
Demo video coming soon
Applications
Industrial Predictive Maintenance
Edge PdM for rotating machinery across rail, wind, marine, and general industry — validated on CWRU and PHM2022 datasets. Covers 5Hz to 1000Hz equipment.
Video & Streaming Intelligence
Real-time video quality-of-experience detection, codec optimization, and semantic analysis — validated on streaming QoE benchmarks
Structural & Infrastructure Monitoring
Continuous structural health monitoring for bridges and civil infrastructure — validated on Z24 Bridge dataset
Architecture
A layered architecture from resource dynamics through competing computational units to edge deployment — MCU for perception, MPU for decision-making.
Competitive Landscape
| Capability | RailMind | Siemens Copilot | Augury | BrainChip |
|---|---|---|---|---|
| NPU required | No (MCU only) | Cloud GPU | Cloud | Dedicated ASIC |
| On-device learning | Continuous | Fixed after training | ||
| No end-to-end backprop | ||||
| Built-in drift detection | External | External | ||
| Multi-domain validated | 35+ datasets, 19 domains | Vibration + thermal | Vibration only | Generic |
Ready to get started?
Whether you're an investor, a potential partner, or an engineer evaluating edge AI — we'd love to hear from you.
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