RANGE Master AI — Six Layers of Simple, Trustworthy Intelli…

Tomorrow’s Technology Today ™

How it works: Conventional electric propulsion readouts—State-of-Charge (%) and average “Time-to- Go”—don’t answer the only question skippers truly care about: How far can I go, right now, and how long will it take to recharge? As electrification accelerates, that blind spot fuels range anxiety and slows adoption of clean technology. RANGE Master AI replaces static gauges with a system that continuously learns your vessel, adapts to conditions, and explains decisions in natural language. It’s the difference between a battery meter and a digital first mate. The insight was to separate immediate assurance from future foresight—two groups of AI that don’t fight each other: • Assurance (Estimating): Physics-informed machine learning and mathematical models create confidence in the current range estimate and support compliance for autonomous control. • Foresight (Prediction): Multimodal “senses” forecast how range will shift with speed, loading, and sea state, and let the skipper interrogate the system in plain language. This split is profound in effect yet simple in operation—reliability without complexity.

The Six Layers 1) Physics-Informed First-Use Modelling (30 minutes to confidence)

Out of the box, hydrodynamic laws and a three-pass learning routine produce the tender- specific drag and negative reactive power curve. A gradient-boosted regressor then tunes coefficients as data arrives, so range estimates start sensible—no “impossible” claims from an untrained boat. RANGE Master predicts range-to-go deltas (not just totals), showing how range changes with speed, load, and sea conditions—presented right above RPM on a super bright screen for immediate decisions. 2) Shadow Model & Canary Promotion (safe, cautious improvement) On current and subsequent outings, live data refines the first-use model without risking stability. A shadow model runs alongside production while 3–5 challengers compete in real time. Canary testing promotes only the best performer, so updates are cautious and auditable. Anonymous summaries can be shared with the OEM fleet to lift all vessels without exposing private data. 3) Federated Fleet Learning (tiny payloads, big gains) Each vessel contributes intelligence to the cloud without uploading massive binaries. Our patent-pending approach ships only a handful of parameters into the canary process, dramatically reducing data traffic. Every unit includes a modem and 5-year eSIM, enabling