Edge-First Vessel Motion Intelligence

The vessel that
learns itself

ShipMotionIQ builds a continuously improving motion model for each ship, from real operational data, no design documents required. Predictions that get better every hour at sea.

Day1
Useful predictions from install
100%
Offline capable, no internet needed
6DOF
Full motion envelope modeled

The Problem

Today's vessel motion prediction is broken

Physics-based simulation tools require expert consultants, full ship design documentation, and weeks of offline computation. They never adapt to the actual vessel in operation. Captains rely on experience and rough estimates when loading and routing decisions matter most.

Static models, dynamic reality

RAO curves and seakeeping simulations are computed once, then never updated. The vessel in operation bears no relation to its design-stage model.

Expert dependency and cost

Meaningful motion analysis requires specialist consultants and full ship design documentation, inaccessible for most operators and impossible in real-time.

Cargo decisions made blind

Cargo owners loading sensitive or high-value freight onto vessels have no certified, data-backed way to assess motion risk before committing.

Three Products, One Core

Built on a self-learning motion engine

Install once. The system continuously observes sensor data, builds its model, and makes predictions that improve with every voyage.

01 / EDGE
Self-Learning Vessel Digital Twin
An empirical motion model unique to each vessel, built from real operational data. No design documentation required. Runs fully offline on the ship, anywhere in the world.
EDGE, OFFLINE CAPABLE
02 / COMMERCIAL
Motion Passport
A verifiable, exportable document showing certified predicted vessel motion behavior for specified voyage conditions. Presented to cargo owners, charterers, and insurers before loading.
KILLER COMMERCIAL FEATURE
03 / API
Routing & Optimization API
The learned motion model exported as an API. Weather routing systems and fleet management tools query vessel-specific motion predictions for motion-aware route optimization and LNG sloshing risk management.
B2B INTEGRATION

The Learning Loop

Every hour at sea makes the model smarter

A closed feedback loop between vessel edge and Azure cloud means the model continuously improves. Cross-vessel learning means every new ship accelerates faster.

Vessel Edge
Sensor Fusion
6DOF IMU, GPS, weather feed, loading computer, fused and filtered in real time.
Roll, pitch, heave, accelerations
Significant wave height & period
Speed, heading, draft, trim
Local Model
Hybrid Physics-ML
Physics prior layer gives day-1 predictions. ML correction layer learns vessel-specific residuals over time.
Physics prior (RAO bootstrap)
ML correction (LightGBM/NN)
Bayesian confidence bounds
Azure Cloud
Fleet Training
Per-vessel model improvement and cross-vessel transfer learning. Model updates pushed back when connected.
IoT Hub + Azure ML pipeline
Cross-vessel transfer learning
Model registry + versioning

Competitive moat through data

The longer the system runs, the more accurate it becomes, and the harder it is to replicate. Data collected offline is synchronized in batches, without requiring real-time connectivity.

Accuracy over time
Bandwidth required
0
Internet dependency

Motion Passport

The performance certificate backed by real data

Generated on-vessel or via cloud API. Presented to cargo owners, charterers, and insurers before loading. Includes confidence levels, data coverage map, and comparison with class society limits.

Digitally signed
Timestamped, verifiable, tamper-evident. Archiveable for audit trail.
Data coverage transparency
Shows which conditions are observed vs. interpolated, honest confidence bounds.
Class society aligned
Designed for alignment with DNV, Lloyd's Register, and Bureau Veritas standards.

Motion Passport

MV Northern Horizon · Sample vessel

VERIFIED
DATA
Hs 3.2m · Tp 11s · 045°
8.4m · Loaded
v4.2 · 847h trained
High (interpolated)
Predicted Motion Response
Roll
4.1°
Pitch
1.8°
Heave
0.6m
VertG
0.09g
Overall Prediction Confidence94.7%

Technology & Trust

Certifiable by design, not an afterthought

Pure black-box AI cannot be certified by classification societies. ShipMotionIQ uses a hybrid physics-informed architecture where AI calibrates physics-based safety envelopes rather than replacing them.

Physics-Informed Hybrid Model

Physics prior layer (RAO-based) provides day-one baseline. ML correction layer learns vessel-specific residuals. Not a black box, understandable, auditable, certifiable.

Honest Uncertainty Quantification

Bayesian confidence bounds on every prediction. The system tells you when it doesn't know, critical for safety-of-life decisions and classification society acceptance.

Edge-First Architecture

Core functionality operates fully offline on vessel hardware. Cloud provides improvement, not dependency. No internet connectivity required for predictions or Motion Passport generation.

Classification Society Ready

The hybrid physics-informed architecture is designed from the ground up for engagement with DNV, Lloyd's Register, and Bureau Veritas. Auditable model structure, documented uncertainty bounds, and traceable data lineage.

Azure-Powered Cloud Layer

IoT Hub, Azure ML, Data Lake Gen2, enterprise-grade infrastructure for fleet training, model registry, and per-vessel model delivery. ONNX model portability ensures cloud-to-edge integrity.

Cross-Vessel Transfer Learning

Every new ship benefits from what the fleet has already learned. Sister vessels bootstrap faster, rare sea states get covered sooner, and the whole fleet converges on safer, more accurate predictions.

Early Access

Join the first wave of vessel operators

Built for heavy lift, offshore wind, and LNG operators who need motion intelligence they can trust on the bridge, not in a consultant's report. Talk to us about bringing ShipMotionIQ onto your vessels.

Targeting heavy lift vessels · Offshore wind installation · LNG carriers