Problem

The flaw in current routing models

Different vessel types behave differently, which is why routing systems already separate models by vessel type and size - because these details matter to how vessels move.

The same principle applies to geography. Every waterway has its own navigational patterns and practical constraints that shape vessel behaviour.

However, to achieve global coverage, most routing models must simplify or generalise this regional detail. This reflects a fundamental modelling trade-off: prioritising coverage means losing the local detail that drives real-world behaviour.

That trade-off becomes critical as vessels approach their destination, where small deviations can quickly compound. Routes stop reflecting reality, arrival times drift, and planners lose confidence in predictions precisely when operations require reliability.

In these environments, detail matters. And it’s exactly where global models struggle.

Solution

Region-specific AI models built on real behaviour

We build models trained on AIS data which learn how different vessel types move in specific waterways. These models are not only specialised by vessel type and operational context, but by geography, ensuring predictions are finely tuned to the reality of how vessels actually move.

This approach allows our models to perform a variety of important prediction tasks:

• Trajectory - predict the most likely route a vessel will take between two locations.
• ETA - estimate arrival times with data-driven confidence bounds by vessel type.
• Plan - generate fuel-efficient passage plans aligned with a target transit time to meet schedules.

Our routing approach delivers clear advantages over existing models:

Use Cases

Our models support any application that requires reliable vessel movement predictions within a defined region.

Port operators

• Optimise berth allocation and reduce disruption with continuously updated ETAs during final approach to custom locations, such as pilot boarding stations or specific berths.
• Coordinate vessel movements with confidence by predicting trajectories and ETAs to shared locations - for example, aligning pilot vessel departures with the arrival of inbound cargo vessels.
• Improve fuel efficiency and arrival timing by generating alternative passage plans when predicted ETAs are not optimal, ensuring vessels arrive when needed.

Vessel tracking

• Enhance situational awareness by integrating trajectory predictions into vessel tracking and monitoring systems.
• Bridge AIS coverage gaps by estimating a vessel’s current position and future route based on historical AIS broadcasts.

Autonomous vessels

• Validate and benchmark navigation systems against realistic, modelled vessel movements.
• Enable safe navigation in complex local environments by embedding region-specific navigational behaviour, allowing autonomous vessels to operate in constrained waterways while adhering to local practices.

Workflow

Insights without changing how you work

Access reliable predictions without changing the way you work. Our models are designed to integrate cleanly into existing workflows and systems, making it easy to move from data to decision.

Generate predictions via:

• Viewer - a no-code, browser-based interface for generating and visualising individual predictions powered by our APIs.
• APIs - low-code integration into planning, monitoring, or decision-support systems. See the Docs for details.

Start by supplying:

• Vessel type and length category - predictions account for specific vessel characteristics.
• Locations - a live or historical vessel position, plus a current or intended destination, supplied as simple coordinates.

Models

Tested against reality

Our models undergo comprehensive testing before deployment, using thousands of randomly selected real vessel transits with results aggregated for robust evaluation. To ensure reliability, each model is benchmarked on two key tasks:

• Trajectory reconstruction - assessing how accurately the model can recreate vessel tracks from start to finish - this forms the core foundation of our predictions.
• ETA prediction - evaluating how accurately the model estimates vessel arrival times by building on the predicted route and capturing vessel behaviour throughout the journey.