Absorbdisruptionbyplan,notbyimprovisation
Rail networks, ports, terminals and fleets fail in the same shape: a local delay becomes a network problem, and the response is assembled by phone because no system shows propagation in time to plan.
Disruptionisanetworkeffecthandledbylocaldecisions
A twenty-minute berth delay is not a twenty-minute problem. It moves a crane plan, a yard position, a truck slot and a departure, and each of those moves has downstream effects no single controller can see in time.
The information to plan better already exists in position feeds, timetables, cargo events and maintenance records. What is missing is one network model at the moment the decision has to be made — which is an ontology problem before it is an optimisation problem.
Whereitapplies
Rail capacity
Timetable, actuals and constraint modelling so propagation is visible before it cascades.
Port and terminal operations
Berth, crane and yard slots planned against one live network picture.
Fleet coordination
Vehicle position, schedule and cargo bound to the same shipment object.
Disruption intelligence
Disruptions modelled with a propagation path, not as a status on one trip.
Predictive maintenance
Maintenance history attached to the routes and assets it constrains.
Arrival prediction
ETAs useful early enough to change a slot, with the reason attached.
Infrastructure monitoring
Track, bridge and terminal condition connected to operational decisions.
Emissions reporting
Per-movement data traceable to source for ETS and scope 3 disclosure.
Offshore operations
Weather-constrained planning with approvals and intermittent connectivity.
Re-slotting and dispatch changes go to the network duty controller. Nothing publishes to terminal or fleet systems without a named human authorisation and the evidence attached.
Questions
- Does this replace our TMS or terminal operating system?
- No. Those remain the execution systems of record. NeuroCluster models across them, reasons about propagation and capacity, and publishes approved plan changes back into them.
- Can the system re-plan autonomously?
- By design it does not. Recommendations go to the network duty controller, who authorises before anything is published. Plan-level autonomy is where transport AI creates safety and contractual exposure.
- How do we start?
- One corridor, one terminal or one fleet, with one measurable number — usually delay minutes or plan adherence. We model that slice and replay historical disruptions before connecting the publishing path.
Continue
- Offshore operationsWeather-constrained planning with intermittent connectivity.
- Critical infrastructureResilience obligations on transport networks.
- IndustrialPlants and supply behind the network.
- OntologyThe network model underneath propagation analysis.
- AgentsMandates and approvals for operational work.
- Use casesGoverned applications across transport and logistics.
Bring us one operational problem.
You do not need a finished brief. Bring the problem — we will work out the next step together.
Or book a call with the team