Domain-Specific Logistics Models

Trained on logistics data
Our models learn from real transport data: orders, trips, rates, status histories and deviations. They recognise how logistics processes actually run in practice.

Knowledge of logistics concepts
Loading metres, pallet spaces, weight tiers, lanes, Incoterms, driving and rest times and ADR classifications are not unfamiliar terms to these models, but part of their basic knowledge.

Understanding of chain logic
Logistics is made up of dependencies. A delay at loading has consequences for delivery, and a capacity shortage on one lane affects another. Domain-specific models take those connections into account.

A generic model has to guess the context first for every question, and regularly guesses wrong. A domain-specific model starts with that context. The result is fewer errors, less explanation up front and outcomes that are correct from a logistics point of view.

Our domain-specific models form the foundation of Noventra’s other AI applications. They are the starting point for Custom AI Model Development and feed, among others, Predictive Analytics & Forecasting and AI-Powered Decision Support.