Think of this as turning Talgo’s trains into ‘smart trains’ that constantly send information about how they are running to a central brain. That brain analyzes everything in near real time so Talgo can run trains more punctually, maintain them before they break, and plan operations more efficiently.
Talgo needs to operate and maintain trains more reliably and efficiently across networks and countries, but data is scattered in multiple systems and coming in real time from trains and infrastructure. The platform consolidates and analyzes this data to improve punctuality, reduce downtime, and optimize maintenance and operations.
Deep domain integration with Talgo’s fleet, telemetry systems, and operational processes plus historical operations and maintenance data, which is hard for competitors to replicate quickly.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Handling and storing large volumes of high-frequency telemetry and time-series data from trains in real time while keeping latency low for analytics and predictive models.