Railspeed
Year:
2025
Service:
Predictive Operations Automation
Industry:
Logistics
Size:
15–25 employees
Client Website:
Railspeed eliminated 42% of operational delays by replacing reactive maintenance schedules with AI-driven predictive systems — turning scattered logistics data into a unified command center for their growing European rail network.
Introduction
Railspeed moves freight across Europe at scale. Thousands of cargo containers. Hundreds of routes. Dozens of locomotives requiring constant maintenance. It's an operation built on precision — except their systems were anything but precise.
The company was scaling fast, but their infrastructure hadn't kept pace. Train schedules lived in one system. Maintenance logs in another. Cargo tracking in a third. Fleet telemetry? Buried in sensor data nobody had time to analyze. When something broke, teams scrambled. When delays happened, they reacted. Planning was guesswork dressed up as strategy.
Railspeed's leadership knew they were sitting on mountains of valuable data — they just had no way to act on it. They needed a system that could think faster than their planners could coordinate.
Challenge
The irony wasn't lost on Railspeed's operations team: they ran one of Europe's most efficient rail networks, yet spent most of their time fighting their own internal systems.
Here's what a typical day looked like:
Planners manually cross-referencing maintenance schedules against route demands
Maintenance teams getting caught off-guard by equipment failures that sensor data had predicted days earlier
Dispatchers playing telephone across departments to understand fleet availability
Leadership making decisions based on week-old reports instead of real-time conditions
The data existed. The expertise existed. What didn't exist was a system capable of connecting the dots fast enough to matter. As one operations manager described it: "We weren't running a logistics network — we were herding information."
Solution
Source didn't just digitize Railspeed's operations — they made them intelligent.
The platform ingested data from every corner of the business: GPS telemetry from locomotives, maintenance histories, cargo manifests, route performance metrics, even weather forecasts. Then it did what humans couldn't: processed it all simultaneously and turned it into action.
The system introduced:
Predictive maintenance engine analyzing sensor data to flag issues before they caused breakdowns
Automated route optimization adjusting schedules dynamically based on demand, fleet health, and network conditions
Exception-based alerts that only notified teams when human intervention was actually needed
Unified operations dashboard giving every stakeholder — from dispatchers to executives — a real-time view of the entire network
No more reconciling spreadsheets. No more reactive scrambling. The system anticipated problems, suggested solutions, and executed routine decisions autonomously. Humans stayed in control of strategy; automation handled execution.
Result
First quarter post-implementation:
42% reduction in delays caused by preventable maintenance issues
30% less time spent on manual scheduling and coordination
Complete operational visibility across routes, cargo, and fleet performance
Maintenance teams shifted from reactive repairs to proactive optimization
But the real transformation was cultural. Railspeed's teams stopped firefighting and started strategizing. Planners could finally plan. Engineers could focus on innovation instead of patching failures. Leadership had the visibility to make confident, data-backed decisions about expansion and investment.
Today, Railspeed doesn't just move cargo — they orchestrate it. Their network adapts, learns, and optimizes itself. And when competitors are still reacting to yesterday's problems, Railspeed is already solving tomorrow's.
