Great Rail Journeys
AI-Powered Fare Prediction
Machine learning that outperformed manual fare estimation in 15 of 16 categories, proving the commercial case for AI-driven pricing in travel.
The Challenge
Great Rail Journeys' customers book up to two years before travel. Rail tickets aren't released for sale until much closer to departure, forcing the business to price packages long before they know their actual costs. Previously this relied entirely on manual estimation. This was time-consuming, prone to missing sudden shifts in inflation, interest rates, and fuel prices, and dependent on key individuals. Accurate estimation is critical: it determines whether customers get a fair, competitive price and whether tours are profitable.
Two-Year Pricing Gap
Customers book years in advance, but rail fares aren't available until close to travel, creating a pricing estimation challenge that directly impacts profitability and competitiveness.
Manual Estimation Risk
Pricing depended on manual processes and key individuals, creating single-point-of-failure risk and inconsistency across the product range.
External Volatility
Inflation, interest rates, fuel prices. These external factors that affect fare prices were difficult to account for systematically using manual approaches.
Our Approach
We partnered with Great Rail Journeys on a proof of concept to determine whether AI/ML could match or improve upon manual fare estimation. We trained multiple models on historical booking data, iterated through data quality challenges in close collaboration with GRJ's data team, and validated predictions against real-world outcomes.
The service was deployed as a cloud-native application on Azure Container Apps with full infrastructure-as-code, creating a production-ready foundation for continued AI adoption.
Technical Solution
ML Ensemble Models
CatBoost, Random Forest, and XGBoost combination producing highest-scoring predictions
Python ML Pipeline
Repeatable pipeline predicting prices across different dates, routes, and product ranges
Azure Container Apps
FastAPI service on Azure with Terraform IaC, VNet security, and HTTPS endpoints
Model Validation
Go-based analysis tool comparing predictions against actual fares using Mean Delta, MAE, and RMSE
Security
Private container registry, IP whitelisting, VNet integration, HTTPS-only access
Data Engineering
Overcame low data volumes, data evolution anomalies, product classification gaps, and recent data challenges
The Impact
The model outperformed manual prediction in 15 of 16 categories across tens of thousands of bookings. The overall mean delta was almost zero, meaning wins and losses cancelled each other out almost exactly across the full product range. The Classic product range, notoriously hard to predict due to ticket class differences the model couldn't see, still achieved a MAE of 13.95, significantly better than manual.
The POC proved that AI/ML can not only match but improve upon manual fare estimation, unlocking fair and competitive customer pricing, reduced risk exposure to price spikes, more accurate P&L forecasting on tours, and the ability to redirect manual effort toward higher-value activities like new route development.
This is a great example of how AI can be used to drive business value that directly hits the bottom line. Working in collaboration with our domain SMEs, Enablis were able to understand our business and deliver an incredibly impressive outcome very quickly.
Kerry Jenkin
CIO, Great Rail Journeys
Long-Term Value
AI-Driven Pricing
Great Rail Journeys now has proven AI capability that transforms how they approach pricing across their product range.
- •ML pipeline from training data to deployed prediction API
- •Model validated across tens of thousands of bookings
- •Business case proven: AI outperforms manual estimation consistently
Strategic Foundation
The success of the pricing POC opened the door to broader AI adoption across the business.
- •Production-ready Azure infrastructure with Terraform IaC
- •Proposed Tour Design Companion platform for AI-assisted product creation
- •Safeguarded against single-point-of-failure pricing dependency
Related Capabilities
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