How Corpay Transformed Pricing and Support with Machine Learning

Corpay, a Fortune 500 leader in business payments, partnered with Orbital Studio to access PhD-level machine learning expertise for two strategic initiatives in their European. Together, we developed and productionized a Dynamic List Pricing (DLP) system that included segmentation, churn, and margin optimization models, scalable across multiple EU countries. In parallel, we engineered an AI Router to classify Salesforce tickets with high accuracy, improving customer support efficiency. These solutions delivered greater automation, scalability, and predictive power, strengthening Corpay’s competitive advantage in the European market.

The Challenge

Corpay, a Fortune 500 leader in business payments and expenses, was seeking a development partner with deep machine learning expertise to support two high-priority initiatives in their European fuel-card business.

They needed:

A Dynamic List Pricing (DLP) framework to propose dynamic monthly margins across multiple EU countries.
An AI Router to process Salesforce tickets more efficiently by classifying free-text requests into actionable categories.

To deliver on these goals, Corpay required access to highly skilled ML engineers capable of building and productionizing advanced models, while ensuring scalability across diverse datasets and markets.

From Challenge to Solution

Corpay approached Orbital Studio to provide PhD-level machine learning expertise that could take early prototypes and turn them into production-ready systems. Their goals were to scale a Dynamic List Pricing (DLP) framework across multiple EU markets and to develop an AI-powered router for Salesforce tickets.

Working closely with Corpay’s team, we:

Built and refined three key models for DLP: segmentation, churn, and margin optimization, including a dynamic margin optimization algorithm.
Productionized the models, ensuring they could operate smoothly across different EU countries and product lines.
Automated workflows and created a simulation module to forecast “possible futures” using live data.
Extended the codebase to cover additional fuel-card products with unique dataset characteristics.
Developed an AI Router tool that classified Salesforce tickets into cancellation vs. non-cancellation categories, balancing high accuracy with cost efficiency.

Together, these solutions delivered scalable, automated, and predictive systems that improved both pricing strategy and customer support efficiency.

The Results

By partnering with Orbital Studio, Corpay gained immediate access to PhD-level AI engineers capable of taking early prototypes to production-grade systems.

Increased efficiency through automation and predictive simulation.
Improved scalability with models extended across multiple countries and product lines.
Faster response times in customer support through AI-powered ticket routing.

Orbital Studio's contribution accelerated Corpay’s ability to apply advanced AI in operational processes, strengthening their competitive edge in the European fuel-card market.