Case Study

Enabling AI-Driven Freight Pricing and Accelerated Pega Modernization for a Leading North American Logistics Provider

Industry

Transportation and Logistics

Location

North America

Our Contributions

AI-Powered Pricing Engine, Pega Modernization, Upgrade Factory Implementation, Platform Engineering

Technologies

Pega Platform (Infinity), AI/ML Models, AWS, Azure

Coforge partnered with a leading North American LTL and freight logistics provider to transform its pricing operations and modernize its enterprise Pega ecosystem. The initiative focused on building a predictive AI-powered pricing and rating engine while simultaneously upgrading and stabilizing a complex landscape of enterprise applications.

The engagement replaced an incumbent partner and introduced a factory-driven modernization model, enabling faster upgrades, improved platform performance, and a clear roadmap toward cloud-native deployment.

Transformation Timeline

Phase 1

Platform assessment and upgrade strategy definition

Phase 2

AI-powered pricing engine development and deployment

Phase 3

Pega upgrade factory implementation and execution

Phase 4

Optimization, stabilization, and cloud-readiness enablement

Drag

The Challenge

The client faced significant operational and technical challenges across both pricing operations and platform modernization.

Key challenges included:

Lengthy and inefficient upgrade cycles taking 6–12 months with limited business value
High risk of operational disruption across critical systems, including driver mobility and dispatch
Complex multi-environment landscape leading to duplication, inefficiency, and extended timelines
Parallel development during upgrades causing rework and instability
Large data volumes impacting upgrade performance and recovery
Mobile compatibility issues across different platform versions

Our Approach

AI-Powered Dynamic Pricing and Rating Engine

Developed a predictive pricing engine to automate freight rating and significantly reduce cycle time for pricing agreements, improving efficiency and accuracy.

Pega Upgrade Factory Model Implementation

Introduced a factory-driven upgrade model to standardize and accelerate upgrades across 15 enterprise-grade Pega applications.

Optimized Upgrade Strategy and Execution

Adopted a near-production clone upgrade approach, reducing dependency on multiple environments and accelerating production rollout timelines.

Architecture and Data Optimization

Implemented split-schema architecture, archival, and data purging strategies to enhance performance, resilience, and minimize downtime.

Automation and Governance Enablement

Leveraged rule-based comparison utilities, test optimization strategies, and continuous reporting to ensure consistency, transparency, and quality across upgrades.

Cloud-Ready Architecture Definition

Established a future-ready architecture aligned with containerized deployments on AWS and Azure, enabling seamless transition to a cloud-native ecosystem.

Impact to Date

30%+

Improvement in auto-rating accuracy

Significant reduction

In pricing cycle time from weeks to hours or days

70-85%

Reduction in upgrade timelines

Zero or near-zero

Downtime across critical applications

20+

Redundant environments removed

Stabilized platform

with sustained upgrade velocity

Business Impact

Accelerated pricing operations with improved accuracy and responsiveness
Reduced operational risk through standardized and efficient upgrade processes
Decreased infrastructure and maintenance costs through environmental rationalization
Improved system stability and performance across enterprise applications
Enabled continuous modernization with a scalable, factory-driven model
Established a clear roadmap for cloud-native transformation

By combining AI-driven pricing innovation with a factory-based modernization approach, Coforge enabled the organization to accelerate operations, reduce risk, and simplify its complex technology landscape. The result is a high-performance, future-ready platform that delivers faster business outcomes today while positioning the enterprise for scalable, cloud-native growth.