Coforge

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Coforge: Where AI engineering meets industry expertise.

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Composable Enterprise

CASE STUDY

Travel Technology Leader Modernized 2M+ Lines of Legacy Code with AI-Assisted Engineering, Accelerating Delivery by 33%

Industry

Travel & Transportation Technology

Our Contributions

Legacy Modernization, Cloud Migration, AI-Driven Engineering

Location

Global

Technologies

GCP, GitHub Copilot, CodeInsightAI, Forge-X

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Coforge partnered with the client to modernize its legacy Global Distribution System (GDS) platform and enable a scalable, cloud-native architecture. The existing system, built on IBM TPF Assembler and TPF C, faced high operational costs, limited agility, and challenges in supporting evolving business demands.

By leveraging AI-assisted engineering and a structured reverse and forward engineering approach, Coforge transformed core reservation, ticketing, and inventory systems. The modernization enabled seamless migration to Google Cloud Platform (GCP), improved system performance, and significantly reduced operational costs while enhancing scalability and observability.

Transformation Timeline

Phase 1
Legacy system assessment and modernization strategy (reverse engineering) 
Phase 2
MVS offload and application migration planning
Phase 3
AI-assisted development and cloud migration (AWS to GCP)
Phase 4
Observability enablement and optimization

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The Challenge

The client’s legacy GDS platform was built on IBM TPF Assembler technology, supporting high-volume OLTP workloads exceeding 1,000 transactions per second. This resulted in high maintenance costs, limited flexibility, and constrained innovation.

Running costs exceeded $100M annually, with additional expenses from maintaining hybrid cloud workloads. The system’s complexity, large codebase (2M+ lines of code), and dependency on legacy technologies made modernization challenging.

Additionally, the client needed to migrate workloads to a cloud-native environment while ensuring minimal disruption, improved observability, and sustained performance at scale.

Our Approach

AI-Assisted Reverse & Forward Engineering

Adopted a two-stage modernization approach, reverse engineering legacy systems followed by forward engineering to modern architectures using AI-powered tools.

Large-Scale Application Modernization

Executed an MVS offload program to migrate five core mainframe applications with over 2M lines of code, ensuring continuity and performance.

Cloud Migration & GCP Enablement

Built a GCP landing zone and migrated workloads from AWS to GCP, optimizing infrastructure and improving scalability.

AI-Driven Development Acceleration

Leveraged GitHub Copilot, CodeInsightAI, and Forge-X to accelerate development, improve code quality, and enhance productivity across the PDLC.

Agile Delivery at Scale

Deployed 40+ autonomous scrum teams to execute parallel modernization streams, ensuring faster delivery and reduced time-to-market.

Partner / Technology Ecosystem

  • Google Cloud Platform (GCP) 

  • GitHub Copilot 
  • CodeInsightAI 
  • Forge-X Platform
  • Legacy Mainframe Technologies (TPF Assembler, TPF C)

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Impact to Date

~33%

Reduction in Time-to-Market

~90%

Automated Test Coverage

Zero

Code Review Defects (Repeat Issues)

Reduced

Infrastructure & Cloud Costs

Business Impact

 

  • Lowered operational costs through cloud optimization and cross-platform migration 
  • Improved system agility and scalability with modern cloud-native architecture 
  • Enhanced code quality and engineering efficiency through AI-assisted development 
  • Increased reliability with end-to-end observability and monitoring 
  • Accelerated delivery timelines with large-scale agile execution

 

Coforge enabled the client to transform a complex, high-cost legacy GDS platform into a scalable, cloud-native ecosystem powered by AI-assisted engineering. The modernization reduced operational costs, accelerated time-to-market, and improved system reliability while preserving business-critical functionality. This transformation positioned the organization to innovate faster, scale efficiently, and meet evolving travel industry demands with confidence.