App Modernization Strategy using Generative AI for Retail Industry

Key Highlights

  • The project aimed to modernize a complex and outdated monolithic Java system for a leading US retail company, which was costly, inflexible, and impeded innovation.
  • The monolithic architecture caused system-wide issues with minor changes, risking outages and slowing down the company’s ability to respond to market needs.
  • We used our advanced GenAI solution, iBEAM, to swiftly refactor the system into microservices, automating code generation, testing, and creating robust APIs.
  • The modernization led to faster release cycles, improved scalability, and reduced costs, enabling the retailer to quickly adapt and enhance profitability.

Problem Statement


Impacts of Outdated system: A retail giant in the USA faced significant challenges due to an outdated monolithic Java system, leading to slow innovation, scalability issues, and high maintenance costs.


Complex Monolithic Architecture: Their complex monolithic architecture caused system-wide cascades with minor alterations, hindering agility and risking potential outages.


Legacy System Challenges: The client actively pursued migration solutions, aiming for rapid modernization within weeks, to overcome legacy system challenges while minimizing costs.

Solution Overview


GenAI analyzed the codebase, identifying microservice-suited functionalities, focusing on high-impact areas that affect customer experience and efficiency.


Our GenAI automated boilerplate, API creation, and unit testing, freeing developers for complex tasks while identifying errors and security vulnerabilities early on.


Integrated GenAI for code reviews and early issue detection, generating clear documentation for each microservice, ensuring knowledge transfer and future maintainability.


Packaged each microservice in a container for lightweight and portable deployment, enabling isolated deployments to minimize risk and downtime.


Microservice performance was monitored to identify bottlenecks, suggest optimizations, and make informed decisions on resource allocation and scaling using GenAI analytics.

Business Impact


Accelerated Time-to-Market: Gen AI reduced the transformation timeline, enabling faster release cycles and quicker time-to-market for new features.
Improved time-to-market


Enhanced Return On Investment: Gen AI automation increased ROI and reduced development cost, enhancing profitability and financial efficiency.
Boosted ROI


Enhanced Scalability: The microservices were scaled independently to accommodate growing demand, and ensuring optimal performance.
Increased scalability



The project involved modernizing an outdated Java system for a prominent US retail company, leveraging our advanced Gen AI solution, iBEAM. The legacy system restricted progress, constrained scalability, and resulted in significant maintenance costs. Gen AI implemented a microservices architecture, leading to faster transformation, improved ROI, and reduced development costs. We enhanced scalability and developer efficiency, rendering the retail business more agile, cost-efficient, and innovative.

Tech Stack

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