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Major Retailer Cuts Mainframe Modernization Time by 70%

Sapient Slingshot proves speed and safety can coexist.

Summary

A six-week Slingshot initiative confirmed that mainframe modernization no longer has to be a multi-year bet.

60– 70 %

faster migration

95 %

accuracy in generating specification

80 %

automated unit test coverage

Summary

Client

Retail Company

Topic

Legacy Modernization

Product

Slingshot

Partner

Microsoft

Intro

With over 2,200 stores across the United States, this major food and drug retailer—like many retailers—has been running parts of its business on systems built for a different era. The mainframe still works, but it costs the enterprise speed. And with a diminishing talent pool, it has been difficult to maintain. Moving to a modern, scalable and supportable platform could ease a lot of their pain points.

The retailer wanted to test if large-scale mainframe modernization could be done with the help of AI safely, quickly and at true enterprise scale across some of the most complex systems they have.

The Problem

Over decades, the retailer’s mainframe environment has grown massive and tangled. Critical business logic lived across COBOL, Java, Python and Shell scripts. These were tightly coupled with limited supporting documentation, which makes it difficult to improve the system. Previous modernization attempts promised progress but didn’t deliver results.

The Solution

Slingshot became the accelerator. In a six-week proof of concept (POC), Slingshot took on the hardest part of modernization: turning legacy systems into cloud-ready services without losing intent or functionality. Slingshot automates the pipeline from code to spec to design, using generative AI with human-in-the-loop controls to keep quality high and errors low.

The platform started by identifying and prioritizing high-impact programs across COBOL, Java, Python and Shell. It mapped dependencies, generated clear technical specifications and BDDs, and translated them into a modern, event-driven target architecture. From there, Slingshot converted legacy logic into Spring Boot Java microservices, resolved cross-system dependencies and pushed code through automated testing and deployment pipelines.

The result wasn’t just migrated code—it was production-ready services deployed on Azure[, designed to scale and built to evolve. The POC established a scalable, AI-led modernization pattern that the retailer can use for future initiatives.

retailer-slingshot-mainframe-modernization

The Impact

The six-week POC delivered measurable results across speed, quality and scale:

  • 60–70% faster migration versus manual
  • 95% accuracy in generating specifications
  • 80% automated unit test coverage
  • Lower modernization cost and risk through repeatable automation
  • Like-for-like functionality delivered through Azure-deployed microservices
  • A proven, scalable GenAI-led modernization pattern ready to be applied across the enterprise

Learn how our platforms actually work in an enterprise

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75%

faster modernization
50%

cost savings

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