Case Study · Quality Engineering

Enterprise Retailer Leverages AI-Assisted Testing to Improve Coverage and Reduce Testing Effort

Enterprise US-based omnichannel retailer with large-scale digital and store operations

2024-202511-month engagement delivered between April 2024 and February 2025

The challenge

The client relied heavily on manual testing across multiple applications, resulting in long release cycles, inconsistent test coverage, and increasing defect leakage into production. As the number of releases grew, testing became a bottleneck, impacting both speed and overall product quality.

What Everest delivered

Everest implemented an AI-assisted quality engineering framework to automate and optimize testing across the client’s technology landscape. The engagement included building scalable test automation frameworks, integrating AI-driven test optimization tools, and embedding continuous testing into the CI/CD pipeline delivered by a team of 24 quality engineers, automation specialists, and DevOps experts.

Solution components

  • AI-assisted test case generation and optimization
  • Automated regression and functional test suites
  • Integration with CI/CD pipelines for continuous testing
  • Intelligent defect prediction and prioritization
  • Performance and load testing for peak traffic scenarios

Business outcomes

  • 48% reduction in manual testing effort
  • 35% increase in test coverage across applications
  • 30% faster release cycles
  • 42% reduction in production defects
  • $2.1M annual savings through automation and reduced rework

Everest helped us move from manual, reactive testing to a smarter, more proactive approach. The improvement in both speed and quality has been significant.

Client perspective, Enterprise US-based omnichannel retailer with large-scale digital and store operations