Challenge
A Fortune 500 FashionTech retailer had no formal QA processes, consistent regression protocols, or scalable automation. With only ~300 existing tests, coverage was low and release cycles were slow, making it risky to ship frequent mobile and web updates with confidence.
A Fortune 500 FashionTech retailer had no formal QA processes, consistent regression protocols, or scalable automation. With only ~300 existing tests, coverage was low and release cycles were slow, making it risky to ship frequent mobile and web updates with confidence.
Solution
ZoolaTech built a QA strategy from scratch for both mobile and web by embedding manual and automation engineers across 7 cross-functional teams in paired iOS/Android/Manual QA units. We designed maintainable test architecture, added network mocking for stability, integrated CI/CD for fast execution, and automated analytics and localization with AI.
ZoolaTech built a QA strategy from scratch for both mobile and web by embedding manual and automation engineers across 7 cross-functional teams in paired iOS/Android/Manual QA units. We designed maintainable test architecture, added network mocking for stability, integrated CI/CD for fast execution, and automated analytics and localization with AI.
Results
Delivered ~2,000 automated tests for iOS and Android and reached 75% automation coverage. Manual regression time dropped from 48h to 8h and the full regression suite runtime fell to 17 minutes via parallel execution. Automated analytics checks produced zero tracking gaps at launch, while AI localization validation cut turnaround by 95% and prevented critical issues.
Delivered ~2,000 automated tests for iOS and Android and reached 75% automation coverage. Manual regression time dropped from 48h to 8h and the full regression suite runtime fell to 17 minutes via parallel execution. Automated analytics checks produced zero tracking gaps at launch, while AI localization validation cut turnaround by 95% and prevented critical issues.