{"id":130333,"date":"2026-05-28T12:27:00","date_gmt":"2026-05-28T15:27:00","guid":{"rendered":"https:\/\/bluestudio.estadao.com.br\/agencia-de-comunicacao\/releases\/releases-geral\/testmu-ai-helps-fyscaltech-reduce-test-execution-time-by-60-and-reclaim-over-600-engineering-hours-monthly\/"},"modified":"2026-05-28T12:27:00","modified_gmt":"2026-05-28T15:27:00","slug":"testmu-ai-helps-fyscaltech-reduce-test-execution-time-by-60-and-reclaim-over-600-engineering-hours-monthly","status":"publish","type":"post","link":"https:\/\/bluestudio.estadao.com.br\/agencia-de-comunicacao\/releases\/releases-geral\/testmu-ai-helps-fyscaltech-reduce-test-execution-time-by-60-and-reclaim-over-600-engineering-hours-monthly\/","title":{"rendered":"TestMu AI Helps FyscalTech Reduce Test Execution Time by 60% and Reclaim Over 600 Engineering Hours Monthly"},"content":{"rendered":"

San Francisco, CA, May 28, 2026 (GLOBE NEWSWIRE) — TestMu AI<\/u><\/a> (previously LambdaTest<\/u><\/a>), the worlds first full-stack Agentic AI Quality Engineering platform, today announced the results of its collaboration with FyscalTech<\/u><\/a>, a large-scale fintech company serving over 150 million users across distributed microservices-driven platforms.<\/p>\n

By adopting TestMu AIs KaneAI and HyperExecute, FyscalTech achieved approximately 60% faster test execution, reduced regression cycles from 610 hours to under one hour, and reclaimed more than 600 engineering hours every month across QA and validation workflows.<\/p>\n

As FyscalTech’s ecosystem grew in scale and complexity, its testing infrastructure began to show its limits. Regression cycles stretched longer, release decisions slowed, flaky automation eroded engineering trust, and the operational overhead of maintaining test stability climbed steadily. During high-traffic periods and major releases, the lack of fast, reliable feedback was directly impacting engineering velocity, release confidence, and the very capability FyscalTech is known for delivering at speed.<\/p>\n

To address these challenges, FyscalTech implemented HyperExecute for large-scale parallel execution and KaneAI for AI-native test creation and debugging workflows.<\/p>\n

With HyperExecute, the team significantly compressed regression execution timelines by running hundreds of tests in parallel across distributed environments. Full regression cycles that previously took most of the workday are now complete in under one hour, enabling same-day validation and faster release readiness.<\/p>\n

Simultaneously, KaneAI transformed the teams testing workflow by enabling natural-language-based automation and contextual failure analysis. Engineers were able to create and maintain tests faster, reduce maintenance overhead, and quickly understand execution failures without manually investigating raw logs.<\/p>\n

Modern engineering teams cannot afford slow or unreliable validation workflows, especially in highly regulated and high-scale environments like fintech, said Mudit Singh, Co-Founder and Head of Growth at TestMu AI. With KaneAI and HyperExecute, FyscalTech was able to accelerate feedback loops, improve confidence in automation, and free engineering teams to focus on higher-value quality initiatives instead of managing infrastructure complexity.<\/p>\n

Key outcomes from the implementation include:<\/p>\n