As part of a company-wide AI-native software delivery transformation initiative, and using its Continuous Delivery Loop operating framework, Coherent Solutions modernized its global Quality Assurance (QA) customer delivery function using AI tools and workflows, improving productivity for more than 360 QA engineers across nine countries.
By combining OpenAI and Claude models with the business process automation platform n8n, the Coherent Solutions QA team built custom AI assistants and automated workflows that enhanced requirements review, test documentation, bug reporting, and performance analysis. Within just a few months, the pilot reduced test-case writing effort by 50% while increasing documentation speed by 60%.
The result was not just faster execution, but an early shift toward AI-native delivery, where quality improves through compound engineering rather than repeated effort.
Coherent’s QA team includes more than 360 engineers across nine countries, each working in complex project environments and utilizing a diverse range of tools. As client expectations for faster releases and greater reliability grew, the QA team needed to expand test coverage and improve accuracy without extending delivery timelines.
Additionally, manual processes were creating inefficiencies and inconsistencies in the test case process. Even routine activities, like regression testing and documentation, consumed far too much of the engineers’ capacity, time that could have been used for analysis and process improvements.
AI tools were available, but inside Coherent’s traditional software development lifecycle (SDLC), they mostly sped up individual steps without improving how the entire delivery workflow functioned. Each sprint felt like starting over, which made it hard to scale or build on what teams had already learned.
Before embedding AI, Coherent’s QA outcomes depended largely on tribal knowledge. It was difficult to improve systems and processes over time because each team took their knowledge and improvements with them at the end of projects.
The delivery leadership team recognized that scaling through additional headcount or extended hours wouldn’t solve the problem. The team needed a solution that could improve efficiency through intelligent automation and provide a consistent framework that could be adapted across projects.