How Coherent Solutions used AI to cut QA testing effort by 35%

The challenge 

Coherent’s QA organization 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 faced constant pressure 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 our engineers’ capacity, time that could have been used for analysis and process improvements. 

Our 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 its efficiency through intelligent automation and provide a consistent framework that could be adapted across projects.

Our approach 

The Coherent QA team explored various AI tools, and their research identified OpenAI, Claude, and n8n as the most compatible platforms to integrate with our existing systems. 

A core team of engineers then began building a library of custom prompts and prototype assistants to support requirement validation, checklist and test-case creation, test planning, and regression definition. Each tool was piloted through internal projects to measure its performance and determine where AI should be applied to most effectively reduce the team’s manual workload. 

The solution

The resulting AI-enabled QA framework integrated OpenAI and Claude models with the n8n automation platform to create a connected system of assistants and agentic workflows that:

• Automate documentation and reporting by retrieving project details and generating test documentation. Updates are automatically pushed to the test management system for full visibility coverage.

• Strengthen requirements analysis by identifying improvement areas and producing targeted follow-up questions. This ensures early clarification and higher-quality inputs.

• Enhance test planning by selecting the most relevant test cases for each release. This maintains alignment with changing requirements throughout the lifecycle.

• Refine coverage analysis by detecting duplicates and evaluating test completeness. Results are tracked against defined requirements for greater accuracy.

• Streamline request handling by classifying client requests and creating predefined work items. Bug and change reports are generated automatically.

• Support team performance by analyzing backlog data and guiding estimation. The system flags potential delivery risks before they affect schedules.

• Ensure consistency and reusability through a shared prompt library and workflow templates. These shorten onboarding and standardize quality across teams.

This framework significantly reduced manual effort and improved consistency and traceability throughout the entire testing cycle.

The impact 

The AI-enabled QA framework has now become an anchor component of Coherent’s AI-infused software development lifecycle (SDLC) and a key enabler of our AI as Fabric mission, delivering measurable, repeatable results every day. Engineers have reported faster documentation, improved accuracy, broader coverage, and shorter planning times, all with less manual input.

The initiative proved that purposeful AI integration is able to improve operations, scale throughput, and create measurable digital value. 

What’s next

Following this QA AI initiative, we will collaborate with Business Analysis, Project Management, and DevOps teams to expand intelligent workflows across the entire SDLC, thereby enabling teams to deliver higher-quality software faster and more consistently.

This work reflects our commitment to practical, no-hype AI innovation. We’re committed to using AI not for its own sake, but to create digital value in how we deliver for our clients and help them succeed.