Discover how Coherent Solutions built SPARK, an AI-powered enterprise knowledge platform that transforms internal data into measurable business value.
As part of a company-wide AI Performance Execution (APEX) transformation initiative, and using its AI as Fabric methodology, 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.
The challenge
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.
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 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.
Instead of using AI to just increase delivery speed, the team integrated the technology into redesigned QA workflows, so what they learned in one cycle carried over to the next.
By grounding the language models in historical test artifacts, defect data, and prior execution outcomes, the team enabled the system to surface recurring risks, inform scope decisions, and adapt regression coverage as requirements evolved.
As the workflows matured over time, QA work shifted away from redoing artifacts toward applying judgment based on automated system insights.
This approach laid the foundation for principles later formalized in Coherent Solutions’ Continuous Delivery Loop (CDL). CDL is an intent-first delivery model in which human expertise and AI work together to retain context, apply learning, and improve outcomes across delivery cycles, rather than resetting at the start of each sprint.
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:
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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.
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Strengthen requirements analysis by identifying improvement areas and producing targeted follow-up questions. This ensures early clarification and higher-quality inputs.
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Enhance test planning by selecting the most relevant test cases for each release. This maintains alignment with changing requirements throughout the lifecycle.
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Refine coverage analysis by detecting duplicates and evaluating test completeness. Results are tracked against defined requirements for greater accuracy.
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Streamline request handling by classifying client requests and creating predefined work items. Bug and change reports are generated automatically.
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Support team performance by analyzing backlog data and guiding estimation. The system flags potential delivery risks before they affect schedules.
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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.
By embedding AI directly into workflows rather than using it to improve individual tasks, the team integrated AI into its delivery fabric. This allowed the technology to function as a crucial automation and support partner, delivering consistent, reliable results.
The impact
The AI-enabled QA framework has now become an anchor component of Coherent’s AI-infused SDLC and a key enabler of its 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.
Each delivery cycle required less manual effort, which wasn’t just due to automating tasks. Manual effort also lessened as the AI-powered systems transferred knowledge between projects. Over time, quality became more predictable as feedback loops tightened.
|
Metric |
Improvement |
Description |
|
Test-case writing effort |
↓ 50% |
Faster review cycles and shorter documentation time |
|
Speed of test documentation |
↑ 60% |
Automated generation and standardized structure |
|
Edge scenario coverage |
↑ 20% |
Broader test robustness through contextual AI prompts |
|
Test-case clarity |
↑ 30% |
Higher readability and less duplication |
|
Generation accuracy |
↑ 45% |
Reliable outputs with minimal input |
|
Test coverage expansion |
↑ 35% |
Broader QA visibility per project scope |
|
Test planning time |
↓ 25% |
More throughput per QA engineer |
|
Document review accuracy |
↑ 30% |
Stronger validation and completeness |
The initiative proved that purposeful AI integration can improve operations, scale throughput, and create measurable digital value. The real value of this initiative emerged in more predictable releases, reduced operational volatility, and delivery gains that compound over time.
What’s next
Following the QA initiative, Coherent 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 the company’s commitment to practical, no-hype AI innovation. Coherent is committed to using AI not for its own sake, but to create real digital value, delivering results for each client, and helping them succeed
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