Case Studies/QA & Testing
AI-Powered Test Case Generation Reduces Manual QA Effort by 70%

AI-Powered Test Case Generation Reduces Manual QA Effort by 70%

Enterprise QA ProgrammeGlobalOngoing engagement
70%
Effort Reduced
3x
Faster Releases
KTAC
Platform Used
Auto
Regression Coverage
Case study highlights

At a glance —
the essentials.

The Customer

Enterprise QA Programme

Industry: QA & TestingRegion: GlobalDuration: Ongoing engagement
Test AutomationAI
Business Challenge
  • The client's QA team was bottlenecked writing test cases manually from lengthy requirements documents, delaying time-to-market and creating inconsistent coverage.
Our Solution
  • Kumaran enhanced its KTAC platform with an AI layer that reads requirements and user stories to automatically generate structured test cases, including edge cases human testers commonly overlooked.
Key Results
  • 70% reduction in manual test case authoring effort
  • Release cycles compressed from weeks to days
  • Automated regression suite built and maintained continuously
  • Edge case coverage improved by AI-suggested scenarios
  • Integrated directly into the CI/CD pipeline
By the numbers
70%
Effort Reduced
3x
Faster Releases
KTAC
Platform Used
Auto
Regression Coverage
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download the PDF.

The downloadable report includes the complete technical narrative beyond the highlights above:

  • Platform Specifications Hardware, OS, runtime environment, and infrastructure topology — including detailed configuration of the source and target platforms.
  • Architecture Diagrams Before-and-after system topology, data flows, integration patterns, and component interaction maps from the production deployment.
  • Project Scope Workstreams, team structure, governance model, RACI matrix, and the phased delivery plan that kept stakeholders aligned.
  • Migration Methodology Phased cutover plan, parallel-run validation harness, automated regression strategy, and rollback playbook used to de-risk production changes.
  • Performance Engineering Bottleneck analysis, tuning techniques, and benchmarking results that drove the measured efficiency gains.
  • Lessons Learned What worked, what we'd do differently, and the reusable patterns we now apply to similar modernization programs.
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