Intelligence-led QA
Shift from manual analysis to data-driven quality engineering
The challenge
A leading global insurer was facing increasing complexity across its software development lifecycle (SDLC), with traditional testing approaches struggling to keep pace with the speed and scale of change.
Test design remained heavily manual, requiring significant effort to translate requirements and user stories into test cases while maintaining consistency and coverage. Although test automation was established, creating and maintaining automated tests remained resource-intensive, particularly as applications evolved.
The challenge became even greater during execution and regression testing. Large, complex test repositories made it difficult to quickly identify the right test cases for a given change, increasing effort and creating the risk of gaps in coverage. Release cycles were further impacted by high volumes of failed automated tests, where manual failure analysis had become a time-consuming bottleneck that delayed defect resolution and slowed delivery.
The solution
Expleo partnered with the client to embed AI-driven quality engineering capabilities across the SDLC, integrating intelligence into existing testing processes and tools, including Jira, Xray, Tosca and established automation frameworks.
AI-supported requirement-to-test transformation enabled the automated generation of user stories, test scenarios, acceptance criteria and manual test cases from requirement documents, creating a more consistent and traceable foundation for testing.
To improve regression and execution activities, Expleo introduced AI-powered search and analysis capabilities that helped teams quickly identify relevant test cases and navigate large regression repositories. AI-driven failure analysis solutions were also deployed to provide centralised visibility into test results, automate root-cause classification and analyse trends across large automation suites.
Together, these capabilities reduced manual effort and improved the efficiency and resilience of testing across frequent release cycles.
The outcome
The organisation transformed quality assurance from a reactive, labour-intensive function into an intelligence-led engineering capability.
AI-driven requirement-to-test automation reduced manual test design effort, improved consistency and strengthened traceability between requirements, test scenarios and execution.
AI-powered regression support enabled teams to quickly identify relevant test cases within large repositories, improving test selection and reducing the risk of missed scenarios.
The greatest impact came from AI-driven failure analysis. Automated root-cause categorisation achieved around 99% production accuracy, analysing more than 14,800 failures and classifying over 14,600 defects. This significantly reduced manual investigation and accelerated defect resolution.
The result was higher testing efficiency, improved coverage and traceability, faster issue resolution, and a scalable, data-driven quality engineering model supporting complex global insurance platforms
From requirements through to release, AI transformed software quality from manual effort into an intelligence-led capability.
How did we help?
The global insurer required a partner capable of combining deep quality engineering expertise with practical AI innovation in a highly regulated environment.
Expleo designed and deployed AI capabilities that integrated directly into the organisation’s existing testing landscape, creating an end-to-end capability spanning requirements, test design, execution, regression and failure analysis.
Expleo delivered a scalable quality engineering model that continues to generate value across multiple release cycles and testing domains.
What made this uniquely Expleo
End-to-end AI-enabled quality engineering across the SDLC
Automated transformation from requirements to test cases
AI-powered regression intelligence and natural-language search
Production-grade root-cause analysis achieving ~99% accuracy
Deep integration into existing enterprise testing ecosystems
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