A GenAI-driven test automation platform generating, self-healing and prioritizing tests directly from requirements and user stories.
Modern software teams ship continuously, but quality assurance often becomes the bottleneck—manual test creation is slow, automated scripts are brittle, and regression cycles stretch release timelines.
A fast-growing digital commerce company set out to modernize its testing function with generative AI, accelerating test authoring and execution while expanding coverage and reducing the maintenance burden that had been consuming engineering capacity.
The client is a fast-growing digital commerce company delivering a feature-rich digital platform with frequent, iterative releases.
Its engineering and QA teams were responsible for safeguarding quality across a complex web application spanning critical user journeys such as onboarding, checkout, and payments.
As release velocity increased, the existing automation suite struggled to keep pace, and a growing share of engineering time was spent maintaining brittle tests rather than building new coverage.
The company needed an AI-driven approach to testing that could scale with its release cadence.
Every operational bottleneck reported was matched to the workstream(s) that resolved it.
Writing and updating test cases by hand could not keep pace with frequent releases, creating a QA bottleneck and delaying delivery.
Generated test cases and executable scripts directly from requirements, user stories, and application flows, drastically reducing manual authoring effort.
UI and locator changes routinely broke automated tests, so engineers spent more time fixing tests than expanding coverage.
Automatically adapted tests to UI and locator changes at runtime, reducing breakages and maintenance overhead.
Untested paths across critical journeys allowed defects to reach production, eroding user trust.
Identified untested paths and continuously improved test completeness across critical journeys.
Full regression runs took days, slowing releases and extending time-to-market.
Ran the highest-value tests first within CI/CD pipelines, surfacing critical defects earlier in the cycle.
As the application grew, test maintenance became increasingly costly and difficult to scale with the existing approach.
Built to integrate with existing pipelines using LLM-driven test generation, parallel execution, and standard automation frameworks for fast, reliable feedback.
Allowed both QA engineers and non-technical contributors to define and review tests in plain language.
AI-generated cases and scripts compressed authoring time and accelerated coverage expansion.
Self-healing reduced test breakages, freeing engineers to focus on building rather than fixing tests.
Coverage analytics closed gaps across critical journeys, catching defects before production.
Faster, prioritized regression runs accelerated releases and improved confidence.
QA shifted from script maintenance to exploratory and risk-based testing.
Regression cycle time was likely reduced from around 3 days to under 6 hours.
Release frequency and confidence likely improved as QA shifted from maintenance to exploratory testing.
