Case Study · Software & Technology

AI-Powered QA Automation Platform

Building an AI-assisted test automation platform that generates, maintains and prioritises regression tests — cutting the manual effort that made thorough testing unaffordable.

PythonPlaywrightLLM APIsFastAPICI/CDDocker

01

The Business Challenge

A product organisation was struggling to keep regression testing in step with a fast release cadence. Manual testing couldn't cover the surface area, and conventional test automation was consuming as much engineering time in maintenance as it saved.

The goal was to keep release confidence high without slowing delivery or growing the QA team linearly with the product.

02

Existing Technology Environment

  • Rapid release cadence across web and API surfaces
  • Flaky, high-maintenance automated test suites
  • QA effort concentrated on repetitive regression checks

03

Our Approach

  1. 01Audited the existing suites to separate genuinely valuable tests from noise.
  2. 02Applied AI to the expensive parts of the QA lifecycle: generating test cases from requirements and user journeys, healing selectors when the UI changed, and prioritising which tests to run per change.
  3. 03Kept humans in the loop — AI proposes, engineers approve — so trust in the suite grew rather than eroded.
  4. 04Integrated the platform into CI/CD so every release carries evidence of what was tested.

04

The Solution Delivered

  • An AI-assisted QA platform generating and maintaining regression tests with engineer approval.
  • Risk-based test selection running the right subset of tests for each change.
  • Release dashboards showing coverage and results for every deployment.

05

Business Impact

  • [ADD METRIC] Reduced test maintenance effort by X%
  • [ADD METRIC] Cut regression cycle time from X to Y
  • [ADD METRIC] Increased automated coverage from X% to Y%
  • Release confidence maintained while delivery speed increased

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