| Sector | Fintech |
| Market | United Kingdom |
| Engaged since | February 2024 |
| Engagement | Test strategy, test asset development, AI Hub deployment |
4 days → 5 hours regression cycle
50% reduction in production defects
60% automation coverage
The Challenge
We joined this UK fintech in February 2024, before its first release. There was no test strategy, no test documentation, no regression suite — nothing to inherit and nothing to improve. Everything had to be built.
That is a different starting position to most engagements, and it carries a particular responsibility. In a product handling real-time consumer financial transactions, the test assets we wrote at the beginning would become the definition of what working meant for the platform. Get the coverage model wrong at the start and every release afterwards inherits the blind spot.
We built the test strategy, the test cases, the regression packs and the defect taxonomy from scratch, and ran them as the platform launched and grew.
Which produced the second challenge, as the platform matured. Success creates its own problem: as functionality expanded, the regression cycle we had originally designed had grown to four days of manual execution before any release could ship. The coverage was sound. The execution model no longer scaled.
The Solution
Because we had written every test asset on the platform, we knew exactly what the suite covered and why each case existed — which made it a strong candidate for AI-assisted automation rather than a risky one.
We deployed AI Hub, our intelligent testing platform, to automate the regression suite we had originally built.
AI Hub analysed the platform specifications alongside its accumulated defect history — captured through our own taxonomy since the first release — to identify where failures had clustered and where the consequence of a miss was highest. It generated the automated test cases from that analysis; our engineers, who had written the original manual versions, reviewed and approved each one. The model proposes; an experienced tester decides.
The deciding factor was maintenance. On a platform releasing this frequently, automated suites decay. AI Hub detects which tests are affected when the application changes and proposes updates, so the suite we spent two years building stays current rather than gradually becoming decorative.
Business Outcomes
- Regression cycle reduced from four days to five hours
- 50% reduction in production defects reaching users
- 60% automation coverage across core platform functionality
- Complete test asset library built from zero and maintained continuously
- Quality function sustained through every stage from pre-launch to scale
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