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Case studies

Programs from the founder’s prior work, anonymized. QualityFirstQA is a young company; these are the engagements the method was built on, and client names are shared only with permission.

Banking · QA and release leadership

A digital bank, launched on time

The situation: A bank was launching a new digital banking platform against a fixed public date, with regulatory scrutiny and no independent quality function to stand behind the release.
The work: Built and led a 20-person QA and release organization: test strategy with requirements traceability, environment and test-data planning, defect triage, release management, and a go/no-go decision backed by documented results rather than status meetings.
The outcome: The platform launched on schedule, with a release record the bank’s risk and audit functions could review.
Why it matters here: It is the origin of the go-live readiness report you see on this site, and of the view that sign-off belongs on evidence.

Semiconductor · Automation architecture

Automation across more than 100 applications

The situation: An enterprise portfolio of over 100 applications had no shared automation approach. Teams wrote tests in isolation, suites broke constantly, and nobody trusted the results enough to gate a release on them.
The work: Designed and built a Playwright and TypeScript framework from an empty repository: page objects, externalized locators, test data, CI pipelines, and deterministic quality gates at commit and pull request so standards held as the suite grew. A 10-engineer team was trained to extend it.
The outcome: Coverage reached across the portfolio, with test creation roughly 35% faster once the framework and standards were in place.
Why it matters here: It is the architecture behind Automation Build and Handover, including the guardrails that keep a suite maintainable after we leave.

Technology · AI-augmented quality at scale

Release quality across five regions

The situation: A global line of business ships on a fixed cadence, with business analysts and test teams across the Americas, Europe, Japan, Greater China, and ANZ, and a hard limit on how long regression can run before it blocks the release.
The work: Introduced AI-augmented tooling around the existing suite: generation of tests from requirements, self-healing locators that never alter assertions, an automated review agent on every test change, failure analysis from traces and network captures, and sharded parallel execution to fit the CI window.
The outcome: Failures caused by UI drift fell from about 15% to 5%, time spent testing dropped by roughly 20%, and each release ends in a documented go/no-go across all five regions.
Why it matters here: It is where the QualityFirst Loop came from: create, run, maintain, diagnose, with AI doing the heavy lifting and people making the call.

Numbers above are from the founder’s prior engagements, measured against each program’s own baseline. Client names, systems, and internal details are kept confidential, as yours would be.

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