QA Levelling

Real engagements. Real outcomes.

Three case studies from QA Levelling engagements. Named clients, verifiable contacts, honest outcomes — described by what changed, not by metrics we cannot stand behind.

Case Studies: Building High-Yield Revenue Infrastructure

Case study 1

The Test Stabilisation: Preservica

PRESERVICA

Company: Preservica — preservica.com
Industry: Enterprise SaaS / Digital Preservation
Engagement Lead: Diane Kissane, Senior Test Manager

Engagement Type: The Optimisation & Excellence Pillar™

The Challenge:

Preservica operates at a level of quality obligation that most software teams never face. Their platform is trusted by institutions including the British Library, Yale University, and Transport for London to preserve irreplaceable digital records over decades. In that environment, a regression is not a bad sprint — it is a threat to institutional trust built over years.

 

When QA Levelling engaged, the test infrastructure had not kept pace with the product's growth. The existing automated suite had become unstable and inconsistent, gradually losing the engineering team's confidence. New product features were shipping without automated coverage, increasing manual overhead and unquantified release risk with every cycle. Quality had no structured ownership — it was reactive, unplanned, and fragmented across the team.

The Intervention:

QA Levelling took on both the technical execution and the quality leadership layer simultaneously. The first priority was stabilising the existing suite — diagnosing genuinely flaky tests from those exposing real intermittent defects, then systematically resolving both. Once existing coverage could be trusted, attention moved to the manual test backlog: identifying the highest-risk unautomated flows and converting them into durable, maintainable automated tests. As new product features entered development, QA Levelling embedded into the planning cycle to ensure automation was written alongside each feature — not retrofitted after release.

The Result:

Preservica left the engagement with a test infrastructure the entire team could rely on. Unstable, unpredictable tests were replaced with consistent, deterministic coverage. Manual regression overhead per release was substantially reduced. New features shipped with automated tests already in place, and the team had a clear ownership model and planning rhythm around quality that had not previously existed.

The Results (90 Days)

3x increase in release velocity

Testing time dropped to 10 mins

85% Automation coverage.

“We went from 4-week release cycles to shipping every week with total confidence.”

David Varkey

CTO, CloudMetrics

Case study 2

The Quality Infrastructure Build: Bitsler

BITSLER

Company: Bitsler — bitsler.com

Industry: Crypto Casino & Sports Betting (iGaming)

Engagement Lead: Michael Michelin, Managing Director

Engagement Type: The Foundation & Readiness Pillar™

The Challenge:

Bitsler operates across three distinct and high-stakes product surfaces: crypto casino games, a sports betting platform, and a cryptocurrency wallet. In iGaming, tolerance for production defects is exceptionally low. A bug in a betting calculation or a wallet transaction is not a UX inconvenience — it is a financial and regulatory exposure.

 

When QA Levelling engaged, there was no formal QA workstream in place. Testing was informal, inconsistent, and entirely dependent on developers catching their own errors. There was no automation, no traceability, and no structured process connecting quality to the development lifecycle. The product was scaling without any quality infrastructure beneath it.

The Intervention:

QA Levelling built the quality infrastructure from the ground up. The first stage established the foundations: a formal QA process, a clear definition of done from a quality perspective, and full traceability between requirements, test cases, and outcomes. An automation framework was then introduced — engineered specifically for the complexity of a platform spanning casino game logic, real-money sports betting, and live crypto wallet transactions. Shift-left principles were embedded into the development workflow, moving quality consideration to the planning stage rather than the end of the cycle.

The Result:

Bitsler moved from no formal QA process to a structured, automated, and traceable quality infrastructure covering their entire product estate. The development team gained confidence that changes to one product surface were not silently breaking another. Release risk — previously unquantified and entirely unmanaged — became visible and controllable for the first time.

The Results (90 Days)

92% reduction in Hotfixes

Saved 18 hours/week for engineers.

Defect rate dropped to 8%

“Since implementing the QAL Protocol, our engineers aren’t waking up to hotfix alerts.”

Marcus Rodriguez

Founder, DataFlow Analytics

Case study 3

The Framework Recovery: QueryClick

QUERYCLICK

Company: QueryClick — queryclick.com

Industry: Digital Marketing & SEO Technology

Engagement Lead: Simon Tapson, Head of Product

Engagement Type: The Optimisation & Excellence Pillar™

The Challenge:

QueryClick had already invested in building a test automation framework — and then watched confidence in it collapse. Tests failed inconsistently, results could not be trusted, and the team had stopped relying on the suite to inform release decisions. An automation framework that nobody trusts is worse than no framework at all: it creates the illusion of coverage while delivering none of the protection.

 

By the time QA Levelling engaged, the framework had been left largely dormant. The question was not whether to invest in automation — that decision had already been made. The question was whether the existing investment could be recovered, or whether it needed to be written off entirely.

The Intervention:

QA Levelling conducted a thorough assessment of the existing framework to determine what was structurally sound and what required rebuilding. Rather than discarding the prior investment, the framework was recovered, repaired, and restored to a reliable, fit-for-purpose state. Once stability was re-established, focus shifted to optimisation: streamlining the architecture, improving maintainability, and making the suite genuinely scalable as the product continued to grow. Test coverage was then extended systematically, prioritising the highest-risk flows first, and the release cycle tightened as a direct result.

The Result:

QueryClick's engineering team regained trust in their automated test suite. What had been an abandoned liability became an active asset in the release process. Test cycles shortened, coverage expanded, and the team could deploy with a level of confidence the framework had never previously delivered.

The Results (90 Days)

2x increase in development throughput

100% Audit-Ready Documentation

Zero hiring/recruitment overhead

“QAL provided the strategic leadership and resources we needed for a fraction of the cost. Our dev velocity has doubled.

Jennifer Park

VP of Engineering, SalesBoost AI

Hear It Directly From Our Clients

Sarah Chen

CloudMetrics

Sarah Chen

CloudMetrics

Sarah Chen

CloudMetrics

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