QA services for a loan management solution

Working on a legal system is always a challenge, yet on this project, the whole setup was challenging:

• No structured test documentation, and product knowledge siloed across teams.

• No traceability between features and requirements.

• No automation.

• High dependency on manual exploratory testing.

• Frequent production issues due to a lack of regression coverage.

• The internal QA team had little to no bandwidth for process improvement or unplanned activities.

• Limited alignment between QA, development, and business teams.

The system was operational but unstable from a quality perspective, with high uncertainty around release confidence.

We introduced changes pretty quickly. First of all, the fact that the QA team had been expanded with 5 external QA engineers gave the team more capacity to tackle the whole variety of tasks that had piled up by that time, particularly process improvements, test optimization, and automation enhancements.

Our QA engineers started actively contributing to process planning by participating in backlog grooming and retrospective sessions. This way, they helped better estimate effort, identify risks early, and align scope with realistic delivery timelines.

From the outset, the team also focused on collecting and documenting system knowledge. They collaborated closely with product managers, developers, and support teams to reverse-engineer requirements from existing functionality and production incidents. This cross-functional alignment enabled the creation of a living test repository, where test cases were derived from real user flows, edge cases, and historical defects.

The newly introduced QA team played a key role in driving continuous improvement across the project. Our QA engineers proactively identified inefficiencies in existing workflows and drafted a structured improvement plan.

Also, they gradually took ownership of repetitive and high-effort QA activities, which reduced pressure on other team members, allowing developers, product managers, and other stakeholders to focus more on enhancing their specific areas rather than tackling operational bottlenecks.

As a result, the overall team became more effective, with clearer ownership boundaries, a more mature process, and increased capacity for innovation and long-term optimization.

Here’s what we did at this phase:

• Prepared reverse-engineered requirements from existing system behavior.

• Collaborated closely with developers, product managers, and support teams.

• Analyzed production defects to identify critical user flows.

• Established a structured and centralized test case repository.

• Defined testing standards, formats, and coverage strategy.

• Developed a regression suite containing 70+ test cases with detailed descriptions and steps.

• Created and expanded onboarding documentation, including 10+ detailed documents for major features, to make the learning curve easier for new team members.

• More than 10 detailed documents for major features.

As a result, releases became significantly faster and more predictable, creating a good foundation for test automation.

Using a modern test framework (Playwright) and best practices for automated testing, our AQA engineer created a robust, fast, and extensible automation suite with 100+ scenarios. This allowed the TMO team to reduce regression execution time and achieve greater testing reliability by generating dynamic test data. The new suite also included a set of visual testing tests, enabling automatic coverage of scenarios with a large amount of sensitive data.

In addition, we developed and implemented a load testing suite that helped identify load bottlenecks at the early stages of development releases and other infrastructure changes.

Both load and regression testing suites were fully integrated into CI pipelines, and the use of AI tools for pull request review reduced the time spent on editing and fixing code.

During this phase, the team focused on optimizing processes and increasing automation efficiency. Over time, test case coverage increased from an estimated 15% to over 60%, while regression execution time was reduced by 60% due to automation and prioritization strategies.

Automated test coverage grew steadily, significantly decreasing manual effort. Time to detect critical issues was reduced from hours to minutes due to CI integration and faster feedback loops.

Because the product roadmap included many new features, functional QA was still required, but not at the previous scale.