No fintech company wants to find out about a bug from an irate user. Here’s a quick look at what changed at this client, and how.
automated tests built
weekly automated test runs
production defects per year
reduction in production issues
That’s basically what was happening at this fintech platform before they partnered with BolsterBiz. The client is a fast-growing, US-based company in the financial wellness space, offering interest-free access to earned wages through a data-for-value model — helping underserved users get liquidity without traditional credit barriers. Good product. But the product had been live for nearly two years with no QA team, no test automation, and no documentation. Every release carried the risk of an undetected defect reaching real users.
The client brought in BolsterBiz to build a Quality Engineering function from the ground up. That engagement kicked off with an embed, build, scale approach, placing a dedicated QE team directly inside the client’s agile squads.
Since then, the client’s release process has changed a lot. Test coverage went from nothing to a full automation framework, production defects dropped sharply, and quality became something built into every sprint instead of a final step.
Industry
Financial technology (fintech)
Company size
Fast-growing, large active user base
Headquarters
United States
What they do
Interest-free access to earned wages through a data-for-value model, plus budgeting and financial wellness tools
Before BolsterBiz, the client’s release process just wasn’t built to catch problems before users did.
No QA team, no test automation, and no documentation — just a live product accumulating risk with every release.
Weak testing let 10–14 defects slip through each year, along with 5–10 hotfixes and rollbacks that disrupted the user experience.
There were no structured test scenarios and no traceability back to product stories, so quality relied on individual knowledge instead of a repeatable process.
With no safety net in place, every deploy carried the chance of an undetected defect reaching real users.
We didn’t just start writing tests. The first step was actually sitting down and looking at the architecture, the defect history, and the existing user stories to figure out where the real risk was.
From there, instead of the usual bottom-up route, we went risk-first: automating a high-risk, end-to-end user journey as a proof of concept before scaling further. We built automated smoke tests into the GitLab CI/CD pipeline so every commit triggered a quality check, then expanded into full regression coverage across web, mobile, API, and database layers.
The results spoke for themselves. Production defects and hotfixes dropped sharply, and that built enough trust to position the QE team as a long-term quality partner across all the client’s agile squads.
KPI
Target
UI test coverage
90% automated
API test coverage
100% automated
Production issues / year
< 3 (0 critical)
Hotfixes & rollbacks / year
< 3 per year
This is just the highlight reel. The full case study walks through:
