For fast-moving tech companies, nothing feels more frustrating than knowing your product is ready but your release process is not. Time gets eaten up by repetitive checks. QA teams get slower. And every new feature launch feels like a gamble.
If you’ve ever felt held back by slow test cycles or limited coverage, this story will feel familiar.
In this case study, you’ll see how Boostlingo, an AI-powered interpretation platform, improved its testing workflow. We'll walk through the challenges they faced, the steps we took together, and the results that now let them ship new features faster, safer, and smarter with every release.
Boostlingo is a platform built for one purpose, breaking down language barriers at scale. They offer on-demand interpretation, multilingual event support, and AI-powered captioning in over 300 languages. Their solution helps healthcare systems, legal teams, and enterprises provide inclusive communication globally.
But while their product was helping the world communicate faster, their internal QA workflows were holding them back.
Here’s what wasn’t working:
After analyzing Boostlingo’s testing challenges, we outlined a plan to address each problem with targeted, scalable solutions. Here’s what we proposed:
“I honestly did not know what to expect. This is very inspiring. Great job.” -Jake Orona, Senior QA Lead Engineer (Boostlingo)
We started by replacing their legacy WebdriverIO setup with Playwright and TypeScript. This upgrade offered improved performance, better debugging tools, and more flexibility.
During the migration, we focused on script readability, modular structure, and long-term maintainability. A key challenge was ensuring feature parity, translating older tests without losing coverage. We also introduced standardized coding patterns to reduce inconsistencies and made sure the new stack was aligned with their existing CI/CD workflows.
Next, we built a scalable test architecture to handle the growing number of user roles and workflows. We created reusable helper functions and page object models, making the suite easy to extend without rewriting logic. To prevent technical debt, we added linting, folder-level organization, and custom logging.
We ensured that any team member, technical or not, could understand how tests were built, maintained, and extended. This architecture became the foundation for future coverage expansion.
One of the biggest hurdles was testing real-time audio/video call flows. These required mic and camera access, which can’t easily be automated using real hardware.
We overcame this by integrating fake media streams and simulating signaling systems to mimic real-world behavior. This allowed full automation of call flows, even under edge-case conditions. We also tested under various browser/device combinations to ensure reliability. We made sure simulations worked consistently across environments and CI pipelines.
To scale faster, we used tools like GitHub Copilot and Cursor. These AI agents helped write test scripts, repetitive test cases, clean up old logic, fix typos, and suggest optimized code.
They were especially helpful for bulk editing across files, saving hours of effort. We still reviewed every suggestion for accuracy, but using AI meant we could focus more on complex logic instead of boilerplate code. The result was 25% faster delivery without compromising code quality.
Finally, we implemented test sharding, allowing the suite to run in parallel across multiple worker threads. This brought down execution time from 6 hours to under 2.
We integrated this setup into their CI to ensure real-time feedback on pull requests.
While setting this up, we made sure tests were isolated and stateless, so they wouldn’t interfere with each other when run in parallel. This step was really crucial to save time and money for our client. As a result, they can execute faster without spending more.
You’ve just seen how ThinkSys helped Boostlingo move from a slow, manual QA process to a fast, intelligent, and fully automated testing pipeline. If you’re struggling with long release cycles, limited test coverage, or outdated tools, you’re not alone, and you don’t have to keep pushing through the pain.
ThinkSys helps growing teams test smarter, ship faster, and scale with confidence. If these challenges sound familiar, let’s talk about how we can help you next.
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