
Scalable e-commerce performance testing integrated into CI pipelines
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Our team transformed a quick-service restaurant company’s testing processes by integrating advanced automated testing solutions across API, mobile, and web platforms.
We expanded automation to hundreds of test cases, adopted low-code and AI solutions, and introduced 24/7 automated reimbursement workflows that recovered nearly $1M within 10 months.
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The quality of work of the Abstracta team has been most impressive as well as the high standards they set for themselves with regards to communication. The code is clean. They set a high bar when it comes to work ethic. They're solution-driven and seek to solve a problem best by carefully listening to their client's needs. Abstracta has exceeded my expectations. I could not have asked for a better experience with a vendor.
Quality Assurance Manager

Known for its commitment to quality and customer service, our client is one of the world's largest quick-service restaurant companies with more than $40 billion in annual system-wide sales and over 30,000 restaurants in more than 100 countries. In this project, we engage with the team that leads a global quick-service hamburger chain.
The global quick-service hamburger chain sought to improve efficiency and accuracy by transitioning from manual to automated testing. They needed a comprehensive automation framework to handle both visual and functional testing across various devices and resolutions.
This included maintaining and enhancing automated scripts, developing a robust mobile automation framework, and automating regression and smoke suites for iOS, Android, and Mobile Web platforms. Automating visual testing across four different screen resolutions was also a key objective.
Our expert team began by setting up the initial repository and automation framework. We automated the Android and iOS smoke suites, enabling comprehensive coverage across both the US and Canadian markets. We integrated the automation into the client's device farm, detailed reporting systems, and CI/CD pipeline.
Then, we expanded to automated regression suites for web, scaling it to more than two hundred test cases with Playwright across QA and Staging environments. Our team scaled visual regression across multiple resolutions and introduced efficient parallel execution across devices and browsers to accelerate feedback.
As the partnership evolved, we extended automation to API validations for third-party services and built 24/7 workflows for third-party reimbursements. We also supported the adoption of low-code automation.
As a main step, we proposed CustomGPT: an AI helper that uses the low-code automation tool API to suggest matching Gherkin-style rules, surface gaps, and guide new rule creation to reduce duplication and speed test authoring. ed.
Strengthening bonds and effective communication are key for us. That’s why, throughout the project, we conducted monthly demos to keep stakeholders updated and engaged.
We delivered a resilient automation program that accelerated releases, expanded coverage, and unlocked measurable business value.
Cut combined mobile + web regression execution to ~35 minutes (from multi-day cycles).
Achieved 100% of the planned mobile automation for the Android and iOS smoke and regression suites.
Reached 100% automation of the targeted web regression suite ahead of schedule.
Built over 200 API automations for key third-party integrations
Automated third-party reimbursement processing 24/7 with daily Slack status—~$1M recovered in 10 months.
Increased throughput with parallel execution across devices and browsers; strengthened traceability via CI/CD and reporting integrations.
Freed manual testers to focus on feature and exploratory testing, elevating overall quality.
Advanced low-code automation tool adoption (stabilization, rule reuse strategy, training) to scale participation and coverage.
Consistently delivered professional advice and direction, provided ongoing expert guidance aligned with industry practices.
Through this partnership, the client gained faster feedback loops, higher release confidence, and an automation ecosystem designed to scale with evolving priorities.
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