Leading Hospitality Platform
Case Study
A global hospitality technology provider needed a QA solution to cut rising tool costs and ensure reliable scalability for high-traffic booking platforms
£60k+
Saved by replacing commercial visual testing tools
1000+
Concurrent users simulated in performance tests
2x
Feedback cycles through CI/CD integration
Challenge
Hospitality platforms must deliver flawless booking and guest experiences even during seasonal traffic peaks. For this provider, quality risks emerged on two fronts:
- Visual QA Costs: The team relied on expensive SaaS tools for regression testing. While they flagged visual defects, the cost was escalating and flexibility was limited — making it hard to adapt thresholds, integrate with pipelines, or scale efficiently.
- Scalability Blind Spots: Without a dedicated performance testing framework, QA had no reliable way to simulate realistic user loads. This left bottlenecks undetected until late in release cycles, creating the risk of failures under peak demand.
Both challenges slowed releases and increased costs, while reducing confidence in platform reliability.
Solution
Illuminate QA designed a dual-track automation approach to tackle these challenges. First, we developed a custom AI-powered visual regression engine using OpenAI models. Unlike traditional SaaS tools, this gave the team fine-grained control over sensitivity thresholds, clear diff reporting, and seamless integration into their CI/CD pipelines. By replacing the commercial toolset, the provider reduced projected costs by more than £60k while gaining flexibility and ownership of the process.
In parallel, we built a reusable performance testing framework with k6 capable of simulating over 1,000 concurrent users across booking journeys. This framework supported different load profiles — from sustained load to stress and spike testing — and was fully integrated into the release pipeline. For the first time, QA had continuous visibility into latency, throughput, and bottlenecks before they reached production.
Before Automation
✘ Visual QA dependent on expensive third-party tooling
✘ No performance testing framework in place
✘ Limited visibility into scalability risks during peak load
✘ Slow feedback cycles for UI and backend stability
After Automation
✔ £60k saved by eliminating reliance on costly tools
✔ 1,000+ concurrent users simulated with k6 framework
✔ Continuous performance checks integrated into CI/CD
✔ Feedback cycles doubled in speed with automated QA pipelines
Results
By combining AI-driven visual regression with scalable performance testing, the hospitality provider transformed its QA function into a strategic enabler. Brand consistency was safeguarded through automated visual checks that no longer carried high licensing costs, while k6 load simulations delivered confidence that booking systems could withstand heavy traffic.
With both capabilities embedded into CI/CD pipelines, feedback cycles doubled in speed and release quality became more predictable. What began as a cost-saving initiative evolved into a robust, forward-looking QA foundation that now supports faster delivery, greater visibility, and long-term scalability.
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