Case Study: is an automated website quality assurance service powered by AI. In this case study, we will explore how helped notify engineers of an outage before any customers noticed, saving the business a substantial amount of money.

The Problem

Like any website, is susceptible to outages. A website outage can negatively impact the business's revenue and reputation, especially if customers are impacted. As such, it is crucial to minimize website downtime as much as possible.

The Solution was created to address this problem. The service continuously monitors website performance and identifies potential issues. If any issues are detected, sends an immediate alert to the website engineers, indicating the nature of the issue, the severity, and where applicable, the potential resolution for the issue.

The Outcome

On a typical day, monitors and analyzes the performance of several websites. One day, the service detected an issue on the business's website. Before any customers noticed the issue, alerted the engineers, who promptly took action to fix the issue. Thanks to the quick notification, the issue was resolved before any customers were affected.

By resolving the issue before customers were impacted, helped save the business a lot of money. If customers had identified the issue before the engineers even knew about it, the business could have lost customers and revenue. Moreover, discovering and resolving website issues more proactively meant that the company could dedicate more time and resources to developing the business and services, rather than being firefighting reactive issues that can be identified using automated services like

Conclusion has proven to be an invaluable solution for businesses looking to minimize website downtime and enhance website performance. By leveraging AI to analyze website performance and detect potential issues, businesses can be proactive in addressing issues before they affect customers. The result is a more stable website, fewer frustrated customers, and ultimately, a better customer experience.