This blog post will uncover how ilert status pages work, the challenges we encountered while developing this feature, and the problem-solving approaches we adopted.

My name is Tim Gühnemann, and as an AI engineering working student at ilert, I had the privilege of developing and continuous improving ilert AI, ensuring it meets the needs of our customers and aligns with our vision. ‍ Our goal was to provide all our customers with access to ilert AI. We aimed to develop a solution that could adapt dynamically and function independently based on our use cases, similar to the OpenAI Assistant API.

At ilert, one of the key tools in our debugging process is the Event Explorer, which provides an extensive overview of incoming events and their processing lifecycle. By reflecting the event process of an alert source, the Event Explorer allows our team to trace event paths, correlate related data, and identify issues quickly.

We are excited to announce a native integration between ilert and Honeybadger.

With increasing numbers of alerts, keeping focus on the important and most critical alerts proves to be more and more of a challenge. A reduction of alert noise, meaning the prevention of too many created alerts and any kind of user notifications, is needed to ensure efficient alert response. While a detailed explanation of this topic is given in this blog post, a flexible and automated setup for your relevant resources can be achieved with Terraform using the ilert Terraform provider.