With AI, as long as you can describe the problem clearly, you can usually get a pretty good answer. I feel much more capable now. How about you?
AI in observability products usually starts from two directions:
- Analysis of the incident itself, giving users a clearer understanding of the current incident.
- Analysis of observability data related to the incident, producing conclusions for root cause investigation.
Flashduty is a one-stop alert OnCall platform. It gathers alert events from different monitoring systems and groups similar alerts into incidents, so providing analysis and summary capabilities for the incident itself is a natural fit.
Flashduty is a SaaS product we built as a startup to solve scattered alerts, alert storms, missed alerts, and related problems.
Nightingale also provides an AI Summary event processor, but that works only on a single event. In many cases, one incident triggers a group of alerts, and Nightingale cannot handle that scenario as well yet.
Flashduty has had the concept of incidents from the beginning. An incident groups many similar alerts together, which makes AI analysis more effective. For example, the incident below contains three alerts:
Click "AI Summary" and Flashduty immediately generates a summary like this:
It summarizes a set of scattered alert events, possibly from different monitoring systems, into information that humans can understand easily. The incident above contains only three alert events. If it contained 300, the value would be even more obvious.
If you are interested, you can register for free and try Flashduty: https://console.flashcat.cloud/. Productivity tools like this can genuinely improve employee well-being.
We have been building this startup for four years. Thank you to everyone who has supported us along the way. I hope today's feature is useful to you, and I wish you smooth work ahead.
