When an incident occurs and you don't know the root cause, Zebrium gives you a root cause report that describes the problem as a summary of relevant log lines and correlated metrics anomalies. No more wasted time staring at dashboards and hunting through logs to figure out what happened.
Disruptions occur because of the unexpected. Zebrium proactively scans and creates incident RCA reports that can be used to improve product quality and fix latent bugs before they manifest as production P1 incidents.
In addition, a user can optionally select any RCA report and request to be notified of future occurrences, without having to manually define and maintain detection rules.
Let an AI virtual assistant catch problems earlier and help you find root cause faster. On average, Zebrium drives down P1 incident resolution time from hours to minutes, reducing service delivery costs involved with incident response. And by detecting problems earlier and being able to catch new/unknown failure modes, Zebrium further reduces engineering costs and the burden placed on operations teams.
Traditional AIOps tools are complex and require weeks or months of training against large data sets. They rely on human-built alerts in other tools, and will only find RCA if they have previously been trained on the same problem.
Zebrium is different. It uses unsupervised machine learning directly on logs and metrics. Without any manual training or rules, Zebrium achieves reliable root cause analysis within the first day of use.