Description
Selector AI leverages AI and ML to create consolidated, contextualized alerts that point your team towards the root cause of an incident.
Video length: 02:06
Transcript
Narrator: Alerting and notification strategies have long been challenges for network and IT operators. Fire alerts too quickly and often you contribute to alerting fatigue. Wait too long and customers inform you of problems before your NMS does with trouble tickets. With Selector’s alerting these considerations become a thing of the past. AI and ML based strategies to enable you to get your alerting right without defining fixed rules. For time-series-based alerting, Selector creates a baseline automatically. When anomalous behavior is detected, a violation is created as an event, along with related context. For log-based alerting, Selector leverages its patented log miner to identify the patterns, context, and severity of issues present within your logs. When log behavior changes, Selector similarly captures the resulting violations and context as events. Selector constantly analyzes the submitted violations and leverages a combination of recommender and associative machine learning models. Leveraging these technologies, Selector is able to identify the event that precipitated an incident, along with the subsequent fallout. The resulting root cause and correlated events are then fed through a GenAI LLM facility, contextualizing the information as a detailed, actionable alert that points your team towards the real source of the problem so you can get things up and running quickly. Along the way, multiple events were consolidated down into one singular alert, helping to reduce the overall number of alerts generated. The net result: better, more actionable alerts, alert consolidation, and a team better equipped to rapidly respond to alerts rather than to ignore them. To stay up to date with features and exciting use cases, visit selector.ai and sign up for our mailing list.