Imagine your entire IT infrastructure—servers, networks, apps—constantly watched by a very fast, very smart assistant that never sleeps. It notices tiny warning signs before humans can, connects dots across thousands of alerts, and either fixes issues automatically or tells your team exactly where to look.
Traditional IT operations teams are overwhelmed by massive volumes of logs, alerts, and metrics across hybrid/cloud environments. They spend too much time firefighting incidents, manually correlating alerts, and chasing false positives, which leads to longer outages, higher costs, and poor user experience. AIOps applies analytics and machine learning to automate detection, correlation, and remediation so IT can scale without linearly adding people.
Moat typically comes from deep integration into existing IT/monitoring stacks, proprietary incident/operations data used to train models, and sticky workflows embedded in NOC/SRE processes (playbooks, runbooks, automation hooks).
Hybrid
Time-Series DB
High (Custom Models/Infra)
High-volume ingest and real-time analysis of logs/metrics/events at scale, plus model performance and noise reduction across heterogeneous IT data sources.
Early Majority
Positioned as an AI-powered layer on top of existing monitoring/logging stacks that focuses on correlating events, detecting anomalies, and automating remediation for IT operations, rather than being just another monitoring or logging tool.
22 use cases in this application