A thousand alerts a day across the monitoring estate. A team that correlates by instinct and escalates by email. Runbooks that are PDFs nobody reads. This is what happens when operations tooling scales but operations intelligence doesn't.
The industry's answer has been machine-learning AIOps: feed the alert firehose to a model and let it guess at groupings. It works until the moment you need to explain a grouping to an auditor — or until the model correlates two incidents that share nothing but a timestamp.
Deterministic beats statistical — when the graph is true
There's another way to correlate: by the real dependency graph. If the CMDB knows that service A runs on cluster B behind load balancer C, then events on A, B, and C group together because they are actually related — not because an anomaly score said so. Every grouping is explainable. Every root cause is traced through dependencies that exist, with an evidence trail per incident.
That's the design principle behind ORCA, the correlation engine at the heart of AORBIT™'s Intelligence layer: deterministic, CMDB/CSDM-driven, explainable — no black box.
The catch — and the payoff
- The catch: deterministic correlation demands a truth layer that is actually true. A hand-maintained CMDB is wrong by Friday. The graph has to be built from runtime reality — discovered, auto-modeled, reconciled continuously.
- The payoff: the truth-layer investment shows its return in operations — measurable, weekly, in MTTR. When dependencies are true, incidents resolve faster.
- The posture: run alongside the ITSM you already have. Correlate and diagnose here, hand tickets off there. Never rip-and-replace.
ITOPS to AIOPS isn't a tooling upgrade. It's the moment operations intelligence starts reading from a graph of what's actually running — and the moment every resolution feeds back into that graph, so the system gets better with use.