Automated root cause analysis uses topology-aware AI to identify the underlying cause of IT incidents in minutes — correlating hundreds of related alerts into a single root cause, eliminating the manual investigation that extends MTTR and frustrates both IT teams and end users.
When systems go down, every minute of MTTR has a business cost. Traditional alert-based troubleshooting requires experienced analysts to manually correlate events across siloed tools. Automated RCA compresses that investigation from hours to minutes.
Every engagement follows a structured process — from discovery and vendor evaluation to pilot design and scale — adapted to the specific constraints and maturity of your organization.
We establish your current MTTR baseline across incident categories — measuring the investigation time component that automated RCA would eliminate — to build the ROI case for investment.
Automated RCA requires an accurate model of your infrastructure dependencies — services, applications, hosts, network paths. We design the topology model that gives RCA the context to identify true root causes.
We evaluate RCA capabilities across AIOps platforms — Dynatrace Davis AI, Moogsoft, BigPanda, PagerDuty AIOps — against your stack's specific topology and alerting patterns.
Automated RCA creates value when it reaches the on-call engineer quickly. We design the integration with on-call management (PagerDuty, OpsGenie) that delivers RCA findings at the moment of escalation.
These are the evaluation dimensions that consistently separate successful deployments from expensive pilots that never reach production scale.
False root cause identification sends engineers down the wrong path — extending MTTR rather than reducing it. Validate accuracy on your historical incidents before production deployment.
How quickly does the platform identify a root cause candidate after incident detection? Evaluate against your incident timeline data — early RCA candidates have the most MTTR impact.
RCA accuracy depends on comprehensive dependency modeling. Evaluate how the platform discovers and maintains topology — auto-discovery vs. manual configuration, CMDB integration, and dynamic topology for cloud environments.
Incidents often span infrastructure, application, and network layers. Evaluate the platform's ability to correlate events across layers rather than producing multiple single-layer root cause candidates.
Beyond identifying a root cause, does the platform provide the supporting evidence — correlated metrics, logs, topology visualization — that lets engineers verify the RCA and act confidently?
RCA accuracy improves as engineers confirm or reject automated root causes. Evaluate the feedback mechanism and how quickly the platform incorporates corrections into future analysis.
"RLM brought structure to a process we didn't know how to start. They asked the right questions, surfaced the right vendors, and kept us from making decisions we would have regretted."
"What set RLM apart was that they didn't have a preferred answer. They evaluated our options honestly and told us what they actually thought."
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