Find the Real Problem — Not Just the Symptoms
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.
What RLM Delivers on Automated Root Cause Analysis
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.
How We Approach Automated Root Cause Analysis
We work Automated Root Cause Analysis the same way each time: establish the baseline, test the market properly, negotiate on evidence, and stay involved through implementation.
MTTR Baseline Assessment
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.
Topology Model Design
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.
RCA Platform Evaluation
We evaluate RCA capabilities across AIOps platforms — Dynatrace Davis AI, Moogsoft, BigPanda, PagerDuty AIOps — against your stack's specific topology and alerting patterns.
Integration with On-Call & Escalation
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.
Automated Root Cause Analysis Selection Criteria
These are the dimensions we have seen separate a Automated Root Cause Analysis deployment that works from one that quietly becomes shelfware.
Root Cause Accuracy
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.
Time to First RCA
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.
Topology Coverage
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.
Multi-Layer Correlation
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.
Evidence Quality
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?
Learning from Feedback
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.
"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."
No upfront fees, no retainer, and no obligation to act on what we find.
Where This Matters Most
Sector-specific considerations we see repeatedly in ai and automation engagements.
A Sample of the AI & Automation Providers We Evaluate
RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →
Where Do You Want to Start With Automated Root Cause Analysis?
Start with a no-cost conversation with an RLM AI advisor — vendor neutral, no agenda, just clarity.
Speak to an Advisor