AIOps & Event Correlation
AIOps and event correlation. Reducing alert noise, grouping related events into a single incident, and automating response for the ones that repeat.
What we deliver
Hundreds of alerts from one root cause collapsed into a single incident, so the on-call engineer sees the cause rather than a list of consequences.
Suppression of repeating, self-clearing and non-actionable alerts. An alert nobody reads is worse than an alert that never fired.
The dependency map and event timeline narrow down which component an incident started in, shortening investigation time.
Defined response steps that run automatically for frequent, well-understood incidents, reserving human attention for what actually needs it.
Reporting which incidents recur and how often, and moving the recurring ones into the permanent-fix queue.
Incident volume, mean time to respond and resolve, and open-ticket age tracked on a single dashboard.
How it works
Every engagement follows the same five steps: baseline the current state, design the target model, roll out in stages, operate it, and improve against measurements.
Assess
Baseline the current state, name the gaps and put the success criteria in writing.
Design
Architect the target operating model and the toolchain it needs.
Deploy
Implement, configure and validate in a staged rollout.
Operate
24/7 management with contracted response times and proactive monitoring.
Improve
Continuous improvement driven by metrics, incidents and changes in the business.
Every engagement runs under a written SLA: a commitment, not a best-effort promise.
Dedicated engineers who know your stack. No generalist help-desk tier in between.
Service reviews every two weeks, roadmap updates every quarter.
The technologies we run this on
The concepts behind this service
- MTTR (mean time to repair)
- The average time from the start of an incident to its resolution.
This section explains the technical terms used on this page. The definitions come from Eclit's own technology glossary, and each term links through to its full entry there.
The full technology glossary →Knowledge Hub
What we have written about running and managing technology, collected in one place.
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4 min read01What does event correlation solve?
A single fault producing hundreds of alerts. When a network switch goes down, every server, application and synthetic check behind it raises its own alert; correlation collects them into one incident and surfaces the actual cause.
02Can it really find the root cause?
Largely, where a dependency map exists. Correlation uses time proximity and topology together; without topology it relies on time alone and accuracy falls. That is why dependency data is a precondition.
03Is there a risk of wrong grouping?
There is. Two independent faults happening at once can be counted as one incident. That is why we review the grouping rules, and every post-incident review also checks whether the correlation was correct.
04How long does setup take?
Connecting data sources takes weeks; getting the rules right takes months. Early on, correlation over- or under-groups, and it only delivers results once it has been tuned.
05How much does it reduce the on-call team's load?
It has to be measured, and we take that measurement before deployment. Gains typically show up in overnight alert volume and in the number of alerts reviewed per incident. They differ in every environment, which is why we measure them instead of promising a figure.
Let's work out where to start
Within two weeks you get it in writing: what works, what carries risk, and a prioritized roadmap.
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