AI applied to operations: fewer pages, faster fixes
Kitecorp applies machine learning and language models to the operations data you already have: anomaly detection on Prometheus and Grafana signals, log and metric intelligence that turns thousands of lines into one cause, and incident automation that carries out the safe actions and asks a person for the rest. The measure is pages avoided and minutes to recovery, not models deployed.
What does an AIOps engagement deliver?
Anomaly detection on the signals that matter, trained on your own seasonality so a Monday morning ramp is not an incident and a quiet Saturday with half the usual traffic is. Correlation across metrics, logs and traces that turns an alert storm into one incident with a probable cause and the evidence for it. A summarisation layer that drafts the incident timeline and the first communication while the on-call engineer is still reading. Automated actions for the cases with a known, safe response, with a person in the loop for everything else. And the measurement: pages per week, time to acknowledge, time to recover, before and after.
Where does AI help operations today, and where does it not?
It helps where the data is rich and the pattern is subtle: detecting that error rate and latency moved together on one node pool before any threshold fired; grouping two hundred log lines into three causes; drafting the timeline a human would spend an hour reconstructing. It does not help where the data is thin, where the action is irreversible, or where the organisation has not decided what “normal” means. We say which of your problems fall on which side in the assessment, and the ones on the wrong side get a conventional fix.
How do we detect anomalies on Prometheus and Grafana signals?
By modelling each signal’s expected range from its own history, including the daily and weekly cycles, and alerting on departures from that range rather than on fixed thresholds. The models run against Prometheus time series, write their expectations back as recording rules so Grafana shows the band alongside the line, and alert through the Alertmanager you already use. Correlation sits on top: an anomaly is promoted to an incident only when related signals (the same service’s error rate, its upstream’s latency, a deploy event) move with it. False positives are tracked as a metric and the models are retuned against them.
How do we automate incident response safely?
With a short list of actions that have been written as runbooks, tested, and approved once by the people who own the service: restart a crashed pod group, scale a stateless service within a bound, fail traffic over from a region that is already marked unhealthy, roll back a deploy that correlates with the anomaly. Each action checks its preconditions before it runs and records what it did in the incident. Anything not on the list arrives as a recommendation with the evidence attached, and a person decides. The list grows only when a recommendation has been accepted unchanged enough times that automating it is a safe bet.
How does an engagement run?
A discovery call to understand the services, the on-call load and the signals you already collect. An assessment of the data and the pain, with the anomaly, correlation and automation candidates ranked and the ones that need better observability first named. The implementation with your team, measured in pages and recovery time from the first month. A handover with the models’ tuning runbooks, the approved action list and the dashboards. Pricing follows the discovery call, once we know the scope.
Frequently asked questions
Will AIOps replace our on-call rotation?
No. It removes the pages that a rule could never express and the hours spent reading logs, and it carries out the actions you have already approved. A person still decides anything that can break production.
Do we need new monitoring tools?
Usually not. The work starts from the Prometheus, Grafana, logs and traces you have. If the signals are missing, that is an observability engagement first; AI on bad data produces confident nonsense.
How do you keep automated actions safe?
Each action is a tested runbook with preconditions, a blast radius and a rollback, approved once by a person and run by the system only when its preconditions hold. Anything outside the list becomes a recommendation, never an action.
Do you work with our existing team?
Yes. Every engagement is delivered with your engineers and ends with a handover, runbooks and alerts they own.
How do we start?
Send a message through the contact page. We schedule a discovery call, agree the scope, and pricing follows what we find.
Tell us what you are scaling
Send a short description of the system and the deadline. A senior engineer replies by email, and pricing follows a discovery call.
Contact Kitecorp or email admin@kitelang.cloud