AI & Data
The full domain, and the other capabilities within it.
AIOps
Fewer, better alerts across your metrics, logs and traces, and tested runbooks that act on them.
What this is
AIOps applies correlation and anomaly detection to operational telemetry so that a hundred related alerts become one incident with a probable cause. It sits on top of metrics, logs and traces, groups events that belong together, suppresses the noise, and routes what remains to the team that can act on it.
The precondition is telemetry worth analysing. Correlation across systems that do not share consistent naming, timestamps or service identifiers produces confident nonsense, and anomaly detection on a metric nobody owns produces alerts nobody actions. Most AIOps disappointments are instrumentation problems wearing an AI label.
When you need it
If more than one of these is true, this is usually the right place to start.
- A single failure produces hundreds of alerts and the on-call engineer finds the cause by reading them manually.
- Your team has stopped reading a monitoring channel because almost nothing in it requires action.
- Time to identify a cause is dominated by hopping between four consoles that do not agree with each other.
- The same incident is resolved the same way every month and the fix has never been automated.
What the scope covers
- Telemetry assessment: what is instrumented, what is missing, and whether identifiers are consistent enough to correlate at all.
- Event ingestion and normalisation across metrics, logs, traces and your existing monitoring tools.
- Correlation and noise reduction rules, tuned against your own historical incident data.
- Anomaly detection where a static threshold genuinely does not work, and static thresholds where it does.
- Runbook automation and incident routing, with auto-remediation limited to actions you have explicitly approved.
What you receive
| Deliverable | What it contains |
|---|---|
| Telemetry and alert audit | What is instrumented, which alerts fired over a recent period, and how many of them required any action at all. |
| Normalisation and correlation layer | Ingestion from your existing tools with consistent service identifiers, grouping related events into single incidents. |
| Detection and routing rules | Tuned thresholds and anomaly detection, with routing to the team that owns the affected service rather than a shared inbox. |
| Automated runbooks | Scripted responses for known failure modes, each with a defined trigger, a rollback path and an audit record. |
Reference architecture
A reference, not a template. Your estate decides which parts apply and in what order they arrive.
How success is measured
Targets are agreed with you before the work starts, and reported against for its duration.
- Alert-to-incident ratio before and after correlation, measured on the same production estate.
- Share of alerts that lead to an action, tracked as the measure of whether noise reduction is working.
- Runbook coverage: how many of your recurring incident types have a tested, approved automated response.
Questions we are asked
Will this replace our monitoring tools?
No, and it should not try. AIOps sits above your existing monitoring, ingesting its events and reducing them to something actionable. Replacing working instrumentation is expensive and rarely addresses the problem, which is usually that nothing correlates across the tools you already run.
Can it fix incidents automatically?
For known failure modes with a scripted, tested response, yes - restarting a stuck service, clearing a queue, scaling a resource. Auto-remediation against a cause the system inferred rather than confirmed is how a small incident becomes a large one. Every automated action is explicitly approved, scoped, reversible and recorded.
How much historical data does it need?
Enough incident history to tune correlation against real events, typically several months of alerts together with their outcomes. Without it the rules are guesses. If that history does not exist in a usable form, collecting it properly becomes the first phase rather than something to skip.
Our telemetry is inconsistent. Is that a blocker?
It is the first piece of work. Correlation depends on being able to say that an alert from one tool and a log line from another refer to the same service, so consistent naming and identifiers come before any model. We would rather fix that than sell detection on top of data that cannot support it.
Does this reduce headcount?
That is not a claim we make. It changes what the team spends time on: less triage of duplicate alerts, more time on causes and on the work that prevents repeats. Any staffing conclusion is yours to draw from your own numbers, and we will not put one in a proposal.
How is this different from a well-tuned monitoring setup?
For a small, stable estate, often not much, and a well-tuned threshold set is the cheaper answer. AIOps earns its place when the estate is large enough that one fault crosses many systems and manual correlation stops scaling. We will tell you which side of that line you are on.
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Start with an assessment
The fastest way to a useful answer is a short, scoped look at what you already have.