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The failure gives signals before it happens. AIOps acts on them.

Monitoring, logs, metrics, and events become a single signal. AI detects the anomaly, groups the noise, points to the root cause, and triggers the fix before the service goes down.

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up to 90% reduction in alert noise
40–60% less MTTR with automated root cause
24×7 continuous detection and remediation
How AIOps works

Five steps that turn noise into action.

From raw data to resolved incident, AIOps closes the loop of detection, correlation, and remediation without manual intervention for known cases.

01

Collection

Logs, metrics, events, and traces from any tool arrive via ready-made connectors or API.

02

Baseline

The model learns the normal behavior of each service by time of day, day, and seasonality.

03

Detection

Deviation from the pattern becomes a graded warning signal before a fixed threshold would trigger.

04

Correlation

Alerts from multiple sources collapse into a single incident with a probable root cause and evidence.

05

Remediation

A runbook resolves routine cases on its own or routes to the right analyst, already with context and history.

Product capabilities

Six capabilities to operate with intelligence and foresight.

Data

Universal data integration

Brings together logs, metrics, events, and traces from monitoring, cloud, and on-premises infrastructure into a single model, without replacing your stack.

AI

Anomaly detection

Identifies out-of-pattern behavior per service, anticipating problems that fixed thresholds don't detect.

Noise

Alert correlation

Groups alerts from multiple sources with machine learning and reduces the flood to a few real incidents.

Context

Root cause explained

Points to the probable origin of the incident in plain language, with the evidence that supports the hypothesis.

Action

Automated remediation

Runs runbooks for known tasks under the governance you define for each service.

Cycle

Connection with CITSmart ITSM

The anomaly is born as an incident with an owner and context, closing the loop of detection, handling, and resolution.

Use cases

From the NOC that puts out fires to the one that anticipates failure.

AIOps changes the operating posture: from reacting to the problem to preventing it before the user notices.

Availability

Service stays up before the customer notices

A per-service baseline anticipates the failure. SLA impact is calculated before the incident, prioritizing by business service.

  • Baseline learned per service
  • SLA impact calculated automatically
  • Prioritization by business service
Infrastructure and cloud

Capacity and consumption under control

Saturation trends are projected ahead of time. A unified view of on-premises and cloud infrastructure with a scaling runbook under approval.

  • Saturation trend projected
  • Unified on-premises + cloud view
  • Scaling runbook under governance
Public services

Continuity of service to the citizen

Demand seasonality is learned automatically. Complete audit trail and operations aligned with public governance.

  • Demand seasonality learned
  • Complete audit trail
  • Operations aligned with Law No. 14,133/2021
Service desk and NOC

A prioritized queue, not an alert stakeout

The analyst's queue shows only what truly needs attention, with context and history. The incident opens in the ITSM automatically.

  • Queue prioritized by real impact
  • Incident opens in the ITSM with context
  • Suggested resolution from history
The market has already moved

AIOps left the lab and became an operational standard.

Organizations that run AI in their infrastructure respond faster, with smaller teams and more guaranteed availability.

90%

noise reduction via ML correlation

Source · Gartner
60%

less downtime with early anomaly detection

Source · Forrester
40%

MTTR reduction with automated root cause

Source · Forrester
70%

of companies will use AI agents in infrastructure by 2029

Source · Gartner
01

Configurable autonomy

you define the control

For each service, choose between just notifying, suggesting the runbook, or executing the automatic fix. Control is granular.

02

Complete audit trail

every action logged

Every detection, correlation, and automated action is logged with origin, evidence, and owner, ready for audit.

03

Natively integrated ITSM

closed loop

The detected anomaly is born as an incident in CITSmart ITSM with context, owner, and history, with no re-entry of data.

04

A baseline that learns

not a fixed threshold

The model learns what is normal for each service and time, detecting deviations before a fixed threshold would trigger.

Frequently asked questions

Common questions about CITSmart AIOps.

Does AIOps replace the monitoring tools we already have?

No. AIOps integrates with the tools you already use (it receives data from any source) and adds a layer of correlation, anomaly detection, and remediation on top of what already exists.

How is automatic remediation controlled?

You define the level of autonomy per service: just notify, suggest a runbook, or execute automatically. Sensitive actions can require human approval before execution.

How long does it take for the model to learn the baseline?

The model starts identifying patterns within the first few hours and becomes reliable after 1 to 2 weeks of data, automatically adjusting to seasonality and behavior changes.

Does AIOps work with on-premises infrastructure?

Yes. AIOps supports on-premises infrastructure, public cloud, and hybrid environments, collecting data via ready-made connectors or API without requiring a change of stack.

Next step

Stop finding out about problems from user complaints.

See CITSmart AIOps detect, correlate, and act on your operation's signals in a demo using data from your own context.

Schedule a demo