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AIOps

AI that runs your operations — not just reports on them.

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80%
alert noise reduction
10×
faster resolution
70%
auto-remediated
24/7
autonomous ops

Overview

What We Deliver

Traditional monitoring generates thousands of alerts that overwhelm on-call teams with noise while genuine incidents get buried. AIOps replaces reactive alert storms with intelligent, autonomous operations — AI agents that correlate signals across metrics, logs, traces, and events to detect real problems, execute self-healing runbooks, and surface only what needs human judgment. Aezona's AIOps practice integrates AI-driven intelligence into your existing observability stack without ripping and replacing your tools.

Technology Stack

DatadogGrafanaPagerDutyMoogsoftBigPandaOpenTelemetryPython / LangChain

What's Included

Core Capabilities

Every engagement includes these capabilities, scoped to your environment and requirements.

Anomaly Detection

ML models trained on your environment's historical patterns to detect subtle deviations before they become outages.

Self-Healing Runbooks

Automated remediation agents that execute approved runbooks — pod restarts, cache flushes, scaling — without waking anyone up.

Event Correlation

Cross-signal correlation reducing thousands of related alerts to a single actionable incident with full context.

Root Cause Analysis

AI-driven causal inference pinpointing the root cause of incidents across distributed microservices in seconds.

Predictive Capacity

Forecast-based capacity planning that pre-scales infrastructure before traffic spikes occur.

Observability Integration

Native integrations with Datadog, Grafana, PagerDuty, Splunk, New Relic, and ServiceNow.

Our Process

How It Works

1
01

Ingest

Connect all observability data sources — metrics, logs, traces, events — into the AIOps platform.

2
02

Train

ML models trained on 90 days of historical data to establish normal behaviour baselines per service.

3
03

Automate

Runbook library built and tested in shadow mode — AI observes for 2 weeks before taking autonomous action.

4
04

Optimise

Continuous model retraining, feedback loops from on-call engineers, and expanding automation coverage.

Real-World Applications

Common Use Cases

Alert Storm Reduction

Microservices platform generating 10,000 alerts/day reduced to 50 actionable notifications after AIOps deployment.

Self-Healing Infrastructure

Kubernetes pods that auto-restart, scale, or reroute traffic based on anomaly signals — all without human intervention.

On-Call Burnout Relief

Remove routine operational tasks from your on-call rotation so engineers focus only on novel, high-value incidents.

Free initial consultation — no commitment

Ready to transform your aiops?

Speak with a certified Aezona architect about your specific requirements. We typically scope a full proposal within 48 hours.

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