AIOps - AI for IT Operations
Thousands of alerts, not enough people, constant firefighting. AIOps uses machine learning for event correlation, root cause analysis, and problem prediction. Your team focuses on what matters, not noise.
What is AIOps?
AIOps (Artificial Intelligence for IT Operations) is an intelligence layer over existing monitoring tools that uses machine learning to correlate events, detect anomalies, and identify root causes automatically. nFlo's AIOps implementation reduces alert noise by 90%, cuts MTTR by 50%, and resolves 40% of incidents automatically — without replacing your current monitoring stack.
Alert fatigue is killing your team's productivity
Intelligent IT Operations
Event correlation
Thousands of alerts → dozens of incidents
Root cause analysis
Automatic cause identification
Anomaly detection
Problem detection before impact
3000 alerts per day, 5 people on the team
An e-commerce company has 500 servers, 50 microservices, 3 clouds. Monitoring generates 3000 alerts daily. Team of 5 people. Most time goes to triaging - deciding what’s important. Real problems get lost in the noise. MTTR grows, customers complain.
Alert fatigue symptoms:
- Team ignores alerts (“probably false positive”)
- Every incident requires manual tracking
- No time for proactive improvements
- High turnover in ops team
- SLA violations increasing
Machine Learning for IT Operations
AIOps is not another dashboard. It’s an intelligence layer over your monitoring tools. ML analyzes millions of events, correlates them, identifies anomalies, and suggests root cause. Your team gets dozens of incidents instead of thousands of alerts.
How AIOps works:
- Ingestion: Collecting data from all sources (monitoring, logs, APM, CMDB)
- Normalization: Format standardization and context enrichment
- Correlation: Grouping related events into incidents
- Analysis: ML identifies anomalies and probable root cause
- Action: Automated remediation or routing to the right team
Features
Event Correlation
Grouping related alerts:
- Topology-based correlation (same host/service)
- Time-based correlation (within time window)
- Semantic correlation (similar content)
- Custom rules (your business logic)
Result: 1000 alerts → 10 incidents
Anomaly Detection
Detecting unusual behavior:
- Baseline learning (what’s normal)
- Seasonal patterns (day/week/month)
- Multi-metric correlation
- Early warning before degradation
Result: Problems detected 15 min before impact
Root Cause Analysis
Automatic cause identification:
- Topology walkback
- Change correlation
- Historical pattern matching
- Confidence scoring
Result: “Probable root cause: deployment v2.3.1 at 14:23”
Automated Remediation
Automatic fixing of known problems:
- Runbook automation
- Self-healing actions
- Rollback triggers
- Human-in-the-loop for risky actions
Result: 40% of incidents resolved without human intervention
Integrations
Monitoring
- Prometheus, Grafana
- Datadog, New Relic, Dynatrace
- Zabbix, Nagios
- CloudWatch, Azure Monitor
Logging
- ELK Stack (Elasticsearch, Logstash, Kibana)
- Splunk
- Graylog
- CloudWatch Logs
ITSM
- ServiceNow
- Jira Service Management
- PagerDuty
- OpsGenie
CMDB
- ServiceNow CMDB
- Device42
- Custom CMDBs (API)
Who is this for?
This service is for you if:
- You have more alerts than you can handle
- MTTR is growing despite a larger team
- Incidents require hours of manual tracking
- Ops team spends time firefighting, not improving
- You have multiple monitoring tools without correlation
Deliverables
AIOps Assessment
- Analysis of current tools and processes
- Quick wins identification
- Implementation roadmap
- Business case and ROI
Time: 2 weeks | Price from: 20,000 PLN
AIOps PoC
- Deployment on selected scope
- Integration of 3-5 data sources
- Baseline and tuning
- Before/after metrics
Time: 6-8 weeks | Price from: 80,000 PLN
Full AIOps Implementation
- Enterprise-wide deployment
- All integrations
- Custom correlation rules
- Runbook automation
- Team training
Time: 4-6 months | Price from: 200,000 PLN
Related Glossary Terms
Learn more about key concepts related to this service:
Contact your account manager
Discuss AIOps - AI for IT Operations with your dedicated account manager.
How we work
Our proven service delivery process.
Discovery
Data source inventory
Integration
Connect monitoring, logs, CMDB
Baseline
ML learns normal behavior
Tuning
Correlation and alert tuning
Automation
Automated remediation
Benefits for your business
What you gain by choosing this service.
Less noise
90% alert reduction
Faster response
50% shorter MTTR
Better team utilization
Focus on important problems
Fewer incidents
Prediction and prevention
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Read more →Frequently Asked Questions
Common questions about AIOps - AI for IT Operations.
How long does AIOps implementation take and when will I see the first results?
A PoC on a selected scope takes 6-8 weeks, full enterprise implementation 4-6 months. First results (60-70% alert noise reduction) are visible after the baseline and tuning phase, i.e. 4-6 weeks from PoC start.
Does AIOps require replacing existing monitoring tools?
No. AIOps is an intelligence layer over your existing tools. We integrate with Prometheus, Grafana, Datadog, Zabbix, ELK Stack, ServiceNow and others via API. Your current monitoring investments stay.
What is the minimum data scope needed to launch AIOps?
For a PoC we need a minimum of 3-5 data sources (e.g. monitoring, logs, APM). ML needs 2-4 weeks to learn baseline behaviors. The more historical data available, the better the correlations from the start.
How much does AIOps implementation cost?
Assessment and roadmap cost from 20,000 PLN (2 weeks). PoC on a selected scope from 80,000 PLN (6-8 weeks). Full enterprise implementation from 200,000 PLN (4-6 months). ROI is typically achieved in 6-12 months.
Can AIOps automatically fix problems without human involvement?
Yes, the Automated Remediation module handles known scenarios (service restarts, deployment rollback, resource scaling). For risky actions we use human-in-the-loop. Typically 40% of incidents are resolved automatically.