Let machines watch the machines.
AI-powered monitoring that detects anomalies before they become incidents. 3am pages become a thing of the past.
The problem
“Your monitoring dashboard has 47 tabs open. Your on-call engineer hasn't slept properly in weeks. The alerts are infinite and most of them are noise.”
- 200+ alerts per day, most are false positives
- Mean time to detection measured in hours, not seconds
- Manual log correlation across dozens of services
- No pattern recognition for recurring incidents
Manual Monitoring vs AIOpsdifference
The difference between drowning in alerts and sleeping through the night.
Manual Monitoring
- 200+ daily alerts
- Hours to detect
- Manual correlation
- Reactive firefighting
AIOps
- 3 actionable insights
- Seconds to detect
- Automatic correlation
- Proactive prevention
How we implement AIOps
A systematic approach to replacing alert fatigue with intelligent monitoring.
Data Integration
Connect monitoring, logs, and metrics into a unified AI pipeline. We ingest data from every source across your infrastructure to build a complete operational picture.
Data Integration
Connect monitoring, logs, and metrics into a unified AI pipeline. We ingest data from every source across your infrastructure to build a complete operational picture.
Model Training
Train ML models on your specific infrastructure patterns. Every environment is unique, so our models learn the normal behavior of your systems to distinguish real anomalies from noise.
Anomaly Detection
Deploy real-time anomaly detection and intelligent alerting. Instead of threshold-based rules that cry wolf, our models understand context and only surface issues that truly matter.
Continuous Learning
Models improve over time as they learn your infrastructure. With every incident resolved and every false positive dismissed, the system becomes more accurate and more valuable.
Tools we use
We integrate with the monitoring and infrastructure tools you already trust.
Prometheus
Grafana
Elasticsearch
Docker
Kubernetes
AWS
GCP
Terraform
Case Study
Acid Labs
The Challenge
200+ daily monitoring alerts, 4-hour mean time to detect issues, exhausted on-call team.
The Result
AI-powered monitoring reducing alerts by 98%, detecting anomalies in seconds, and auto-remediating common issues.
Read full case study98%
Alert reduction
<30s
Detection time
85%
Auto-remediated
0
3am pages
Frequently Asked Questions
Ready to let AI handle the noise?
Focus on building products, not watching dashboards. Let intelligent monitoring do the heavy lifting.