Your platform that learns from itself.
AravaliStack's AIOps layer applies machine learning to your own platform telemetry — detecting anomalies before they become incidents, correlating root causes across hundreds of signals, and automating remediation for known failure patterns.
Intelligent operations across every platform layer.
Unsupervised ML models trained on your baseline telemetry — CPU, memory, network, latency, error rates. Detects deviations from normal patterns 20–40 minutes before they become user-visible incidents.
When an incident fires, AIOps correlates alerts across compute, networking, storage, and application layers to surface the probable root cause — reducing mean time to diagnosis from hours to minutes.
For known failure patterns, AIOps executes remediation playbooks automatically — pod restarts, traffic rerouting, resource scaling — with a full audit trail of every automated action and a rollback mechanism.
AIOps analyses resource utilisation trends across all workloads to forecast when you'll need additional capacity — weeks in advance, not hours. Capacity recommendations are integrated into the cost intelligence dashboard.
Every AIOps model in AravaliStack trains exclusively on your platform's own telemetry. No data leaves your perimeter. No SaaS AIOps vendor has a window into your operations. The intelligence belongs to you.
Anomaly Detection
Unsupervised ML models trained on your baseline telemetry — CPU, memory, network, latency, error rates. Detects deviations from normal patterns 20–40 minutes before they become user-visible incidents.
Root Cause Correlation
When an incident fires, AIOps correlates alerts across compute, networking, storage, and application layers to surface the probable root cause — reducing mean time to diagnosis from hours to minutes.
Automated Remediation
For known failure patterns, AIOps executes remediation playbooks automatically — pod restarts, traffic rerouting, resource scaling — with a full audit trail of every automated action and a rollback mechanism.
Predictive Capacity Planning
AIOps analyses resource utilisation trends across all workloads to forecast when you'll need additional capacity — weeks in advance, not hours. Capacity recommendations are integrated into the cost intelligence dashboard.
- Multivariate anomaly detection across correlated signals
- Dynamic baselines that adapt to business cycles
- Low false-positive rate via contextual suppression
- Cross-layer causal graph analysis
- Historical incident matching
- Suggested remediation with confidence score
- GitOps-managed playbook library
- Approval gates for sensitive actions
- Complete action audit trail
- 30/60/90-day capacity forecasts
- Cost-optimised scaling recommendations
- Hardware procurement lead time awareness