AIOps & Intelligent Automation
Your operations team is drowning in alerts. Most of them are noise. AIOps uses machine learning to separate the signal from the noise - and automate the response before humans even wake up.
Get StartedThe Problem
Modern infrastructure generates millions of events per day. Traditional threshold-based alerting cannot scale. Engineers spend more time triaging noise than fixing real problems, and alert fatigue means genuine incidents get missed.
The cost of reactive operations is enormous. Mean time to detect (MTTD) and mean time to resolve (MTTR) directly impact your SLAs, your customer trust, and your engineering team's wellbeing. On-call burnout is a retention problem as much as an operations problem.
AIOps applies machine learning to your telemetry data to detect anomalies before they become incidents, correlate alerts that are symptoms of the same root cause, and automate the runbook steps that consume 80% of your on-call hours.
Our Approach
Telemetry consolidation
We centralise your metrics, logs, and traces into a unified observability platform. No AIOps system works well with fragmented data.
Anomaly detection baseline
We implement ML-based anomaly detection across key signals - latency, error rates, resource utilisation, and custom business metrics. Alerts fire on deviation from learned baselines, not arbitrary thresholds.
Alert correlation and noise reduction
We group related alerts into incidents using topology-aware correlation. A cascading failure across 12 services generates one incident, not 47 alerts.
Automated runbook execution
We codify your top 10 most common incidents as automated runbooks - restart a service, scale a deployment, clear a cache, page an engineer only if automation fails.
What You Get
- Unified observability platform (OpenTelemetry + Elastic or Grafana)
- ML-based anomaly detection across infrastructure and application metrics
- Alert correlation engine (BigPanda, Dynatrace, or open-source equivalent)
- Automated runbooks for top-10 incident patterns
- Predictive capacity alerting (catch resource exhaustion before it happens)
- On-call noise reduction (typically 60–80% fewer pages)
- AIOps dashboard with incident timeline and root cause suggestions
- Runbook documentation and team training
Tech Stack
Real Example
Context: SaaS platform with 200+ microservices. On-call engineers receiving 300+ alerts per week, 80% of which required no action.
AIOps implementation reduced actionable pages by 74%. MTTR dropped from 42 minutes to 11 minutes. On-call satisfaction scores improved significantly.