Reduced server downtime by 73%

The client, a rapidly growing D2C e-commerce brand serving 50,000+ daily active users across India, was experiencing unpredictable server outages that directly impacted their checkout and payment flows. Their AWS infrastructure — spanning EC2 instances, RDS databases, ElastiCache clusters, and an ECS-based microservices architecture — had grown organically without unified observability. The operations team relied on CloudWatch alarms with static thresholds, which generated excessive false positives during traffic spikes and missed genuine anomalies during off-peak hours.

Aadyora deployed a comprehensive AI-powered monitoring solution built on a time-series anomaly detection engine. The system ingested metrics from 200+ infrastructure endpoints, learned baseline patterns for each service independently, and used ensemble ML models to distinguish genuine anomalies from normal variance. We implemented predictive alerting that identified degradation trends 15-30 minutes before they would escalate into outages, routed through Slack and WhatsApp with contextual root cause analysis. Auto-scaling triggers were configured with ML-driven demand forecasting rather than reactive CPU thresholds.

Within 8 weeks of deployment, server downtime reduced by 73%. Mean time to detection dropped from 45 minutes to under 3 minutes, and mean time to resolution improved by 60% due to automated RCA reports. The estimated revenue recovery was ₹8L/month from prevented checkout failures. The client's engineering team reported a 40% reduction in after-hours pages, significantly improving developer satisfaction and retention.