AI devops
90%
Faster Investigation
70%
Lower MTTR
10K
Processed Alerts
Our Impact
Modern distributed systems generate massive volumes of infrastructure logs, but raw data alone does not create reliability. Engineering teams often spend hours manually triaging logs, searching for error patterns, and correlating failures across services. This reactive approach increases downtime, delays resolution, and diverts valuable engineering time away from innovation. This AI-driven monitoring platform converted high-volume Kazmon log streams into real-time operational intelligence. Instead of combing through fragmented logs, teams gained automated anomaly detection, instant failure pinpointing, and targeted alerts routed to the right systems and owners.
Real-Time Log Intelligence
Continuously monitored high-volume log streams and detected anomalies as they occurred.
Faster Incident Investigation
Reduced log triage time by 60–90% through automated error pattern recognition and correlation.
Lower Mean Time to Resolution
Cut MTTR by 40–70% by pinpointing failing servers and components instantly.
Proactive Failure Detection
Identified emerging issues before they escalated into major outages.
The Challenge
The organization relied on manual log analysis to diagnose system failures across distributed services. Kazmon logs generated large volumes of data, making it difficult to quickly identify the root cause of incidents. Engineers had to correlate errors across multiple servers and components, often under time pressure during outages. This process increased mean time to resolution and introduced delays in routing incidents to the appropriate teams. As infrastructure scaled, the manual approach became unsustainable and limited operational reliability.
Our Approach
Solutions
— Asneha Shadman