The Challenge
Our client operates technology platforms that support hospital networks across the country. Their IT operations team was receiving over 10,000 alerts per week with no effective way to separate signal from noise. Critical issues were getting lost among false positives, and mean time to resolution for real incidents was unacceptably high.
Our Approach
Intelligent Event Correlation
We built ML models trained on historical incident data to automatically correlate related alerts. This cut the volume of actionable alerts by 85% while making sure no critical issues fell through the cracks.
Predictive Analytics
Using time-series analysis and anomaly detection, we built predictive models that catch degradation patterns before they cause outages, giving operations teams time to step in before users notice anything.
Automated Remediation
For known failure patterns, we built automated remediation workflows that trigger without human intervention, cutting MTTR for common issues from hours to seconds.
The Results
The operations team moved from reactive firefighting to proactive management. Patient-facing system availability improved to 99.99%, and the team redirected capacity that used to go into manual operations toward strategic improvement work instead.