Jan 13, 2025
The brief:
A major financial technology company needed a faster and more proactive way to manage service interruptions in its trading and order management systems. Outages during market hours created real financial impact, slowed trade execution, and frustrated clients.
The problem statement:
IT operations teams manually searched logs and telemetry to diagnose issues
Analysis took too long when seconds mattered most
Fixes could not be deployed while markets were open
Recurring outages damaged customer confidence and revenue
Support teams were stuck reacting to fires instead of preventing them
The solution:
Tenjumps designed and deployed a telemetry ingestion and machine learning analysis system
Automated ETL pipelines pulled CPU data, memory use, process logs, and system signals from every server
Machine learning models identified outage patterns and surfaced early warnings
Insights were routed to ops teams with recommended fixes
Approvals were required before applying changes to avoid risk during trading windows
System became smarter over time as more incidents were processed
IT teams gained a real time view of emerging failures and a playbook of actions to resolve them before users ever felt the impact.
The outcomes:
Earlier detection of performance degradation
Faster incident response during trading hours
Lower downtime across trading systems
Reduced operational strain on support team
Higher customer satisfaction and trust in platform stability