Reduced support costs by 15% while improving response time by 20%

Automated Ticket Resolution

Reduced Support Costs by 15% While Improving Response Time by 20%

Automated Ticket Resolution


Support overhead reduced by 15%.


Reduction in tickets routed to humans by 22%.


Automation increased to 94%.


First response time for tickets improved by 20%.


Our customer’s data center management costs were increasing over time and not giving the customer the economies of scale they had forecasted. The customer was looking for a vendor that could reduce the ticket volume flowing to the support engineers by using RPA with rule engines to auto-resolve alarms and tickets.


  • An average of 10M alarms per month was generated from the data centers.
  • After the elimination of duplicate alarms, the monthly average ticket volume was still at 7M.
  • The percentage of tickets being auto resolved was at 70%.
  • Currently, business rules could only be updated by the development team.


  • Increase the auto-resolution of tickets to 90%.
  • Reduce monthly support costs by 10%.
  • Improve first response time by 20%.


  • Integrated a newer rules-engine (JEXL) that could be leveraged to perform more complex maintenance activities driven by business rules.
  • Built alarm correlation algorithms to group related alarms into a single ticket with the corresponding log files.
  • Auto-routing of tickets to skill groups was enhanced by automatically attaching log files to tickets using RPA. This improved the first response time.
  • Enabled the import of business rules using CSV files to eliminate the dependency on the development team.
  • The CSV files enabled faster updates to rules and maintenance commands resulting in increased auto resolution.
  • Automation layer added to auto-resolve device on-boarding tickets. The automation workflow would be retried based on policies, before assigning the ticket to a human.
  • Implemented Anomaly Detection, Alerting, and Auto Resolution workflows.
  • Implemented Alarm Management to handle customer maintenance windows.


Support overhead reduced by 15%.

Reduction in tickets routed to humans by 22%.

Automation increased to 94%.

First response time for tickets improved by 20%.

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