What the Client Was Facing
A real-time payment processing platform had experienced 3 major incidents in 12 months where the first sign of a problem was a customer complaint — each costing hundreds of thousands in SLA penalties and lost trust.
What ZippyOPS Was Engaged To Do
ZippyOPS was brought in to design and implement a solution addressing the root causes of the client's challenges — delivering measurable outcomes within a fixed engagement timeline. Our team worked embedded with the client's engineers throughout the entire project.
How We Solved It
ZippyOPS implemented predictive alerting using machine learning on time-series metrics from Prometheus and Datadog. Anomaly detection models were trained on 12 months of historical data to establish normal baselines. When patterns indicating future failures were detected, PagerDuty alerts fired with root cause context 15–30 minutes before impact.
Technologies Used
Measurable Outcomes Delivered
Zero customer-impacting incidents in 12 months post-implementation
Average warning time of 22 minutes before predicted failures — enabling proactive remediation
SLA penalty exposure eliminated — £1.2M in avoided penalties in year one
On-call engineers now receive actionable alerts with context, not noise
Want Similar Results for Your Team?
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