What the Client Was Facing
A fintech payments platform suffered 6–8 P1 incidents per month, each causing regulatory reporting obligations and customer SLA penalties. Post-incident analysis showed the same 5 failure patterns repeating — but the on-call team had no tooling to detect them early enough.
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 an AI-powered operations platform combining Prometheus, Datadog and a custom ML anomaly detection layer. Known failure pattern signatures were encoded as detection models trained on historical incident data. When patterns were detected, automated Ansible remediation playbooks executed pre-approved responses.
Technologies Used
Measurable Outcomes Delivered
P1 incidents reduced from 6–8 per month to 1 within 90 days
Mean time to detect reduced from 18 minutes to 2 minutes
Automated remediation handling 3 of 5 known failure patterns without human intervention
Regulatory reporting burden reduced — fewer incidents means fewer mandatory notifications
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