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🏒 Telecoms Provider

Predictive Alerting Saving Β£1.8M in Annual Incident Costs at Telecoms

33/45Project Reference
14 weeksEngagement Duration
5 architectsZippyOPS Team
4Measurable Outcomes
The Challenge

What the Client Was Facing

A telecoms company was spending Β£2M annually on incident response for network infrastructure failures. Post-incident analysis showed warning signals were present 15–45 minutes before each failure β€” but no automated detection existed.

Our Role

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 β€” delivering measurable outcomes within a fixed engagement timeline. Our team worked embedded with the client's engineers throughout the entire project.

The Solution

How We Solved It

ZippyOPS implemented a predictive alerting system trained on 18 months of historical metrics, log patterns and incident data. LSTM models detected early warning patterns for 12 known failure classes. When a pattern was detected, PagerDuty alerts fired with the predicted failure type, estimated time to impact and recommended intervention.

Technologies Used

Python TensorFlow Prometheus Elasticsearch PagerDuty Grafana Kafka AWS SageMaker Airflow NVIDIA GPU
The Results

Measurable Outcomes Delivered

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Β£1.8M in annual incident costs avoided β€” payback achieved in under 3 months

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Proactive intervention enabled for 78% of predicted failures before customer impact

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Mean time between failures extended 40% through earlier intervention

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NOC team operating proactively β€” 3 interventions/week replacing 8 reactive incident responses

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