With thousands of network nodes and complex dependencies across fiber, power, and transport layers, the client struggled with:
Delayed root cause identification during site outages
Manual triaging, requiring engineers to dig through logs, tickets, and device metrics
Recurring incidents due to missed systemic patterns
Poor SLA performance impacting revenue and customer satisfaction
They needed a way to accelerate incident understanding and reduce mean time to resolution (MTTR).
Seawolf AI deployed OpsMind, a large language model–powered assistant designed to perform real-time root cause hypothesis generation and resolution support.
Key capabilities included:
Incident Summarization from Logs & Tickets
AI agents synthesized system logs, NOC tickets, and alerts into a cohesive incident overview for engineers.Hypothesis Generation
OpsMind offered ranked potential root causes using contextual understanding of past incidents and known failure patterns.Resolution Suggestions
Based on playbooks and previous fix history, OpsMind recommended high-likelihood fixes, dramatically reducing time spent searching documentation.Postmortem Automation
After resolution, the system auto-generated an RCA draft to feed into internal knowledge bases.
We used to spend hours piecing together what happened. Now, we have the root cause and fix path in minutes.
VP of Network Operations
Faster resolution of service interruptions
Less engineer time spent triaging repetitive alerts
Stronger institutional memory across engineering shifts