Case 04
Multimodal ML Monitoring for Predictive Service Assurance
Situation
Customer-reported incidents are a lagging indicator, by the time a user calls in a problem, the degradation has already happened. On a platform serving millions of users across dozens of carrier networks, the gap between when a problem starts and when it surfaces as a customer complaint was costing both experience quality and engineering time spent on reactive response.
Complication
The signals that predict call quality degradation are spread across multiple data types such as voice quality metrics, network telemetry, usage behavior, carrier-side events. No single signal is sufficient. Building a monitoring platform that could fuse these streams meaningfully, without generating alert noise that engineers would learn to ignore, required a more sophisticated approach than traditional threshold-based monitoring.
Approach
Developed and introduced a multimodal ML monitoring platform that combined voice quality signals, network telemetry, and behavioral data into a unified predictive model. Led the program from requirements definition through engineering delivery and operational integration, including the process changes needed to act on early signals before they became customer-visible incidents.
Outcome
Customer-reported incidents reduced by 30%. The platform shifted its operational posture from reactive incident response to predictive service assurance, catching degradation early and enabling remediation before users were impacted. This represented a foundational change in how quality was managed at scale.