A leading telecommunications client needed to extract real-time insights from large volumes of diverse, unstructured data across its nationwide 5G network. As network complexity increased, faster issue detection and real-time visibility became critical.
Aditi’s engineering team embedded production machine learning models into operational workflows. Mean Time to Detect/Repair was reduced by 35%+.
At a glance
Client data volume
Reported result
Deployment
The Challenge
The Challenge
The client processed terabytes of network data each day. Extracting actionable insights in real time required scalable machine learning workflows, advanced anomaly detection models, and automated root cause analysis capable of operating across its nationwide 5G network.
The Solution
The Solution
Aditi deployed a full-stack engineering team to build production models for anomaly detection, trend analysis, and root cause identification. The team embedded those models into operational workflows.
Aditi also delivered Splunk enterprise onboarding, analytics and insights portals, interactive dashboards, and Machine Learning as a Service (MLaaS) model development and deployment.
Business Outcomes
What Changed
Network analysis became part of how the client detected and investigated issues across its 5G network.
Technology Environment
Technology Environment
Technology Environment
