Network Anomaly Detection
Identify unusual behavior across network KPIs, signaling, session activity, traffic patterns, and infrastructure telemetry.
AI & Network Intelligence
Practical AI for Telecom Network Operations
Apply AI and machine learning to real engineering challenges — from detecting abnormal network behavior to forecasting capacity and helping engineers investigate incidents. Our approach begins with the operational problem, the available evidence, and a measurable evaluation plan.
Discuss a Telecom AI Use Case
What We Apply AI To
AI Capabilities
Each capability is scoped to a defined operational problem, evaluated against measurable criteria, and subject to human oversight for consequential actions.
Capacity Forecasting
Forecast traffic, session growth, resource consumption, and capacity requirements using historical data and relevant business drivers.
Incident Correlation
Group related alarms, events, changes, and performance anomalies to help engineers investigate incidents more efficiently.
Probable-Cause Ranking
Rank possible failure domains using observed evidence, historical incidents, topology, and change context. Results support investigation; they do not replace engineering validation.
Engineering Knowledge Assistant
Help authorized engineers search approved documentation, runbooks, configuration references, and incident knowledge bases.
5G Analytics & NWDAF
Assess relevant 5G analytics capabilities, supported interfaces, data availability, and integration opportunities based on the operator's deployed architecture and release.
Our Approach
AI Delivery Principles
We apply AI where it solves a defined problem with measurable criteria and responsible governance.
1Start with a defined operational problem
2Assess data quality, coverage, and representativeness
3Establish a baseline and evaluation criteria
4Use explainable evidence where practical
5Keep human approval for consequential actions
6Monitor model performance and drift
7Protect network and subscriber information
8Document limitations and conditions under which a model should not be trusted
Engagement Process
AI Engagement Lifecycle
Use-Case Discovery
Define the operational problem, success criteria, and stakeholder requirements.
Data & Feasibility Assessment
Evaluate data quality, coverage, completeness, and technical feasibility.
Prototype & Baseline
Develop an initial model or prototype and establish a performance baseline.
Model Evaluation
Assess results against agreed criteria. Document limitations and conditions.
Controlled Integration
Deploy into the operational environment under defined governance and approval controls.
Operational Monitoring
Monitor model performance, detect drift, and manage the model lifecycle.