AI for Network Optimization in Telecommunications |…

Plan, tune, and operate telecom networks with AI to raise quality, eff

Network optimization uses AI to improve coverage, capacity, latency, and reliability across RAN, transport, and core. It combines planning, routing, parameter tuning, and resource scheduling with guardrails for stability and compliance.

Deliver better user experience and cost efficiency through data-driven, safe, and explainable optimization.

Network KPIs (throughput, latency, drop rate), configuration, topology

Traffic forecasting, coverage/capacity modeling, parameter optimization (e.g., tilt/power), routing optimization, anomaly detection, what-if simulations.

Planner/ops consoles, recommendation engines, automation with approval gates, and rollback.

live demo · related simulation● LIVE

Sleep modes and load-aware power management.

Self-Organizing Networks

Closed-loop optimizations with approvals.

Frequently asked questions

What is the integration strategy for OSS/NMS, probes, and planning tools?

Integrate OSS/NMS, probes, planning tools; ensure time-sync, data quality, and secure access; log changes and outcomes.

How are governance, safety, and explainability addressed in this AI network optimization process?

Governance, Safety, and Explainability

What information is presented to demonstrate the drivers behind recommendations and their expected impact?

Show drivers for recommendations and expected impact.

How are guardrails implemented on parameters, and what is the process for staged rollouts with automated rollback?

Guardrails on parameters; staged rollouts; automated rollback.

▶ Try it live

Everything above runs in your browser — open Force-Directed Graph and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Force-Directed Graph simulation