المدن الذكية والمستدامة

Yes. The platform connects to OSS, BSS, CDR, network probes, and legacy telecom systems through REST APIs, batch files, and secure adapters. It can run in Saudi or UAE data centers, helping operators modernize analytics without replacing core systems.

No massive dataset is required to start. Models can be trained on six to twelve months of customer, usage, and network data, then improved with live 5G and IoT telemetry. Accuracy depends on data quality, but early churn and fault predictions typically reach usable confidence within weeks.

Yes. The solution can be deployed on-premise, in private cloud, or hybrid within GCC data centers to meet Saudi PDPL, UAE data localization rules, and telecom regulatory expectations. Data encryption, role-based access, audit logs, and residency controls are included.

The platform uses supervised ML, time-series forecasting, anomaly detection, and NLP for support logs. Churn models score subscriber risk, fault models predict equipment or radio issues, and traffic models forecast regional demand in high-growth GCC markets.

Ridkk offers a subscription model based on data volume, users, and deployment scope, with an optional proof of concept. Implementation for a GCC operator usually takes eight to twelve weeks, including integration, validation, training, and ongoing model monitoring.