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AI-Driven Telecom Network Optimization and Customer Experience Enhancement Platform

alltegrio.com

Telecommunications

Agriculture

5G Services

Challenges in Network Efficiency and Customer Engagement

Inefficient manual network management processes leading to delayed anomaly detection, high operational costs, and suboptimal customer satisfaction levels in a complex 5G network environment serving multiple industries.

About the Client

Telecom network operator seeking AI/ML-driven operational automation and customer engagement improvements

Project Goals for AI-Driven Network Optimization

Implement automated network management systems using ML models
Reduce network anomaly detection time by 70%
Decrease operational costs by 40% through predictive maintenance
Improve customer satisfaction scores by 35% via AI-driven engagement tools
Optimize resource allocation using predictive analytics

Core System Functionalities

Real-time network anomaly detection system
Predictive maintenance scheduling engine
Dynamic resource allocation optimizer
AI-powered customer churn prediction module
Intelligent chatbot for customer service
Network traffic analysis dashboard
Cybersecurity threat detection system

Technology Stack Requirements

Apache Spark

AWS

Azure

TensorFlow

spaCy

OpenAI

System Integration Needs

Existing telecom infrastructure APIs
Customer relationship management (CRM) systems
Network monitoring tools
Billing and service management platforms

Non-Functional Requirements

99.99% system availability
Real-time processing latency <50ms
Enterprise-grade data encryption
Horizontal scalability for 10x growth
Compliance with GDPR and telecom regulations

Expected Business Impact of AI-Driven Network Optimization

Anticipated 70% faster issue resolution times, 40% reduction in operational expenses, and 35% improvement in customer retention rates through proactive network management and personalized customer engagement tools, establishing new industry benchmarks for telecom service excellence.