5G networks are too complex for manual management. Our AI automates network slicing, edge computing orchestration, and resource optimization across thousands of nodes—reducing operational costs by 45% while enabling new revenue streams.
5G introduces exponentially more complexity than 4G—network slicing, edge computing, ultra-low latency requirements, and massive IoT connections. Manual management simply doesn't scale.
5G supports hundreds of simultaneous network slices with different SLA requirements—impossible to manage manually.
Managing thousands of edge nodes with dynamic workload placement requires real-time AI decision-making.
Ultra-low latency applications (1-5ms) demand automated resource allocation faster than human reaction time.
5G OPEX is 40% higher than 4G due to complexity—AI automation is essential for profitability.
Boaweb AI deploys intelligent systems that automate network slicing, edge orchestration, resource optimization, and service assurance across your entire 5G infrastructure.
AI automatically creates, configures, and manages network slices based on service requirements—ultra-reliable low-latency (URLLC), enhanced mobile broadband (eMBB), or massive IoT (mMTC). The system analyzes SLA requirements, traffic patterns, and resource availability to provision optimal slice configurations in seconds rather than the hours manual configuration requires. Dynamic slice scaling adjusts resources in real-time as demand changes.
Machine learning continuously optimizes workload placement across distributed edge nodes based on latency requirements, compute capacity, data locality, and cost constraints. The AI predicts application demand patterns to pre-position resources at edge locations before traffic arrives, ensuring consistent low-latency performance. Automated failover and load balancing maintain service quality even when edge nodes fail or become congested.
AI analyzes real-time network conditions—spectrum utilization, compute capacity, bandwidth availability, and service demands—to optimize resource allocation across radio access network (RAN), edge, and core infrastructure. The system continuously rebalances resources between competing services and slices, maximizing network efficiency while maintaining SLA commitments. This reduces infrastructure costs by 35-45% compared to static provisioning.
The AI continuously monitors service quality metrics—latency, throughput, packet loss, jitter—for every network slice and application. When performance degrades or SLA violations are predicted, the system automatically triggers remediation actions: adjusting resource allocations, rerouting traffic, scaling capacity, or alerting operations teams. This proactive approach prevents 78% of service quality issues before customers experience impact.
Machine learning models forecast 5G capacity requirements based on subscriber growth, new service launches, IoT device proliferation, and seasonal usage patterns. The AI recommends optimal infrastructure investments—where to deploy additional edge nodes, when to upgrade RAN capacity, which regions need spectrum expansion—enabling data-driven CapEx planning that avoids both over-provisioning waste and capacity shortfalls.
Stop struggling with 5G complexity. See how AI management can reduce operational costs by 45% while enabling new revenue streams through automated network slicing and edge services.
A European telecom operator deploying 5G across major cities faced mounting operational complexity. Managing 150+ enterprise network slices with varying SLA requirements (autonomous vehicles needing 1ms latency, IoT sensors with massive connection density, AR/VR requiring high bandwidth) required a team of 67 network engineers. Manual slice provisioning took 3-5 hours, preventing rapid service deployment and new revenue opportunities.
Boaweb AI Solution: We deployed an intelligent 5G management platform that automates network slicing, edge orchestration, and resource optimization across 8,500 base stations and 240 edge computing nodes. The AI continuously analyzes service requirements, traffic patterns, and infrastructure capacity to optimize slice configurations and resource allocation in real-time.
Results after 9 months: Network slice provisioning time decreased from 3-5 hours to under 2 minutes (automated 92%). Operational costs reduced by €12.4M annually (47% reduction). Resource utilization efficiency improved from 34% to 87%, delaying €45M in planned infrastructure investments. The operator launched 23 new enterprise services that were previously impractical due to manual management constraints, generating €8.7M in new annual revenue.
Our AI platform integrates with standard 5G network components via 3GPP-compliant interfaces—connecting to core network (5GC), radio access network (RAN), edge computing platforms, and orchestration systems (MANO, Kubernetes). The system works with multi-vendor infrastructure from Ericsson, Nokia, Huawei, Samsung, and others. Deployment typically takes 6-10 weeks without requiring infrastructure replacement.
Yes, that's precisely what the AI is designed for. The system continuously monitors and optimizes hundreds of concurrent slices with varying requirements—some need ultra-low latency (1-5ms for autonomous vehicles), others prioritize high bandwidth (AR/VR streaming), and others focus on massive connectivity (IoT sensors). AI dynamically allocates resources to maintain all SLA commitments while maximizing infrastructure efficiency.
The AI includes extensive safeguards: continuous SLA monitoring, automatic rollback when performance degrades, human approval for major configuration changes, conservative optimization modes for critical services, and engineering override capabilities. The system learns from mistakes—if an optimization action causes issues, it adjusts its models to prevent similar problems in the future.
AI continuously analyzes application requirements (latency, compute, data locality), edge node capacity, and user locations to optimize workload placement. The system predicts demand patterns to pre-position resources at edge locations before traffic arrives. When conditions change—user mobility, edge node failures, traffic spikes—AI automatically migrates workloads to maintain performance while minimizing cost.
Most operators see 40-50% reduction in 5G operational costs within 12 months through automation and efficiency gains. Additionally, faster service provisioning (minutes vs. hours/days) enables new enterprise revenue streams that manual management makes impractical. Infrastructure utilization improvements of 50-70% delay capital expenditure by 18-36 months. Typical ROI payback period is 10-16 months.
Stop struggling with manual 5G management. Get an AI platform that automates network slicing, edge orchestration, and resource optimization—reducing costs by 45% while enabling new revenue opportunities.
Includes infrastructure analysis and automation roadmap.