Automation Driven Network Transformation

Leveraging extesive expertise and capabilities developed from our support of CSPs globally, B-Yond implemented and hardened solutions using Machine Learning, primarily in th efollowing areas of the Network Lifecycle.

Planning & Design
Data-Informed CAPEX Planning

CAPEX planning has historically been a rules-based process that isn't structured to keep pace with rapid changes in network capabilities and customer needs. The PLATFORM AI platform can apply dynamic machine learning acquired through its Anomaly Detection, Root Cause Analysis and Predictive Maintenance tools to inform accurate, higher-return, Customer-centric network investments.

CAPEX Planning with Graphic Impact

PLATFORM's CAPEX planning capability features drag-and-drop tower placement modeling. Within seconds, planning teams can assess tower placement against topology and customer populations to ascertain not only the ROI of additional towers, but also the optimal positioning for maximum CX (Customer Experience) improvement.

Bring Greater Accuracy to CAPEX Planning

Cell-level data delivers unprecedented insights on tower performance and empowers more informed decision making. PLATFORM can help planning teams align CX (Customer Experience) investment with ROI goals along critical parameters, including:

  • smaller, high-revenue VIP customer cohorts
  • larger, low-revenue, high-churn customer population
  • high-consumption services
Certification Insights & Validation

Before going into production, it is necessary to guarantee that user devices, network components, virtual functions and network features are tested, validated, and certified, to ensure a quality rollout of the network.

Change & Configuration Management

It is estimated that up to 60% of issues related to change management are self-inflicted. Avoid introducing incidents in your network during maintenance windows and beyond.

Network Health Insights

Today, companies continue having fundamental issues performing network monitoring that provides real visibility of network health and how it impacts business and customer experiences. One of the main causes is still the predominance of a reactive break/fix model.

Service Health Insights

Continuous Network monitoring and optimization help analyze your network, to detect and correct any performance issues, and to maintain a healthy network and furthermore, E2E quality of service for your customers.


These 5 domain cases utilize the following capabilities:

Continuous Anomaly Detection

Network data anomalies can occur randomly or as a result of hacks, security attacks, or issues with servers and services. With the increase of network virtualization and disparity of vendors, anomaly detection can no longer rely on static rules.

Anomaly Detection for the 5G Age

Through continuous monitoring of different data sources, PLATFORM discerns patterns among alarms, data logs, events, and KPIs, and then dynamically scores them as anomalies/non-anomalies.
Anomaly Detection Framework Graphic

Beyond Anomaly Detection to Anomaly Understanding

In addition to providing network and infrastructure teams with a continuous real-time monitoring capability, PLATFORM and its machine learning capability deliver increasingly greater and more sophisticated insights into the drivers AND main impacts of network data anomalies.

Anomaly Root Causes & Recommendations

With increasing network complexity, automation, and disparity of networks and tools, NOC troubleshooting has become too complex, time-consuming, and time-critical for outmoded static, human-written rules. Applying the power of its machine learning platform, the PLATFORM AI Root Cause & Recommendation engine informs operator action after network anomalies are detected and identified.

Anomaly root cause determination to accelerate troubleshooting

By analyzing ingested data and assessing network conditions in real time, INFINTY determines the root causes of network anomalies: service/hardware/software issues, or any other issues surfaced in customer experience scoring. It then provides instant, AI-driven recommendations that engineering teams can assess and follow to rectify the anomaly.

AI machine learning reduces, and keeps reducing, MTTR

As SMEs from B-Yond and your organization map anomalies to root causes, the result is continuous growth and expansion of your PLATFORM custom Root Cause & Recommendation knowledge base. PLATFORM surfaces recommendations more quickly, operators can more efficiently prioritize remediation actions; and a steady, ongoing decline in MTTR is realized.