AI-Driven CSP Network
Operations Optimization Solutions
Leveraging extensive expertise and capabilities developed from our support of CSPs globally, B-Yond has implemented and hardened solutions using Machine Learning, primarily in the following areas of the Network Lifecycle.
As a result, AGILITY's machine-learning capabilities continually build a network knowledge base that informs intelligent future planning. With that AI
knowledge base in place and constantly expanding, your organization's "institutional memory" is no longer at the mercy of employee turnover.
Open Platform and Ecosystem CSPs implementing B-Yond's network optimization solutions experience sustained and measurable improvements in:
• Incident response
• Mean Time to Repair
• Incident prevention
• CEI/Customer retention
• Service QoE
• Long-term revenue growth
B-Yond Network Optimization Solutions
B-Yond has developed the following practice areas to focus on resolving high-impact CSP challenges:
Use Cases For AI
Below are examples of our application of artificial intelligence and machine learning to E2E life cycle test automation:
Contrary to data, IP voice services have significantly less tolerance to drops or delays in communication packets when compared to streaming applications or internet traffic.
Accordingly, operators providing IP voice services over LTE (VoLTE) need to differentiate and automate parameter optimization to maintain or gain a competitive advantage in the market.
Automating dynamic link quality management and mobility management will significantly improve the call setup and drop rates, packet loss, and voice quality. The result is improved quality of experience (QoE).
Each day, NOCs (Network Operation Centers) face the challenge of navigating numerous alarms and warnings.
Due to increased network complexity and customer demand for uninterrupted services, the pace of organizations' transformation will determine their future success.
One such transformation will involve leveraging AI to automate tier-1 activities such as correlating alarms and selecting meaningful insights.
Automatic root cause analysis and action recommendation result in troubleshooting accuracy, proactive responses, and significant time savings due to faster, systematic, and consistent processes at this level of the NOC. Consequentially, Tier-2 ticket volumes decrease while team efficiency rises.