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Telaverge Predictive Analytic Engine uses the neural approach to parse large data sets to predict and analyze various outcomes in the required solutions.

In today’s digitally driven ecosystem, a couple of key requirements for enterprises and operators are an Observability Platform and an Automated Testing Platform which saves time, runs test cases effectively, and employs the latest technology to minimize test failures. Besides providing the latest technological advantage, Telaverge’s Network Observability Platform and Test Automation Orchestration Platform have both integrated Artificial Intelligence (AI) and Machine Learning (ML) to take the QA process to a new level.

Telaverge’s AI and ML algorithms are being developed to recognize patterns from large volumes of data, and valuable knowledge is presented in an easy-to-understand manner. This facilitates quick troubleshooting and improves the overall performance of the network.

Telaverge Predictive Analytic Engine uses the neural approach to parse large data sets to predict and analyze various outcomes in the required solutions. This capability ensures better performance and productivity with minimal human intervention.

Use Cases

Customer Churn

Customer Demographics

Forward-looking predictive failures in network elements

Security breach forewarning ahead of incidences

Test Case Prioritization

Failed Test Case Classification

Why Telaverge

Artificial Intelligence (AI) and Machine Learning (ML)

Using Telaverge’s AI and ML enabled network observability and test automation solutions provides a huge cost benefit. The following use case shows the Pre-ML and Post-ML benefits:

Pre-ML
  1. Test planning and prioritization were intuition driven.
  2. Defect classification was a manual process.
  3. More than 100 defects needed to be scanned to locate one product defect.

Post-ML

  1. Test planning and prioritization are data assisted.
  2. Promotes Fail-Fast – Fix-Fast strategy.
  3. The ML integrated technique offers 90% accuracy, which reduces resource and operational costs.