AI for risk assessment and decision-making

The company provides telecom services in major parts of North America and the UK, hence the business is very big, so is the data generated by their operation. The company wanted to have an AI-based monitoring and decision-making facility to identify key risks involved in their service such as to analyze call data, categorize it, identify the critical failure area and remediate the issue. By ensuring that the service does not suffer an already known issue and it will be easy to provide a seamless service.

Based on our approach we implemented PCA, Isolation Forest and Autoencoder based anomaly detection models to identify anomalies from the good service. The time-series call data forecasting was achieved by training an LSTM model on historical volume data and to forecast for the desired time in the future. Notably, these two solutions are real-time which required a high level of optimization to accommodate the high frequency of incoming data. AngularJS UI was developed to serve as a front-end for this application which will help the user monitor, schedule and take action right from the UI without worrying about the backend processes

Advanced state-of-art AI models High-performance training of models Real-Time prediction and Decision Making User-friendly UI to manage the application