Mphasis Granted U.S. Patent for AI-driven Application and Infrastructure Management

Mumbai: Mphasis today announced that it has been awarded a U.S. patent for its AI-driven application and infrastructure management solution. The newly issued patent provides a solution for enterprises worldwide to optimize their technology investments through in-depth data analysis.

The solution predicts errors and failures of applications and infrastructure and enables preventive maintenance measures. The powerful machine learning, complex systems analysis, and graph theory-based algorithms identify and predicts stand-alone as well as the chain of events and incidents which lead to failure in technology infrastructure. It provides early warning systems and near to real-time device failures prediction using pattern recognition, network evolution, and machine learning and identifies interdependencies, cascading a well as ripple effect between components. The complex systems-based modeling solves problems arising from direct and indirect factors affecting infrastructures and enables the automation of repeatable tasks with respect to monitoring and resolution.

“The indispensable technology ecosystems of today have made it crucial for enterprises to stay ahead in terms of their IT investments and frameworks. The solution harnesses the power of AI-driven predictive analysis to improve application and infrastructure efficiency and enables enterprises to automate decision-making for a healthy technological environment,” said Srikumar Ramanathan, Senior Vice President, Global Head – Solutions, Mphasis.

“Technology applications and infrastructure have become all-pervasive and the Mphasis solution empowers decision-makers to identify dependencies between components and predict system anomalies. The benefits include early warning of system anomalies, optimization of enterprise application and infrastructure landscape, and elimination of system downtime,” said Dr. Jai Ganesh, Senior Vice President, Head – Mphasis NEXT Labs.

The core functionalities of the solution include Complex Interdependency Analytics to identify error dependencies between components, Root Cause Analysis to identify the reason for the breakdown and take corrective actions at the earliest, Storage/Capacity Management to predict the demand and make recommendations as to when enterprises need to go on cloud or extend or reduce the virtual capacity and Incident Management through ticket and resource prediction, analysis, and resolution.

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