Accenture Global Solutions Limited

Sangeetha Jayaram

Koushik M Vijayaraghavan

Aditi Kulkarni

Parul Jagtap

Bhaskar Ghosh

Rajesh Nagarajan

Roopalaxmi Manjunath

Purnima Jagannathan

Pradeep Senapati

Jeffson Felix Dsouza

Sudhir Hanumanthappa

Badrinath Parameswar

Manas Mishra

Gaurav Goenka

Vaijayanthi Ramaswamy

Mahesh Zurale

Ramanathan Venkataraman

Gaurav Sood

Ranjith Tharayil

Sudha Srinivasan

Nishanth Kumar

Rajendra Tanniru Prasad

A system may receive enterprise information associated with a client enterprise. The system may select, using an industry analysis model, a set of queries associated with obtaining status information that is associated with a technology profile of the client enterprise. The system may generate client data that is associated with the enterprise information and the status information. The system may convert, using a matrix factorization technique, the client data associated with the client enterprise to a client matrix. The system may convert, using the matrix factorization technique, reference data associated with reference enterprises to a reference matrix. The system may determine, based on a comparison of the client matrix and the reference matrix, a set of scores associated with technology metrics of the technology profile. The system may perform an action associated with the client enterprise based on the set of scores.

Enterprise management system using artificial intelligence and machine learning for technology analysis and integration

A device may receive project management data associated with development of a software product and may process a first portion of the project management data, with first models, to generate timeliness scores and an overall timeliness score for the software product. The device may process a second portion of the project management data, with second models, to generate quality scores and an overall quality score for the software product and may process a third portion of the project management data, with third models, to generate product readiness scores and an overall product readiness score for the software product. The device may utilize a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product and may perform one or more actions based on the success probability for the software product.

Utilizing a combination of machine learning models to determine a success probability for a software product

Growing community of inventors

Mumbai, India

Sangeetha Jayaram