Accenture Global Solutions Limited

Parul Jagtap

Aditi Kulkarni

Koushik M Vijayaraghavan

Rajesh Nagarajan

Roopalaxmi Manjunath

Sangeetha Jayaram

Bhaskar Ghosh

Purnima Jagannathan

Pradeep Senapati

Jeffson Felix Dsouza

Badrinath Parameswar

Sudhir Hanumanthappa

Manas Mishra

Naveen Kumar

Vaijayanthi Ramaswamy

Gaurav Goenka

Gaurav Sood

Mahesh Zurale

Ramanathan Venkataraman

Sudha Srinivasan

Ranjith Tharayil

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

A device may receive audio-video content regarding a system; segment the audio-video content to generate audio content and video content; process the audio content based on generating the audio content; process the video content based on generating the video content; identify a hierarchy for the set of sections based on processing the audio content and the video content; generate a system understanding document based on the hierarchy of the set of sections and based on the audio content and the video content; and store the system understanding document in a knowledge base.

Information transition management platform

Growing community of inventors

Delhi, India

Parul Jagtap