Abstract: 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.

Abstract: 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.

Abstract: In some implementations, a device may receive project management data including text descriptions related to one or more requirements associated with a project. The device may identify a set of work items associated with the project and a set of existing dependencies among the set of work items associated with the project. The device may generate, based on the text descriptions related to the one or more requirements associated with the project, a set of recommended dependencies among the set of work items associated with the project using an artificial intelligence model. The device may generate a user interface to visually indicate information related to the set of existing dependencies and the set of recommended dependencies among the set of work items associated with the project and to provide one or more elements to manage the set of existing dependencies and/or the set of recommended dependencies.

Abstract: A device may receive entity data identifying an entity and may identify harvested templates, contract profiles, and commitment accuracy scores, based on the entity data. The device may process the harvested templates, the contract profiles, and the commitment accuracy scores, with a first machine learning model, to generate first templates and may process the harvested templates, the contract profiles, and the commitment accuracy scores, with a second machine learning model, to generate second templates. The device may process the first templates and the second templates, with a third machine learning model, to generate final templates and final rankings for the final templates and may select a template from the final templates based on the final rankings. The device may process the template, with a fourth machine learning model, to identify deliverables associated with the template and may generate and implement a strategic plan based on the template and the deliverables.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.