Consultancy assistance and correlation systems and methods

A software consultancy assistance system, comprising:

• a memory;
• and
• a processor operatively coupled with the memory, wherein the processor is configured to execute program code to:
• extract subject matter items from one or more webpages of one or more internet platforms;
• semantically process and analyze text data or image data of the one or more webpages using natural language processing to determine one or more sentiments of one or more third-party evaluators for the subject matter items based on analysis from the one or more third-party evaluators considering one or more client preferences and analysis of anonymous aggregate client data;
• associate analyzed collected data of the subject matter items with one or more consultant workflows;
• combine the one or more consultant workflows with the one or more client preferences;
• automatically process and create one or more best-fitting subject matter items options for one or more selections by one or more clients after the determination of the one or more sentiments of the one or more third-party evaluators is made;
• track and update the one or more selections of the one or more clients to update the one or more client preferences;
• and
• compare the one or more selections of the one or more clients to the one or more sentiments of the third-party evaluators to determine a level of client expertise after the automatic processing and creating of the one or more best-fitting subject matter items options, wherein the level of client expertise is calculated based on a percentage comparing how close the one or more client preferences are to the one or more sentiments of the one or more third-party evaluators, and wherein the greater the percentage is calculated that the one or more client preferences match the one or more sentiments of the one or more third-party evaluators the greater the level of client expertise.

A software method of a consultancy assistance system, comprising:

• extracting subject matter items from one or more webpages of one or more internet platforms;
• extracting text data or image data from the one or more webpages that comprises third-party evaluations of the subject matter items;
• accessing one or more client preferences for one or more clients related to the subject matter items or other subject matter items;
• semantically processing and analyzing, based on the one or more client preferences and anonymous aggregate client data, the text data or image data from the one or more webpages using natural language processing to determine one or more sentiments of the third-party evaluations with regard to the subject matter items;
• associating analyzed collected data of the subject matter items with one or more consultant workflows based on matching the analyzed collected data of the subject matter items with the one or more client preferences using the one or more consultant workflows;
• combining the one or more consultant workflows with the one or more client preferences;
• based on the combination of the one or more consultant workflows and the one or more client preferences, automatically processing and creating one or more best-fitting subject matter item options for selection by the one or more clients after the determination of the one or more sentiments of the one or more third-party evaluations is made;
• presenting the best-fitting subject matter item options to the one or more clients for selection;
• receiving information identifying one or more selections of the best-fitting subject matter item options by the one or more clients;
• iteratively performing the following operations in response to receipt of each selection of the one or more selections:
• identifying online platform-independent text associated with the selection;
• semantically analyzing the online platform-independent text associated with the selection;
• updating the one or more client preferences based on the selection and the semantic analysis of the online platform-independent text associated with the selection;
• and
• determining a level of client expertise based on calculating a percentage comparing how close the one or more client preferences are to the one or more sentiments of the one or more third-party evaluations, and wherein the greater the percentage is calculated that the one or more client preferences match the one or more sentiments of the one or more third-party evaluations the greater the level of client expertise.