SYSTEMS AND METHODS FOR ENHANCING CUSTOMER EXPERIENCE

1 . A method for enhancing customer experience, the method comprising:

receiving one or more customer inputs while a customer is conversing with a user;

predicting a software application of interest for the customer based on the one or more customer inputs; and

generating a buildcard based on the predicted software application.

2 . The method of claim 1 , wherein predicting the software application comprises:

determining an intent of the customer based on the customer input using an intent classifier model;

identifying one or more sections of the conversation based on the determined intent and the one or more customer inputs;

running one or more models for the identified one or more sections of conversation; and

predicting the software application for the customer based on an output of the one or more models.

3 . The method of claim 2 , further comprises:

determining one or more templates intended by the customer for the software application based on the output of the one or more models; and

predicting the software application of interest for the customer based on the determined one or more templates.

4 . The method of claim 3 , wherein generating the buildcard comprises:

determining one or more features intended by the customer based on the output of the one or more models; and

generating the buildcard based on the predicted software application and the determined one or more features.

5 . The method of claim 2 , wherein the one or more models comprises at least one of a feature tagging model, a feature recommendation model, a template recommendation model, an entity tagger model, a response classifier model, and a prompt mirroring model.

6 . The method of claim 1 , further comprises:

generating a complexity of the software application and a timeline required for developing the software application based on the generated buildcard; and

displaying the generated complexity of the application and the timeline required on a user device communication console while the user is conversing with the customer.

7 . The method of claim 6 , wherein generating the complexity of the software application and the timeline required for developing the software application comprises:

retrieving the historical data from a database;

selecting a machine learning model from a plurality of machine learning models, wherein each of the plurality of machine learning models includes a Light Gradient Boosting model;

inputting the historical data and the generated buildcard to the selected machine learning model; and

generating the complexity of the software application and the timeline required for developing the software application based on an output of the selected machine learning model.

8 . A computer system to enhance customer experience, the computer system comprises:

a memory; and

a processor coupled to the memory and configured to:

receive one or more customer inputs while a customer is conversing with a user;

predict a software application of interest for the customer based on the user inputs; and

generate a buildcard based on the predicted software application.

9 . The computer system of claim 8 , wherein to predict the software application, the processor is configured to:

determine an intent of the customer based on the customer input using an intent classifier model;

identify one or more sections of conversation based on the determined intent and user input

run one or more models for the identified one or more sections of conversation; and

predict the software application for the customer based on an output of the one or more models.

10 . The computer system of claim 9 , wherein the processor is further configured to:

determine one or more templates intended by the customer for the software application based on the output of the one or more models; and

predict the software application of interest for the customer based on the determined one or more templates.

11 . The computer system of claim 10 , wherein to generate the buildcard, the processor is configured to:

determine one or more features intended by the customer based on the output of the one or more models; and

generate the buildcard based on the predicted software application and the determined one or more features.

12 . The computer system of claim 9 , wherein the one or more models comprises at least one of a feature tagging model, a feature recommendation model, a template recommendation model, an entity tagger model, a response classifier model, and a prompt mirroring model.

13 . The computer system of claim 8 , wherein the processor is further configured to:

generate a complexity of the software application and a timeline required for developing the software application based on the generated buildcard; and

display the generated complexity of the application and the timeline required on a user device communication console while the user is conversing with the customer.

14 . The computer system of claim 13 , wherein to generate the complexity of the software application and the timeline required for developing the software application, the processor is configured to:

retrieve the historical data from a database;

select a machine learning model from a plurality of machine learning models, wherein each of the plurality of machine learning models includes a Light Gradient Boosting model;

input the historical data and the generated buildcard to the selected machine learning model; and

generate the complexity of the software application and the timeline required for developing the software application based on an output of the selected machine learning model.

15 . A computer readable storage medium having data stored therein representing software executable by a computer, the software comprising instructions that, when executed, cause the computer readable storage medium to perform:

receiving one or more customer inputs while a customer is conversing with a user;

predicting a software application of interest for the customer based on the one or more customer inputs; and

generating a buildcard based on the predicted software application.

16 . The computer readable storage medium of claim 15 , wherein predicting the software application comprises:

determining an intent of the customer based on the customer input using an intent classifier model;

identifying one or more sections of the conversation based on the determined intent and the one or more customer inputs;

running one or more models for the identified one or more sections of conversation; and

predicting the software application for the customer based on an output of the one or more models.

17 . The computer readable storage medium of claim 16 , further comprises:

determining one or more templates intended by the customer for the software application based on the output of the one or more models; and

predicting the software application of interest for the customer based on the determined one or more templates.

18 . The computer readable storage medium of claim 17 , wherein generating the buildcard comprises:

determining one or more features intended by the customer based on the output of the one or more models; and

generating the buildcard based on the predicted software application and the determined one or more features.

19 . The computer readable storage medium of claim 16 , wherein the one or more models comprises at least one of a feature tagging model, a feature recommendation model, a template recommendation model, an entity tagger model, a response classifier model, and a prompt mirroring model.

20 . The computer readable storage medium of claim 15 , further comprises:

generating a complexity of the software application and a timeline required for developing the software application based on the generated buildcard; and

displaying the generated complexity of the application and the timeline required on a user device communication console while the user is conversing with the customer.