SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE ENHANCEMENTS IN AUTOMATED CONVERSATIONS

1 . A method for generating custom client intents in an AI driven conversation system comprising:

receiving data for the client;

auto-generating a series of standard intent categories responsive to the client data;

receiving a custom question from the client;

classifying the custom question to the standard intent categories using at least one AI classification models, wherein the classifying calculates a percentage confidence of a match between the custom question and the intent categories;

displaying the classification results along with the percentage confidence to the client; and

receiving feedback from the client to either merge the custom question with one of the standard intent categories or generate a new intent category.

2 . The method of claim 1 , wherein merging the custom question with one of the standard intent categories updates the AI classification models as an additional training variant for machine learning.

3 . The method of claim 1 , further comprising comparing the percentage confidence to a threshold.

4 . The method of claim 3 , wherein the threshold is 90%.

5 . The method of claim 3 , wherein the threshold is 80%

6 . The method of claim 3 , wherein the threshold is 70%

7 . The method of claim 3 , wherein the threshold is between 65 to 92%.

8 . The method of claim 3 , wherein when the feedback is to create a new intent category, generating the new intent category when the confidence percentage is below the threshold, and requesting a policy exception when the confidence percentage at or above the threshold.

9 . The method of claim 8 , wherein the policy exception requires policy review by a data scientist.

10 . The method of claim 1 , further comprising generating the new intent category responsive to the feedback.

11 . The method of claim 10 , further comprising requiring the client to provide a dataset of variants for the new intent category as a dataset.

12 . The method of claim 11 , further comprising providing the client feedback when the dataset is sufficient.

13 . The method of claim 12 , wherein the sufficiency of the dataset is based upon at least one of the number of variants in the dataset, and the degree of factor difference between the various variants.

14 . The method of claim 11 , further comprising training the AI models for the new intent category using the dataset.

15 . The method of claim 10 , further comprising:

receiving a message from a contact;

classifying the message against the standard intent categories and the new intent categories; and

generating a response for the message responsive to the classification

16 . A method for contact updating in a conversation between an original contact and a dynamic messaging system comprising:

receiving a response message;

classifying the response message using at least one AI model, wherein the classifying indicates the original contact is no longer with an organization;

deactivating a record for the original contact;

updating a conversation stage to ‘contact stopped’;

updating a conversation status to ‘no longer with company’;

parsing the response message for an alternate contact information; and

when alternate contact information is present sending a notification to a client user informing that the original contact is no longer with company and that alternate contact information was found.

17 . The method of claim 16 , further comprising receiving feedback from the client user that a new contact should be created.

18 . The method of claim 16 , further comprising generating the new contact using the alternate contact information.

19 . The method of claim 18 , further comprising validating the new contact.

20 . The method of claim 18 , further comprising messaging the new contact.

21 . The method of claim 16 , further comprising, responsive to a configuration by the client user, notifying the client user of the contact disqualification when no alternate contact information is found.