Multi-service business platform system having custom workflow actions systems and methods
Techniques are provided for an automated crawler for crawling a primary online content object and storing a set of results, a parser for parsing the stored set of results to generate a plurality of key phrases and a content corpus, a plurality of models for processing at least one of the plurality of key phrases or the content corpus, wherein the processing results in a plurality of topic clusters which arrange topics within the primary online content object around a core topic based on semantic similarity, a suggestion generator for generating a suggested topic that is similar to at least one topic among the plurality of topic clusters and for storing the suggested topic, and an application for developing a strategy for development of online presence content.
1. A method comprising:
crawling a primary online content object to create a set of results from the crawling;
parsing the set of results to generate key phrases and a content corpus from the primary online content object;
processing the key phrases and the content corpus to create topic clusters which arrange topics within the primary online content object around a core topic based on semantic similarity;
generating a suggested topic that is similar to a topic of the topic clusters, wherein the suggested topic is generated based upon a competitiveness criteria corresponding to a measure of how a domain of an enterprise will be ranked for a term used to create the suggested topic, wherein the suggested topic is stored within a topic cluster data store;
generating, by an application, a strategy for development of online presence content, wherein the application includes a set of tools for exploring and selecting suggested topics stored in the topic cluster data store for generating the online presence content that is linked to the primary online content object by a cluster of semantically related content, wherein a subtopic is identified and recommended through the application for a selected suggested topic based upon the subtopic being validated using scoring metrics and a similarity of the subtopic to the core topic; and
providing a list of the suggested topics that are of highest semantic relevance for the enterprise based on the parsing of the set of results from the crawling.
2. The method of claim 1 , comprising:
executing an Artificial Intelligence/Machine Learning (AI/ML) concierge to host a chat interface through which content is displayed.
3. The method of claim 1 , comprising:
displaying, through the application, enrichment information through a sidebar component.
4. The method of claim 1 , comprising:
populating, using a conversation agent, a customer chat utilizing the suggested topic.
5. The method of claim 1 , comprising:
updating a knowledge graph with a new relationship between an entity and a new entity, wherein the application utilizing the knowledge graph generate a personalized message for an individual.
6. The method of claim 1 , comprising:
configuring a client-specific service system that includes the application for processing tickets utilizing a ticket pipeline.
7. The method of claim 1 , comprising:
hosting the application as a machine learning-as-a service system that generates inference outcomes in response to inference requests.
8. The method of claim 1 , wherein the application is provided with access to a customer relationship management system, and wherein the method comprises:
creating, by the application, a custom object within the customer relationship management system.
9. The method of claim 1 , wherein the application is provided with access to a customer relationship management system, and wherein the method comprises:
creating, by the application, an event record for a primary object stored within the customer relationship management system, wherein the event record associates an event type with the primary object.
10. A computing device comprising:
a memory comprising machine executable code; and
a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to perform operation comprising:
crawling a primary online content object to create a set of results from the crawling;
parsing the set of results to generate key phrases and a content corpus from the primary online content object;
processing the key phrases and the content corpus to create topic clusters which arrange topics within the primary online content object around a core topic based on semantic similarity;
generating a suggested topic that is similar to a topic of the topic clusters, wherein the suggested topic is generated based upon a competitiveness criteria corresponding to a measure of how a domain of an enterprise will be ranked for a term used to create the suggested topic, wherein the suggested topic is stored within a topic cluster data store;
generating, by an application, a strategy for development of online presence content, wherein the application includes a set of tools for exploring and selecting suggested topics stored in the topic cluster data store for generating the online presence content that is linked to the primary online content object by a cluster of semantically related content, wherein a subtopic is identified and recommended through the application for a selected suggested topic based upon the subtopic being validated using scoring metrics and a similarity of the subtopic to the core topic; and
providing a list of the suggested topics that are of highest semantic relevance for the enterprise based on the parsing of the set of results from the crawling.
11. The computing device of claim 10 , wherein the operations comprise:
generating, by the application, conversation information derived from a conversation recording.
12. The computing device of claim 10 , wherein the operations comprise:
generating, by the application, a workflow associated with an event, wherein the workflow is triggered based upon an occurrence of the event.
13. The computing device of claim 10 , wherein the application is provided with access to a customer relationship management system, and wherein the operations comprise:
propagating, by the application, a property change to a corresponding property of an object stored by the customer relationship management system, wherein the property change corresponds to an object type of a change event matched to a list of registration associations.
14. The computing device of claim 10 , wherein the application is provided with access to a customer relationship management system, and wherein the operations comprise:
generating, by the application, concatenated information to track a campaign using an urchin tracking module.
15. A non-transitory machine-readable storage medium comprising instructions that when executed by a machine, causes the machine to perform operations comprising:
crawling a primary online content object to create a set of results from the crawling;
parsing the set of results to generate key phrases and a content corpus from the primary online content object;
processing the key phrases and the content corpus to create topic clusters which arrange topics within the primary online content object around a core topic based on semantic similarity;
generating a suggested topic that is similar to a topic of the topic clusters, wherein the suggested topic is generated based upon a competitiveness criteria corresponding to a measure of how a domain of an enterprise will be ranked for a term used to create the suggested topic, wherein the suggested topic is stored within a topic cluster data store;
generating, by an application, a strategy for development of online presence content, wherein the application includes a set of tools for exploring and selecting suggested topics stored in the topic cluster data store for generating the online presence content that is linked to the primary online content object by a cluster of semantically related content, wherein a subtopic is identified and recommended through the application for a selected suggested topic based upon the subtopic being validated using scoring metrics and a similarity of the subtopic to the core topic; and
providing a list of the suggested topics that are of highest semantic relevance for the enterprise based on the parsing of the set of results from the crawling.
16. The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:
executing an Artificial Intelligence/Machine Learning (AI/ML) concierge to host a chat interface through which content is displayed.
17. The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:
displaying, through the application, enrichment information through a sidebar component.
18. The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:
populating, using a conversation agent, a customer chat utilizing the suggested topic.
19. The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:
updating a knowledge graph with a new relationship between an entity and a new entity, wherein the application utilizing the knowledge graph generate a personalized message for an individual.
20. The non-transitory machine-readable storage medium of claim 15 , wherein the operations comprise:
configuring a client-specific service system that includes the application for processing tickets utilizing a ticket pipeline.