Abstract: Techniques for business-to-business (B2B) chat routing are disclosed, including: receiving, by a B2B chatbot during a chat session with a user, user input including a user-supplied business name; performing a business lookup based at least on the user-supplied business name, to obtain a canonical business name and a unique business identifier associated with the canonical business name; performing a customer relationship management (CRM) system lookup based at least on the unique business identifier, to identify a corresponding business account; routing the chat session from the B2B chatbot to a human chat agent assigned to the corresponding business account.

Abstract: Techniques for business-to-business (B2B) chat routing are disclosed, including: receiving, by a B2B chatbot during a chat session with a user, user input including a user-supplied business name; performing a business lookup based at least on the user-supplied business name, to obtain a canonical business name and a unique business identifier associated with the canonical business name; performing a customer relationship management (CRM) system lookup based at least on the unique business identifier, to identify a corresponding business account; routing the chat session from the B2B chatbot to a human chat agent assigned to the corresponding business account.

Abstract: Embodiments described herein include receiving an application flow defining logic for triggering automated actions for a candidate post on one or more web-based communication channels. An artificial intelligence (AI) powered web crawler may crawl a set of websites for target content. Responsive to identifying target content, machine learning may be leveraged to identify an entity name for an entity that is a subject of the target content and associate the entity with one or more event categories. Based on the event categories, a segment may be generated at runtime for the application flow where segment membership was not known at the time the application flow was defined or before application runtime.

Abstract: Embodiments described herein include receiving an application flow defining logic for triggering automated actions for a candidate post on one or more web-based communication channels. An artificial intelligence (AI) powered web crawler may crawl a set of websites for target content. Responsive to identifying target content, machine learning may be leveraged to identify an entity name for an entity that is a subject of the target content and associate the entity with one or more event categories. Based on the event categories, a segment may be generated at runtime for the application flow where segment membership was not known at the time the application flow was defined or before application runtime.