The text discusses extending the scope of chatbots and conversational AI experiences without relying on subject matter experts. It highlights the challenges of adding more intents and conversational flows, bringing context from virtual assistant interactions to live chat and calls, and integrating with different systems of record. The post aims to provide insights into how Symbl can help automate data labeling activities, understand new patterns in real-time, and transfer context between channels. Symbl's platform enables users to process unstructured conversation data, identify unknown patterns or scale the identification of known ones, using machine learning and natural language processing techniques. The text also introduces Trackers, which are user-defined entities that allow tracking critical moments in conversations across multiple use cases in real-time and asynchronously. By leveraging Symbl's Async APIs, users can speed up data labeling, use as a failover intent detection engine, or inform new conversation patterns for the same intent.