Abstract: In an example method, a system accesses first data including text transcripts of a plurality of voice calls, and generates, based on the first data, labeled representations of the voice calls using the one or more computerized LLMs. The system generates the labeled representations by determining a plurality of contextual categories associated with the voice calls, segmenting the text transcript into a plurality of transcript segments, and associating each of the transcript segments with a respective one of the contextual categories. Further, the system generates second data representing the labeled representations of the voice calls and stores the second data using the one or more hardware storage devices.

Abstract: A multi-agent generative AI system is described. The system includes at least one data storage device storing a vector database, in which the vector database includes (i) a plurality of documents, and (ii) for each document of a plurality of documents, a respective document embedding that represents a semantic meaning of the document; at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to implement a computerized chat agent, a computerized retrieval agent, and a computerized triage agent.