Abstract: A system and method for generating LLM-based chatbot responses are provided. The method includes generating, using a first specific-trained language model (STLM), which returns a set of filters based on an input question, wherein the input question relates to at least one sales call; filtering a call dataset based on the set of filters, wherein the set of filters is applied to metadata included in the call dataset, wherein the call dataset further includes at least transcripts; embedding the input question into a vector representation; comparing the vector representation of the input question to vector representations of textual information stored in the call dataset to retrieve a target dataset; engineering a prompt to provide a single coherent command including information in the target dataset and the input question; and feeding the engineered prompt to a second STLM to retrieve an answer to the input question.

Abstract: A system and method for generating LLM-based chatbot responses are provided. The method includes generating, using a first specific trained language model (STLM), which returns a set of filters based on an input question, wherein the input question relates to at least one deal; filtering a deal dataset based on the set of filters, wherein the set of filters are applied to metadata included in the deal dataset, wherein the deal dataset further includes at least call transcripts related to sales deals; embedding the input question into a vector representation; comparing the vector representation of the input question to vector representations of textual information stored in the deal dataset to retrieve a target dataset; engineering a prompt to provide a single coherent command including information in the target dataset and the input question; and feeding the engineered prompt to a second STLM to retrieve an answer to the input question.

Abstract: A system and method for streamlining a deal pipeline based on large language models are provided. The method includes encoding an input query into a numerical representation in a business domain; retrieving data from a deal knowledge base based on the numerical representation; generating a prompt based on the encoded input query and data retrieved from the knowledge base; feeding the prompt to a generic-trained language model; and ranking responses provided by the generic-trained language model, wherein the responses are related to at least a deal pipeline.

Abstract: A system and method for generating LLM-based chatbot responses are provided. The method includes generating, using a first specific-trained language model (STLM), which returns a set of filters based on an input question, wherein the input question relates to at least one sales call; filtering a call dataset based on the set of filters, wherein the set of filters is applied to metadata included in the call dataset, wherein the call dataset further includes at least transcripts; embedding the input question into a vector representation; comparing the vector representation of the input question to vector representations of textual information stored in the call dataset to retrieve a target dataset; engineering a prompt to provide a single coherent command including information in the target dataset and the input question; and feeding the engineered prompt to a second STLM to retrieve an answer to the input question.

Abstract: A system and method for streamlining language data processing through a multi-stage pipeline is presented. The method includes creating, based on input data, a targeted message for a lead using a trained generator, wherein the input data includes lead data; causing projection of the targeted message via a user device of the lead; determining at least a label for interaction data by applying a classifier, wherein the interaction data is collected from causing the projection of the targeted message to the lead, wherein the interaction data are processed for classification; determining a next step based on the determined at least a label, wherein the next step is determined with respect to the lead; and performing the next step upon determination.

Abstract: A system and method for generating LLM-based chatbot responses are provided. The method includes generating, using a first specific trained language model (STLM), which returns a set of filters based on an input question, wherein the input question relates to at least one deal; filtering a deal dataset based on the set of filters, wherein the set of filters are applied to metadata included in the deal dataset, wherein the deal dataset further includes at least call transcripts related to sales deals; embedding the input question into a vector representation; comparing the vector representation of the input question to vector representations of textual information stored in the deal dataset to retrieve a target dataset; engineering a prompt to provide a single coherent command including information in the target dataset and the input question; and feeding the engineered prompt to a second STLM to retrieve an answer to the input question.