In the context of earnings calls, AI is being utilized to analyze the impact of artificial intelligence on various companies, particularly those in tech and financial services. A Retrieval Augmented Generation (RAG) system was developed to extract insights from live earnings calls, leveraging OpenAI's Whisper model for transcription and a large language model (LLM) such as GPT-3.5 for generating conversational answers. The RAG system involves indexing pipeline steps including data collection, transcription, embedding, and indexing, followed by a query pipeline that uses both vector and semantic retrieval to select relevant documents and prompt the LLM with user questions. This approach allows for the generation of condensed answers grounded in recent data, overcoming training data limitations. The development of this RAG system was made possible using deepset Cloud's AI platform, which streamlines the development life cycle by providing a unified environment and customizable components.