In the digital era, companies are inundated with vast amounts of data, making accurate and efficient access to information a relevant concern. This master thesis delves into the integration of generative artificial intelligence (AI) in the realm of enterprise search in collaboration with Iriscube Reply, proposing a pipeline that synergies semantic embeddings and similarity search with generative AI capabilities. At the core of this approach is an information retriever that leverages semantic embeddings to understand the hidden relationships and meanings within the enterprise data. By doing so, it can effectively identify and return a set of documents most pertinent to a user's query.