Retrieval-Augmented Generation (RAG) has emerged as a powerful approach to enhancing the accuracy and relevance of responses in natural language processing applications. This thesis explores the development and evaluation of an advanced RAG system tailored for a company specializing in survey analysis within the dental products industry. The primary goal of the system is to answer questions—ranging from simple factual inquiries to complex analytical queries—based on survey data collected from various business partners, in order to better and, expecially, easier understand various dental market actors and their relations. For instance, it will be much easier to simply ask the system “What was the most preferred brand for *DENTAL PRODUCT*_ among dentists in *COUNTRY* in *YEAR*?” instead of manually searching inside a database.