In recent years there has been a surge in the development and adoption of Retrieval-Augmented Generation (RAG) pipelines, as they constitute a cost-effective, flexible, and highly customizable way to leverage the advantages of LLMs on private and custom data. While modern RAG pipelines can work with almost any type of data, existing document processing systems focus predominantly on textual content, often ignoring visual elements. This text-centric approach may suffice when text constitutes the main information carrier, but it fails to extract all meaningful insights from documents like slide presentations, where content is equally distributed across text, charts, images, and tables that often interact with each other to convey complete information.