Enhancing Archival Transparency with AI: A RAG-based Case Study on Cinematic Heritage
Enea Ahmedhodzic, Andrea Trentini · Archiving Conference · 2025
Cinematic archives preserve an invaluable heritage, yet accessing their content is often challenging due to data fragmentation, inconsistent standards, and the absence of user-friendly tools. Even when materials are available, consultation may require archival staff or specialized knowledge. This paper introduces Valter, a prototype AI chatbot developed as a case study for the Film Center Sarajevo (FCS), which explores how retrieval-augmented generation (RAG) can support transparency and accessibility in under-resourced archival settings. The system uses semantic search over multilingual embeddings to retrieve relevant information and generate answers in natural language. While still under development, Valter demonstrates the potential to enhance resource discovery and metadata validation, offering a replicable approach that could inform future digital access strategies across similar institutions.