In medieval Europe, stories circulated across linguistic boundaries, giving rise to multiple text versions and copies. Some of these stories, dealing for instance with king Arthur, Charlemagne or Alexander the Great, have been central in building a European literary tradition. During the process of their transmission, these stories evolved and adapted to different audiences, giving rise to many variations, blending inheritance with innovation. Many of them share common characters or places, giving rise to distinct cycles or storyverses. While philologists have long analyzed individual texts, a macro-level understanding of how these stories survived, transformed, or disappeared remains elusive. This gap underscores the need for large-scale, multilingual data collection to model textual transmission as a dynamic, multifaceted process. Our project addresses this challenge by building a corpus of medieval chivalric literature, structured around four core entities — story, text, witness, and document. The data is collected inside a relational database, using the Heurist software, to facilitate the collaboration among specialists across linguistic traditions. The data is aligned with major online resources for the different linguistic domains (e.g., Jonas, Arlima, Handschriftencensus) and integrates IIIF manifests, for the collection of full-text versions, using machine learning. The data pipeline transforms Heurist data into interoperable formats (TEI, JSON). Data is archived on Zenodo and will be made accessible following a Distributed Text Services API. By documenting our infrastructure and methodologies, we aim to support broader research in medieval studies and the digital humanities, fostering comparative analyses of textual transmission and its underlying variables.