This article presents the ongoing work on developing a methodology for the systematic analysis and narrative of heritage image archives, focusing on the photo archive by Robert Gerstmann of the Sacambaya Expedition in 1928. This work combines state-of-the-art artificial intelligence techniques, such as advanced algorithms used in computer visión like convolutional neural networks, with Large Language Models (LLM) for generation purposes. The intent is to establish a practical and accessible framework in this area for institutions and individuals. The proposed method incorporates human-generated image labels with LLMs to produce narratives that aid researchers and users in their sense-making process while they explore a large archive of images. Through this iterative process, we aim to contribute not only to the understanding of this specific historical photo collection but also to the broader development of scalable solutions for the exploration and interpretation of heritage image archives. We seek to achieve a deeper understanding of the contents and meanings of the analyzed files, suggesting and highlighting new clustering of these materials and thematic/narrative connections that may not have been considered by a human observer.