Abstract The total volume of data created and consumed worldwide is growing rapidly, supporting numerous digital services that depend on detailed individual information. This growth raises privacy concerns, making anonymisation essential to address them. When choosing a technique, data practitioners need to consider the type of data, privacy regulations, and data utility requirements. The research landscape of anonymisation techniques is broad and continues to evolve. This creates a need for a system that can accept the requirements of a data-collecting use case, understand its constraints, search the scientific literature, and recommend appropriate anonymisation methods. This work proposes a recommender system for data anonymisation methods, built using Retrieval-Augmented Generation (RAG). A knowledge base of research papers on anonymisation is created and stored in a vector database. A simple RAG pipeline is set up as the starting point. Several types of improvements are tested on top of this, focusing on retrieval settings, re-ranking, query enrichment, and planning. To assess the system, a multidimensional evaluation framework is designed to cover retrieval quality, generation quality, and recommendation quality. Given the absence of a standardised benchmark for this task, a custom evaluation framework was developed from the knowledge base to allow testing. The results show that combining retrieval and generation enhancements substantially outperforms the naive baseline. The findings demonstrate that an enhanced RAG pipeline can provide accurate, constraint-aware recommendations for anonymisation methods.