AI Safety Connect Platform

AI Safety Connect addresses a structural coordination gap between academic research and AI safety communities by constructing an integrated platform that systematically maps authors, publications, and thematic areas relevant to AI safety. The system relies on a hierarchical taxonomy of Areas, Fields, and Subfields to impose conceptual structure over a heterogeneous research landscape. A comparative evaluation of major scholarly indexers identified Semantic Scholar as the most stable and semantically informative source for large-scale extraction, enabling the retrieval of 185,715 documents aligned with the taxonomy. These data are processed through a Medallion Architecture deployed on AWS, yielding progressively structured representations that include deduplicated metadata, citation graphs, thematic distributions, and author-level profiles. A semantic layer based on E5-Large embeddings provides a high-dimensional representation of conceptual similarity across papers, complementing structural signals derived from citation networks. The platform exposes these components through REST and semantic-search APIs, supporting relevance-based retrieval and researcher matching. By integrating hierarchical querying, graph-based analysis, and embedding-space retrieval within a single computational framework, AI Safety Connect establishes a scalable approach for characterizing the AI safety ecosystem and identifying potential collaborations between academic and community actors.