Poster presented at the Open Science Conference 2025 Abstract: The OpenAIRE Graph stands at the forefront of research infrastructure innovation, combining cutting-edge AI techniques with Open Science principles to process and analyze 400M+ research records monthly, including 290M+ publications, 82M+ datasets, and 1M+ software entries. More than a metadata aggregator, the OpenAIRE Graph fuses diverse sources into a richly linked, machine-actionable research ecosystem, powered by an advanced AI-driven analytical workflow that elevates data quality, connectivity, and usability through: automated metadata enrichment of persistent identifiers (e.g. ORCID, ROR), Fields of Science classifications, Open Access status, licensing terms, and semantic types using Natural Language Processing; entity recognition and disambiguation using ML models to connect authors, institutions, projects, and funders across heterogeneous sources; knowledge graph embeddings and similarity scoring to detect and link conceptually related research artefacts, enabling cross-disciplinary exploration and contextualization; relationship extraction and network mapping, to uncover latent connections among research outputs, such as citations, co-authorships, and funding dependencies. These mechanisms are continuously refined using feedback loops, benchmarking datasets, and community input, ensuring the Graph remains a trusted foundation for Open Science monitoring, research assessment, and discovery. Our demonstration will show how these AI capabilities operationalize the FAIR principles, support evidence-based policymaking, and streamline research workflows. This session will offer practical insights for those exploring AI-enhanced infrastructures for scholarly communication and assessment.