Enable entity linking, classification, normalization, provenance capture, and semantic search across biologics datasets, and ground agentic AI and retrieval workflows (e.g., knowledge graph and GraphRAG approaches) so agents can reliably navigate, interpret, and act on scientific data. Partner closely with biologists, assay scientists, automation teams, and other domain experts to elicit, refine, and translate complex scientific concepts into computable semantic models, balancing scientific accuracy with practical usability and adoption.