Generative AI performance in life sciences depends on more than the underlying model. Reliable results require high-quality, contextualized data, structured data models, and access to the relationships that give scientific information meaning. This article explains how retrieval-augmented generation (RAG), GraphRAG, model chaining, and knowledge graphs can improve the accuracy and relevance of generative AI applications.
It also examines the role of a unifying platform in preparing enterprise scientific data for AI. By connecting laboratory, manufacturing, clinical, and business processes, organizations can preserve data provenance, apply domain-specific ontologies, and give AI systems the context needed to interpret samples, experiments, instruments, materials, and results more reliably.
Alfredo Coviello, Director of Product at L7, explores how L7|ESP® combines data model management, knowledge graphs, normalized schemas, ontology integration, and workflow orchestration to support generative and agentic AI in regulated life sciences. These capabilities establish auditable connections between data and process execution, enabling traceability, human oversight, and governed AI workflows across research, development, manufacturing, diagnostics, and post-market operations.
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