AWS GraphRAG framework cuts drug discovery cycles by 87 percent

The architecture combines Amazon Neptune Analytics with the Bedrock AI platform to overcome the industry-wide struggle of data silos. Historically, research stalled because critical context remained trapped in disconnected storage environments or departed with staff. The new framework uses Anthropic’s Claude 4.5 Sonnet to parse unstructured documents, mapping findings to standardized medical ontologies. This creates a deterministic foundation where every AI-generated answer is linked to verifiable citations and a traceable reasoning path.

Beyond speed, the system addresses long-term data decay. When senior researchers leave, their project history and experimental outcomes remain indexed within the graph, allowing new personnel to query past decisions instantly. While the setup requires strict schema governance to avoid relational mapping errors, early adopters report an 85 percent improvement in data retrieval speeds and a 70 percent drop in review times. The modular design ensures that teams can swap language models or update graph structures without rebuilding the underlying application, providing a scalable blueprint for any enterprise facing fragmented legacy data.