Standardization for Data Connectivity, Analytics, and Actionable Solutions: Metabolomic Data as a Case Study

• Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
  Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.

• Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.

If you’re working with complex biological data, you may be asking:

• Can generative AI truly assist in scientific reasoning, not just data analysis?

• What does it mean for hypothesis generation, literature review, or even designing experiments?

• Could this accelerate—not replace—my discovery pipeline?

Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.