When developing drugs - for example, those designed to cross the blood–brain barrier to treat CNS disorders - development teams need to know how competing drugs are performing in the market. This competitive intelligence often lies buried in vast volumes of clinical trial publications.
The current process involves - Manually reviewing trial reports, extracting data from PDFs, and converting unstructured information into usable tables. By the time this process is completed and the data is ready, the opportunity to act on insights may already be lost.
With a multi-agentic AI approach, Polly Xtract automates the extraction and structuring of PK/PD data from clinical trial reports.
When developing drugs - for example, those designed to cross the blood–brain barrier to treat CNS disorders - development teams need to know how competing drugs are performing in the market. This competitive intelligence often lies buried in vast volumes of clinical trial publications.
The current process involves - Manually reviewing trial reports, extracting data from PDFs, and converting unstructured information into usable tables. By the time this process is completed and the data is ready, the opportunity to act on insights may already be lost.
With a multi-agentic AI approach, Polly Xtract automates the extraction and structuring of PK/PD data from clinical trial reports.
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%.
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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.
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