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Operating a modern CDMO increasingly requires adopting cutting-edge data infrastructure and leveraging purpose-built models.
Operational excellence requires solving critical technical problems:
These are just a few of the fundamental scientific & operational challenges that define the industry today.
The foundation of these problems is in data infrastructure - critical information currently remains trapped in fragmented spreadsheets or multi-omics datasets. At Elucidata, we’re building Data products & models directly contribute to revenue goals.
In this webinar, we showcase our agent-based workflow to enable Business development teams at CDMOs to file competitive RFPs and win more deals. Our agentic system standardizes chemical properties from unstructured inquiries and helps prepare structured RFPs.
Operating a modern CDMO increasingly requires adopting cutting-edge data infrastructure and leveraging purpose-built models.
Operational excellence requires solving critical technical problems:
These are just a few of the fundamental scientific & operational challenges that define the industry today.
The foundation of these problems is in data infrastructure - critical information currently remains trapped in fragmented spreadsheets or multi-omics datasets. At Elucidata, we’re building Data products & models directly contribute to revenue goals.
In this webinar, we showcase our agent-based workflow to enable Business development teams at CDMOs to file competitive RFPs and win more deals. Our agentic system standardizes chemical properties from unstructured inquiries and helps prepare structured RFPs.
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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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You’ll see how CDMOs are evolving from data chaos to connected intelligence:
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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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