Webinar
Upcoming Webinar
In collaboration with

Adopting a Modern Data Strategy, Scalable Data Governance and Mesh

What the AI Co-Scientist Paper Actually Demonstrates for Biologists and Data Scientists

A modern data strategy gives you a comprehensive plan to manage, access, analyze, and act on data. As a result, there is a shift to consider the adoption of a data mesh architecture where data is organized by domain, clear ownership of data and technology stack is enhanced, & a more agile setup is achieved. This would enable R&D teams in clinical & pre-clinical set-ups to further enhance discovery programs by achieving faster breakthroughs.

Please enter only business email id.
Thank you for registering.

Please check your inbox for further details to join this webinar.
Oops! Something went wrong while submitting the form.
Registrations are closed!

Real-World Applications We’ll Cover

  • 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%.

Register now

What You’ll Learn

Register now

Why This Matters for Biomedical Researchers

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.

Register now
Meet the Experts of this discussion
Key Takeaways
How data providers ensure adherence to quality standards through validation and compliance.
How GUI-based workflows, CLI tools, and collaborative workspaces enable streamlined data ingestion and synchronization at scale.
Understand how automated pipelines assess conformance, plausibility, and consistency, ensuring high-quality, AI-ready data products.
Key Takeaways
Reduce operational costs by streamlining data delivery through reusable, governed products.
Accelerate diagnostic development and clinical trial execution by delivering compliant, high-quality data at scale.
Improve audit readiness and regulatory confidence through governed data products and built-in quality assurance.
Equip cross-functional teams to act on trusted data—faster, and with greater confidence.
Who Should Attend?

All Webinars