If you're already building a knowledge graph - or stuck trying to get more out of the one you have - this session is for you.
Join us on 5 August 2025, to see how Polly KG goes beyond static schemas and surface-level Natural Language processing to deliver answers your team can trust.
Built for the pace of biomedical innovation, Polly KG lets you ingest your internal data, extend schemas to fit your science, and interact with your data through natural language. All without retraining a team of data engineers.
If you're already building a knowledge graph - or stuck trying to get more out of the one you have - this session is for you.
Join us on 5 August 2025, to see how Polly KG goes beyond static schemas and surface-level Natural Language processing to deliver answers your team can trust.
Built for the pace of biomedical innovation, Polly KG lets you ingest your internal data, extend schemas to fit your science, and interact with your data through natural language. All without retraining a team of data engineers.
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%.
%201.svg){kind=icon}
Use natural language to query multimodal datasets - without needing data engineering support.
Learn how Polly KG brings together structured and unstructured data - from single-cell and GWAS to experiment logs and literature - into one living graph.
Every instance is tailored: from scoring logic and ontologies to output formats and access controls. This isn’t optional - it’s what makes scientific AI usable.
Polly KG combines expert-curated knowledge with transparent, interpretable AI - bridging the gap between algorithmic output and scientific reasoning.
Get a look behind the scenes at how Polly KG powers discovery workflows across oncology, rare diseases, and translational research. One therapeutics company cut its hypothesis cycle from 6–8 months to just 2 weeks.
See how it all works in a live walkthrough - from querying in plain English to exploring custom-scored results across multimodal datasets.
.png)
.png)
.png)
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.
.png)
.svg)
.svg)
%201%20(1).svg)
.svg)
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.png){kind=spacer}
.webp)
.webp)
.webp){kind=spacer}
.webp){kind=spacer}
.webp)
.webp)
.webp)
.webp){kind=spacer}
.webp)
.webp)
.webp)
.webp)
.webp)
.svg)
%20(1).svg)
