
Polly helps drug discovery teams find, harmonize, and analyze multi-omics and clinical data - so you can move from fragmented datasets to decision-ready insights without engineering bottlenecks.

Discover high-quality public and proprietary datasets relevant to your research - across multi-omics, clinical, and literature sources.
Search across curated biomedical datasets with Polly Scout
Identify relevant cohorts, biomarkers, and disease models
Reduce time spent on manual dataset discovery
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Transform fragmented biomedical data into structured, analysis-ready datasets using Polly’s LLM-powered harmonization engine.
Convert unstructured data from PDFs and samples into structured formats
Map data to standard ontologies and vocabularies
Ensure consistent, high-quality datasets across sources

Analyze harmonized data to uncover mechanisms, validate hypotheses, and identify patient subgroups faster.
Run advanced analytics across multi-omics datasets
Identify driver genes and disease subtypes
Predict toxicity and treatment response
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Centralize all your datasets into a connected knowledge layer that supports collaboration, reproducibility, and scale.
Ensure even historical data has context and is findable. Avoid duplication and having to regenerate data.
Enable cross-study and cross-modality insights
Polly's powerful harmonization engine processes measurements, links to harmonized metadata and transforms them into a Unified Data Model.
Automatically curate, structure, and standardize raw biomedical data - reducing manual effort and accelerating downstream analysis.
Identify disease mechanisms, biomarkers, and patient subgroups using integrated multi-omics and clinical data.
Work seamlessly across genomics, transcriptomics, clinical, and literature data - all within one platform.
Polly helps R&D teams in scaling their data science and making heterogeneous biological data 'AI-ready'. The platform has been utilized by multiple biopharma partners to harmonize millions of datasets across a variety of enterprise-grade drug discovery initiatives.
Biomedical data harmonized every month
Accuracy on Data Harmonization
Unique biological data types transformed
Automation of internal ETL processes