Built for world's leading life science teams
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Why 'Drylab'?
In science, a dry lab refers to the part of research that happens outside the wet bench-like analyzing data, designing experiments, and running simulations. It's half of the scientific workflow, and just as demanding. At Drylab, we believe the dry side of science shouldn't be tedious. Our mission is to make it seamless-so scientists spend less time on manual work and more on discoveries.
Who is Drylab for?
Drylab is designed for leading life science companies, biotech startups, and research labs to analyze large biological datasets seamlessly and accelerate scientific discoveries.
What makes Drylab Al different from general Al tools like ChatGPT?
(1) Unlike general AI tools, Drylab is trained on biological datasets, experimental data, and scientific workflows, allowing it to better understand biological concepts and perform multi-modal data analysis more effectively. (2) We focus on making data analysis - one of the most labor-intensive part of the bio workflow seamless. While general AI can generate code snippets based on your request, you typically have to run them separately — on a coding platform, server, or your own machine. Drylab makes it simple: it suggests high-quality plans, generates code, and runs analyses with one click, using pipelines that learn from your workflow.
How can I ensure that my lab's internal data and information remain secure?
Our platform includes a secure storage system called 'Vault' to protect your lab's sensitive research data. Vault uses strong encryption—like locking your data with a digital key—and multi-factor authentication to ensure that only authorized users can access it. Vault will monitor data access in real time and automatically back everything up, providing both security and peace of mind. Vault is on track to achieve full enterprise-grade security benchmarking by September 2025.
What kind of datasets can Drylab run?
Drylab supports multimodal datasets, including—but not limited to—genomics, transcriptomics, proteomics, epigenomics, metabolomics, imaging, spatial omics, and clinical or phenotypic data.
