CATO is an AI data analysis and literature review tool for biomedical researchers, built by NYRUS AI. ChatGPT is an excellent general assistant, and many researchers use both. The difference is what happens after you ask the question.
Describing analysis vs running it
A general chatbot explains what an analysis would look like, or produces code for you to run elsewhere. CATO actually executes the statistics on your real dataset in a live, sandboxed Python and R environment, and hands back the figures, tables, p-values, and the exact code and assumptions behind every result.
Citations that exist
Fabricated references are the best-known failure mode of general chatbots in academia. CATO pulls and formats citations directly from PubMed, ClinicalTrials.gov, bioRxiv and medRxiv, arXiv, OpenAlex, Semantic Scholar, and patent databases, so nothing is invented, and a QA step flags uncited claims in generated documents.
Built for the research workflow
CATO keeps persistent projects with your datasets, figures, and conversations; edits figures in a no-code editor; exports reports to Word, PowerPoint, and Excel; and reads 50+ research file formats including SPSS, SAS, Stata, FASTQ, VCF, and DICOM without conversion.
Cost for the usage you actually get
For research-grade analysis, CATO ends up cheaper than foundation-model subscriptions relative to the work delivered: Standard is $20 per month, Pro is $100 per month with systematic reviews, and Pay-as-you-go has no monthly fee.
You can try CATO at cato.nyrus.ai, or learn more at nyrus.ai/cato.