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Develop a data infrastructure with tools that help you find, manage and share your data. Our ontology-led approach delivers precise results.

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Why choose SciBite?

At SciBite, we're passionate about helping our customers get more from their data. We love what we do and we think you'll love working with us, here's why...

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    Built by scientists for scientists

    Our suite of semantic solutions is the culmination of the perfect storm of tens of years of experience

  • Pictograph / icon - Magnifying Glass

    Unique deep learning approach

    Transforming previously unusable but scientifically relevant textual content into machine-readable clean data

  • Pictograph / icon - Heart

    Putting customers at the heart

    Our philosophy is to listen, engage & work together with our customers to make ground-breaking achievements

  • Pictograph / icon - Pharmaceutical

    Supporting the top 20 pharma

    Our customers are utilising higher quality data, integrating more data much faster with greater accuracy

Our customers

News and opinion

  1. Functional foods: Revolutionizing health through diet and innovation

    Headshot of Mark Streer, SciBite

    Embark on a journey through the transformative realm of functional foods, where the convergence of nature's wisdom and cutting-edge innovation is reshaping our approach to health. In the 21st century, these foods emerge as powerful allies, combating lifestyle diseases and ushering in a new era of well-being.

  2. What is Retrieval Augmented Generation and why is the data you feed it so important?

    Headshot of Joe Mullen, SciBite

    Within the life sciences, evidence-based decision-making is imperative; wrong decisions can have dire consequences. As such, it is vital that systems that support the generation and validation of hypotheses provide direct links, or provenance, to the data that was used to generate them. But how can one implement such a workflow?

  3. Unlocking important RWE from patient data (Part 3) – Can we find all the relevant patients?

    Image and link to LinkedIn profile of blog author Arvind Swaminathan

    In our final installment of this series, we demonstrate how to extract a relevant subset of patients from the simulated data using two approaches – one using SciBite tools and one without.