Vortext Systems applies cutting-edge Machine Learning to make sense of your documents.


Whether it's dealing with scientific publications, archives, or web pages; together we'll figure out how to get more value out of your documents.


Years of experience building custom solutions. Our engineers have a solid track record for keeping up-to-date with the latest trends, and employing industry best practices.


With strong ties to the academic world we are always happy to provide ideas when dealing with large amounts of literature. PDF's are our specialty. 


Robot Reviewer

Robot Reviewer is our state of the art tool for detecting Risk of Bias in Randomised Controlled Trial publications. 

A lot of biomedical science is about drawing conclusions from large amounts of published literature. Unfortunately, this literature is often unstructured, so a lot of effort goes into extracting information by reading documents. We made Robot Reviewer to help extract information from these documents; so researchers and clinicians can make better, quicker, and more informed decisions about treatments and diagnoses.


Vortext Annotate allows you to directly highlight and annotate PDF documents from within your browser.
It is currently very experimental, but drop us a line if you're interested in seeing this being developed further!

Selected Publications

  • Kuiper, J., Marshall, I. J., Wallace, B. C., & Swertz, M. A. (2014). Spá: A Web-Based Viewer for Text Mining in Evidence Based Medicine. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2014) (Vol. 8726, pp. 452–455). Springer Berlin Heidelberg. [preprint] [doi]
  • Marshall, I. J., Kuiper, J., & Wallace, B. C. (2014). Automating Risk of Bias Assessment for Clinical Trials. In Proceedings of the ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) (pp. 88–95). ACM. [preprint] [doi]
  • Kuiper, J., & van Valkenhoef, G. (2013). Top-Level MeSH Disease Terms Are Not Linearly Separable in Clinical Trial Abstracts. In Proceedings of the conference on Artificial Intelligence in Medicine (AIME 2013) (Vol. 7885, pp. 130–134). Springer Berlin Heidelberg. [preprint] [doi]