There are more than 80 million research papers available online and more being published every day. But sadly, about 50% of these research papers are read by less than five people. This isn't because people aren't interested in the knowledge but due to a lack of effective methods to access, process and analyse all of this information.

Founded at the NASA Ames Research Park in Silicon Valley, Iris.ai aims to make it easier for everyone to understand and make use of the unprecedented amount of knowledge out there. The tool is a machine-learning powered science assistant with the aim to accelerate the discovery of contextually relevant scientific papers - mapping and reviewing thereby doubling productivity. Iris.ai starts from a paper of your choice, "fingerprints" is based on machine extracted keywords and contextual synonyms and hypernyms, and matches the fingerprint against more than 83M Open Access papers. By giving Iris.ai a research paper to “read”, it reads the abstract, maps out the key concepts and presents the user with the most relevant article. The results are visualised, explorable and can help to quickly create an overview of the field of study.

Iris.ai consistently outperforms old school search tools and will revolutionise the work of R&D departments in big corporations, academics and researchers. They aim to democratise science, disrupt research system incentives and improve scientific content, making it more transparent, accountable and widespread in society.