A knowledge validation engine combining blockchain and AI technology
COMPANY NAME: Iris.ai BG EOOD
APPLICANT NAME: Jacobo Elosua
TEAM COMPOSITION: People from one or two legal entities
PROJECT SECTOR: [“Open Innovation”]
[“Blockchain technologies”,“Distributed ledger technologies”,“Peer to peer technologies”,“Artificial intelligence”]
Chemistry is one of the top-five most published fields in research, but breakthroughs are becoming fewer and only 25% of chemical companies think that they are good innovators. Too much information is lost or missed. Distilling useful information, assessing the quality of research results and reproducing them have become tedious challenges that require time and expertise.
To unlock new innovation opportunities R&D teams need to find ways to: (1) utilise core competencies and existing knowledge to generate new revenue; (2) reduce the risk of lab experiments failing by having as much upfront information as possible; and, (3) make the R&D process more cost efficient without compromising quality. Project Aiur enables these goals. More specifically, our project starts by addressing chemistry organizations’ painful early stage research process inefficiencies.
R&D relies on external sources of knowledge, but not every piece of information produced by the scientific community is of high quality, leading to waste of time and resources in trying to reproduce results as well as missed opportunities. Therefore, as R&D intensive organisations launch new research projects, they are faced with highly problematic knowledge validation issues.
Our research assistance premium software products currently cater for university library and research institute use cases. From that toe in the water, our commercial efforts are expanding into corporate R&D, with chemistry as the initial (highly attractive but currently underserved) beachhead market. We believe the proposed Knowledge Validation engine presents an opportunity around R&D automation, a $14bn yearly revenue opportunity (11m R&D researchers and $138bn spent yearly on digital enablers).
Post successful pilots with existing chemistry R&D players, we will deepen the commercial collaboration to evolve our solution into an enterprise ready system, sold via price-per-seat or price-per-project SaaS business models. We will also analyse and evaluate strategic collaboration opportunities with other players with already established sales channels into our target customer base, including digital notebook providers currently selling into corporate research labs.
Iris.ai is a seed funded, Singularity University, 500 Startups, Founders Factory, German Technology Entrepreneurship Centre and Creative Destruction Lab backed, three year old international startup developing an AI to democratize access to scientific knowledge. Since founding Iris.ai in 2015 we have focused our efforts on developing a machine that can read and understand scientific text. Our initial trajectory has been covered by Fast Company, TechCrunch, Wired, Science Magazine or the World Economic Forum, among others.
The project uses LEDGER as a lever, pooling it with other Iris.ai investor and client money to make Project Aiur a gradual reality over time.
We connect the ambitious long-term roadmap Project Aiur roadmap with the timeline restrictions presented by initially focusing on an MVP that generates validated knowledge dependency trees in the chemistry domain, based on US Patents and Open Access (OA) research articles.