Oslo, Norway March 16, 2022 Iris.ai, developer of AI tools for processing scientific research, launches a new platform the Researcher Workspace. The purpose of this comprehensive suite of tools is to help researchers in industry and academia, librarians and students follow their own research process. Modules include a visual content based search, analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents and very powerful filters based on context descriptions, the machine's analysis, or specific data points or entities.
Anita Schjψll Brede, the CEO of Iris.ai, shares her excitement about the launch: "We're very excited to launch our new platform! It's the culmination of several years of both working closely with clients and serious Research and Development efforts. With this new platform, we have focused on flexibility and adaptability so that each researcher is able to streamline their exact literature review process, and unlock the knowledge they need as swiftly and painlessly as possible! We're also exceptionally proud of the tools we now offer as part of the Workspace: the Extraction tool and the Abstractive summarization are very unique not to mention the fact that our research team has found a way to train the entire system on each client's research field, with no humans involved. I do believe we're about to see the next chapter of what's possible in using AI/ML for scientific research and I am so proud of what our team has accomplished."
The Researcher Workspace is mainly directed towards R&D heavy industries like chemistry, pharmaceuticals, MedTech, material science, biotech, food safety or engineering. The tool can be reinforced on the customer's field. The machine can be trained on industry specific terminology to provide more precise and accurate results.
Iris.ai is one of the world's leading start-ups in the research and development of artificial intelligence (AI) technologies. Founded in 2015, the start-up offers an award-winning AI engine for scientific text understanding. The company uses Natural Language Processing/Machine Learning to review massive collections of research papers or patents: find the right documents, extract all their key data or identify the most precise pieces of knowledge. Applied to literature reviews, data extraction, document summarization, competitive intelligence or any other task involving thousands of documents like papers or patents, R&D professionals and students no longer waste time on tasks the Iris.ai tools can do for them. Iris.ai collaborates both with innovation-oriented universities and corporate customers, and contributes to many joint research projects fostering Open Science (CORE) and innovation.