Research faster, better. AI charged.
The Iris.ai products are process tools aimed specifically at researchers in the early phase of a new project. They are especially suitable for interdisciplinary projects where the combination of knowledge from across a range of research fields will be vital to the project’s success.
Exploration of interdisciplinary research
Consistently outperforming old school search tools, Iris.ai starts from a paper of your choice, “fingerprints” it based on machine exctracted keywords, contextual synonyms and hypernyms, and matches the fingerprint against >83M Open Access papers.
Traditional keyword searches limit you to what you already know. Great when you know what you’re looking for, but a big problem when you don’t.
The citation system is great for learning about researcher networks. For finding new solutions they can be unhelpful at best, or introduce popularity bias at worst.
Navigate papers visually
Endless result lists are terrible for quickly getting an overview. The Iris.ai maps give you a visual overview of the topics for a much faster distilling of the content.
Find something interesting? Bookmark it into your reading list to dig into it later.
Download full text directly
Found exactly what you’re looking for? Download the full-text Open Access directly.
Access full history
Connecting the dots with something you saw ten papers ago? Your full history is saved!
Full research landscape mapping
Use the Exploration tool starting from your free text description of the problem, and a result editor, to build a large corpus of documents related to your problem statement. Use the iterative Mapping tool to narrow down to a precise reading list.
Incorporate additional databases
The Iris.ai tools can be connected to most scientific text-based content, including from third-party providers as well as internal company documents.
Exploration starting from full-text
Using a self-written problem statement as a starting point allows far more flexibility in the Exploration phase.
Edit exploration results
Premium users are given the option to iteratively edit the results in the Exploration phase, offering far more flexibility and customized results.
Iteratively narrow down results list
The Focus feature allows the user to iteratively narrow down a corpus of up to 20,000 documents, to a short comprehensive reading list.
Corpus based criteria suggestions
In the Focus process, the tool suggests topics for inclusion and exclusion based on the initial corpus, facilitating a rapid and precise iterative filtering.
Manual precision/recall validation
With optional manual verification, the user can measure the precision and recall of the filtered corpus – building confidence with the results.
The road towards an accessible science world
The Iris.ai products semi-automate the existing research process – currently for the first two steps of the process: Broadly for an overview, and then to a precise reading list.
Write out the problem you are trying to solve in your own words.
Use 300-500 words, and make sure to cover the problem from multiple angles.
Iris.ai uses this text to build a fingerprint of the problem, matches it against >120M research papers, and distributes them into topics.
Use the visual maps to gain an overview of your problem.
Build new maps from other articles or new text where more content or deeper exploration is needed.
“Jump into the rabbit hole of science” as a user once called it.
Use the hierarchy editor to fine-tune your map results.
Allowing you a bit more control over the exploration, the hierarchy editor lets you explore the fingerprint; merging words, selecting synonyms and deleting concepts that are currently irrelevant to you.
Bookmark papers and maps for later visits.
Build a reading list of specific papers you wish to read later.
Bookmark full maps for use in the next phase with the Focus tool.
Currently in the Iris.ai R&D pipeline
We’re currently working on the next steps of the technology, which is what we call hypothesis extraction: giving the Iris.ai machine the ability to extract the core information of a paper in the form of a hypothesis structure (problem – solution – evaluation – result). This will create a number of interesting features such as:
- Highlighting which section of the paper is most relevant to your problem statement, and why.
- Allowing you to build your process only focused on e.g. method sections, or only problem areas.
- Show you a structural summary of the hypothesis of a paper.
- … and stay tuned for more!