Research discovery with artificial intelligence
Go from millions of documents to a precise reading list in just two days.
Exploration of interdisciplinary researchConsistently outperforming old school search tools, Iris.ai builds an interdisciplinary research map based on a problem statement or research paper of your choice. Iris.ai does this by building a "fingerprint" using machine extracted keywords, contextual synonyms and hypernyms, then matches the fingerprint against more than 70M Open Access papers
- Bypass keyword search
- Bypass citations
- Navigate papers visually
- Bookmark papers
- Download full text directly
- Access full history
Natural Language Understanding
Iris.ai uses a combination of keyword extraction, word embeddings, neural topic modeling, word importance based similarity of document metrics and hierarchical topic modeling. The approach is mainly unsupervised but we utilize an evaluated annotation set from our community of AI Trainers for benchmarking and improving our tools.
Key information extraction
Key terms identification. Marking of possible contextually disambiguating information. Forming basis for a document fingerprint.
Neural Topic Modelling
Clustering of semantically similar documents. Cluster labeling. Document fingerprint update.
Saved time and increased radical discovery
Industrial researchers use on average 3 weeks to build a reading list, with a self-reported 70% confidence. Using the systematic academic approach, we save you time and resources – while increasing the chances of groundbreaking interdisciplinary discovery.
- Up to 90% time reduction
- 85% precision
- Increase interdisciplinary inspiration
- Organize your internal R&D documents