Research discovery with artificial intelligence
Use Natural Language Processing 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.
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