Iris.ai
AI research platform for scientific literature - semantically searches 13M+ papers, extracts concepts, and organizes findings in a structured workspace.
Iris.ai is an AI-powered research intelligence platform that helps scientists, engineers, and corporate R&D teams discover relevant academic literature and patents through semantic search rather than keyword matching. Founded in 2015 in Oslo and backed by the European Innovation Council, Iris.ai indexes over 13 million scientific papers and patents, allowing researchers to describe a topic in natural language and receive ranked results including papers they would never have found through traditional database searches. The platform offers a structured Research Workspace where users can cluster, annotate, and build upon saved literature collections, plus a Technology Scouting API used by enterprise R&D and IP teams to monitor scientific developments in a domain continuously. Iris.ai has been used by research teams at automotive manufacturers, pharmaceutical companies, and defense contractors for competitive intelligence and patent landscape analysis. Academics and individual researchers can access a free workspace, while enterprise pricing covers team deployments and API access.
Key Features
- Semantic paper discovery - describe a research topic in natural language and find relevant papers beyond exact keyword matches
- Research Workspace - save, cluster, and annotate papers in an organized workspace for literature review projects
- Concept extraction - AI automatically identifies key concepts, entities, and relationships within saved paper collections
- Patent search - searches both academic publications and patent databases in a single query for comprehensive IP research
- Technology Scouting API - enterprise endpoint for continuously monitoring scientific publications in a defined technology domain
- Cross-disciplinary discovery - surfaces papers from adjacent fields that solve similar problems using different terminology
Use Cases
- Graduate students and postdocs building comprehensive literature reviews that must cover cross-disciplinary work beyond their home field
- Corporate R&D teams scanning scientific literature and patents to identify technologies relevant to product roadmap decisions
- IP lawyers and technology scouts monitoring academic publication activity in a domain for competitive intelligence
- Research librarians building curated reading lists and topic guides for faculty and graduate students
Pros
- Semantic search surfaces relevant papers that keyword-based searches miss - essential for cross-disciplinary and emerging research areas
- Free Research Workspace makes it accessible to individual academics and students without enterprise budget requirements
- Combined academic paper and patent search in one tool eliminates the need to query separate specialized databases
Cons
- Enterprise API and technology scouting pricing requires sales contact with no self-serve tier for teams needing programmatic access
- Coverage of conference proceedings and preprint servers is less comprehensive than Semantic Scholar or PubMed for some disciplines
- The research workspace UI is less refined than consumer-facing tools like Notion or Readwise for non-technical users
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