Iris.ai

Iris.ai is an artificial intelligence–powered research discovery and literature analysis platform designed to help researchers, students, and R&D teams navigate vast amounts of scientific literature. It uses natural language processing and machine learning to understand research questions, map relevant papers, and extract insights automatically. Founded in 2015, it was developed to accelerate scientific discovery by making sense of the overwhelming scale of global research data.

Why it matters

• Scientific literature is growing exponentially, Iris.ai addresses the challenge of finding and connecting relevant research across disciplines.
• Instead of simple keyword searches, Iris.ai understands concepts in text, enabling more meaningful discovery — finding related work even when terminology differs.
• It saves researchers time in the early stages of projects by summarizing, clustering, and filtering thousands of papers into thematic maps or concise reviews.
• For organizations, it helps in competitive intelligence, technology scouting, and building structured knowledge bases from unstructured publications.

Key features & advantages

• Semantic search and mapping: Instead of keyword matching, Iris.ai creates a “concept map” of research related to a given problem or text input (such as an abstract or research question).
• Automated literature review: The platform identifies, categorizes, and summarizes relevant papers to support systematic reviews and scoping studies.
• Knowledge extraction: Extracts key data (methods, materials, parameters, results) from documents to build structured datasets.
• Filtering and clustering: Groups papers into conceptual clusters, showing how research areas interconnect.
• Integration with research databases: Works across multiple scientific repositories and can process PDFs directly from users.
• AI model customization: Enterprise clients can train private models for domain-specific literature (e.g., pharma, materials science).

Limitations & things to watch

• Access to full functionality may require institutional or enterprise licensing, free tiers have limited capabilities.
• While its AI captures conceptual similarity, it may not always perfectly distinguish methodological nuances — human review is still important.
• For specialized or very narrow domains, performance depends on the volume and quality of available literature in the indexed sources.
• Extracted summaries and maps are AI-generated, interpretation and validation by subject experts are necessary before decision-making.