Iris.ai

Searches academic literature using semantic understanding of research concepts rather than keyword matching. Understands relationships between ideas and returns contextually relevant papers even when exact keywords don't match.

Creates visual maps showing relationships and connections between research papers, concepts, and authors. Helps researchers identify clusters of related work and discover gaps in the literature landscape.

Provides a structured workspace for organizing, annotating, and managing research papers and findings. Allows researchers to create custom collections, add notes, and systematically organize literature review materials.

Analyzes the landscape of published research to identify underexplored areas and potential research gaps. Uses visual mapping and semantic understanding to highlight where research is sparse or missing.

Identifies non-obvious connections and relationships between papers, concepts, and research areas across different domains. Helps researchers find interdisciplinary insights and unexpected links.

Reduces time spent on literature discovery and synthesis by automating search, organization, and relationship mapping. Helps researchers quickly build comprehensive understanding of a research area.

Identifies and tracks key authors, research groups, and institutions working in specific research areas. Helps researchers understand who the leading researchers are and how they collaborate.

Provides meaningful research capabilities without payment, making advanced literature discovery and organization tools accessible to students and independent researchers with limited budgets.