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Free AI-powered academic search engine indexing 236M+ papers, for researchers wanting semantic literature discovery and an augmented reader.
Free nonprofit tool that visualizes scientific literature as topic maps, clustering papers to speed research discovery.
AI-assisted systematic literature review platform for research teams handling screening, deduplication, extraction, and PRISMA reporting.
Interactive learning app with an AI tutor for math, coding, science and data-visual, adaptive lessons for all ages.
No public pricing
No public pricing
No public pricing
- ✦AI-powered semantic search across 236M+ papers
- ✦Semantic Reader for augmented, contextual paper reading
- ✦Free public API for paper search and metadata
- ✦Author and topic discovery
- ✦Citation graph exploration
- ✦Beta program for new features
- ✦AI-generated visual maps of research topics
- ✦Clusters papers into themes
- ✦Open-access resource discovery
- ✦Open-source and nonprofit
- ✦Custom integrations/embedding for organizations
- ✦Reference import, organization, and deduplication across large libraries
- ✦AI-powered relevance predictions to prioritize screening
- ✦Collaborative title/abstract and full-text screening workflows
- ✦Structured data extraction and PICO framework support
- ✦Risk of bias assessment tools
- ✦Auto-generated PRISMA flow diagrams and audit trails
- ✦Mobile app for screening on the go
- ✦Personal AI tutor (Koji)
- ✦Visual, interactive lessons
- ✦Adaptive practice based on mastery
- ✦Courses in math, CS, science and data
- ✦Gamified streaks, levels and daily goals
- ✦iOS and Android apps
- →Researchers searching scientific literature across disciplines
- →Developers building applications on top of scholarly data via the API
- →Students exploring citation networks for a topic
- →Librarians and academics needing free literature discovery tools
- →Get an overview of a research field
- →Find relevant papers faster
- →Identify key concepts in the literature
- →Embed discovery tools in library systems
- →Running systematic literature reviews as a research team
- →Speeding up deduplication and abstract screening for large reference sets
- →Producing PRISMA-compliant documentation for publication
- →Coordinating divided screening workloads across reviewers
- →Learning math and coding by doing
- →Getting adaptive tutoring outside class
- →Building STEM intuition
- →Daily skill practice