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Well-adopted AI research assistant for finding papers, summarizing, and extracting data at scale.
AI tool that summarizes scientific papers into abstract, methods, results, and conclusion sections, with figure and citation support.
Widely used visual tool for exploring related academic papers.
Enterprise AI knowledge platform that unifies complex regulated-industry data into trusted, AI-ready intelligence for R&D and agents.
AI-assisted systematic literature review platform for research teams handling screening, deduplication, extraction, and PRISMA reporting.
Free trial available
No public pricing
- ✦AI-enabled systematic reviews
- ✦Research reports
- ✦Upload your own PDFs
- ✦Quick summaries
- ✦Source quotes
- ✦Question answering
- ✦Structured section-by-section paper summarization
- ✦Bulk and multi-paper summarization and comparison
- ✦Chat with figures for data interpretation
- ✦Folder and tag-based library organization
- ✦Semantic search across indexed documents on Pro
- ✦ChatGPT connector integration
- ✦Visual graph generation of related academic papers
- ✦Discovery of prior and derivative works
- ✦Identification of important and relevant papers
- ✦Exploration of research field dynamics
- ✦Axion for turning data chaos into AI-ready intelligence
- ✦Neuralith for converting enterprise knowledge into an AI engine
- ✦RSpace for precision intelligence in complex R&D
- ✦Data unification across fragmented enterprise sources
- ✦Built for regulated, compliance-driven industries
- ✦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
- →Speeding up literature reviews
- →Automating systematic reviews and meta-analyses
- →Learning about new domains
- →Data extraction in rigorous systematic literature reviews
- →Students digesting dense research papers quickly
- →Researchers doing literature reviews across many papers
- →Academics organizing a personal paper library
- →Getting a visual overview of a new academic field
- →Ensuring no key papers are missed in a field with a large volume of new publications
- →Creating a bibliography for a thesis
- →Discovering relevant prior and derivative works
- →Grounding enterprise AI agents in trusted internal knowledge
- →Accelerating R&D literature and data analysis
- →Unifying siloed regulated data for AI applications
- →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