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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.
Free-first literature discovery tool that visually maps citation networks and related papers to speed up systematic reviews.
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.
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
- ✦Citation and author network exploration from a seed paper
- ✦Interactive visualizations of how topics relate over time
- ✦Unlimited library organization and collection sharing
- ✦Zotero integration for syncing saved papers
- ✦Up to 300 seed articles supported on the paid tier
- ✦Alerts for new related research
- ✦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
- →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
- →Building a systematic literature review efficiently
- →Discovering research gaps in an unfamiliar field
- →Tracking new publications related to saved papers
- →Visualizing how a body of research has evolved
- →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