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Semantic Scholar
✓ verifiedFree
Free AI-powered academic search engine indexing 236M+ papers, for researchers wanting semantic literature discovery and an augmented reader.
7.9M visits/mo13K saves
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SciSummary
✓ verifiedFreemium
AI tool that summarizes scientific papers into abstract, methods, results, and conclusion sections, with figure and citation support.
74K visits/mo162K saves
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Iris AI
✓ verifiedPaid
Enterprise AI knowledge platform that unifies complex regulated-industry data into trusted, AI-ready intelligence for R&D and agents.
56K visits/mo
Pricing
No public pricing
Free Trial: $0 for first 7 days (30,000 words, 5 figures, 100 chat messages)
Student: $0 for first month (promo code, unlimited use)
Pro: $4/month billed as $48/year (unlimited summaries, figures, chat, and searches)
Free trial available
No public pricing
No public pricing
Core features
- ✦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
- ✦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
- ✦Scholar Search
- ✦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
Use cases
- →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
- →Students digesting dense research papers quickly
- →Researchers doing literature reviews across many papers
- →Academics organizing a personal paper library
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- →Grounding enterprise AI agents in trusted internal knowledge
- →Accelerating R&D literature and data analysis
- →Unifying siloed regulated data for AI applications
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