A user enters a research question. Elicit searches its database of papers (via Semantic Scholar) and generates a structured table. Each row is a paper, and columns display data extracted by the AI. Users can add custom columns by typing questions (e.g., "What was the demographic of the control group?"). The AI reads the full text of the papers (when available) to populate that column. Users can also upload a batch of proprietary PDFs for the AI to analyze in the same tabular format.
Elicit
Elicit is an AI research assistant designed specifically for deep academic literature review and data extraction. Built originally by the non-profit research lab Ought, Elicit automates the most labor-intensive parts of the academic research process: finding relevant papers, extracting specific data points across methodologies, and organizing findings into structured tables.
While standard AI search engines might summarize an abstract, Elicit is built to dig deeper. It allows researchers to upload their own PDFs or search a large-scale corpus of open-access papers, and then command the AI to extract highly specific data—such as "population size," "dosage," or "p-values"—from dozens of papers simultaneously.
It is the tool of choice for systematic reviewers, medical researchers, and PhD candidates who need to strictly evaluate the methodologies and outcomes of large sets of scientific literature.
Best For
- Researchers conducting systematic reviews
- Medical and hard science doctoral candidates
- Policy analysts synthesizing large reports
- Scientists needing to extract methodologies
How It Works
Key Features
Search & Extraction
- Tabular data extraction
- Custom AI column generation
- Full-text paper analysis
- Semantic literature search
Document Management
- Upload and analyze personal PDFs
- Chat with specific papers
- Export tables to CSV/RIS
- Highlight tracking back to source text
Pros & Cons
Pros
- Unrivaled at extracting specific data points into structured, comparative tables
- Allows analysis of private, uploaded PDFs not just the public database
- Highly transparent, showing exactly where it found the data in the original text
- large-scale reduces the manual labor of building literature matrices
Cons
- Steeper learning curve than simple Q&A search engines
- AI extraction accuracy varies depending on how obscure the requested data point is
- Full-text extraction is limited by open-access availability unless PDFs are uploaded
- Pricing is based on a credit system, which can be difficult to predict for large reviews
Pricing
Elicit operates via a credit system. The Basic free plan provides a small monthly allowance of credits for light searching. The Plus and Pro subscriptions provide significantly more credits, allow for the export of results, and enable high-volume PDF uploads and extraction tasks.
How It Compares
Elicit's primary competitors are Consensus, Perplexity, and traditional reference managers like Mendeley. Consensus is better for asking a simple question and getting a broad scientific "yes/no/maybe" summary. Perplexity is better for general web research. Elicit differentiates itself powerfully by focusing on tabular data extraction—it is essentially an AI that builds systematic review spreadsheets for researchers based on deep reading of methodologies.