Elicit

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AI research assistant that searches 200M+ academic papers, extracts key findings, and synthesizes literature reviews automatically.

Elicit is an AI research tool built by Ought and launched publicly in 2022 that helps researchers discover and synthesize academic literature at scale. Unlike general AI search tools, Elicit indexes over 200 million academic papers from Semantic Scholar and uses language models to extract structured data - study populations, interventions, outcomes, and effect sizes - directly from paper texts. This enables automated literature review synthesis, systematic review support, and structured comparison of research findings across dozens of studies in a single table view. Elicit is widely used by PhD students, scientists, policy researchers, and medical professionals for rapid evidence gathering without manually reading every paper. The free plan provides 12 credits per month; Plus at $10/month provides 50 credits, with additional credits purchasable on demand.

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Freemium

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elicit.com

Freemium

Pricing Model

Research & Analysis

Key Features

Searches 200M+ academic papers from Semantic Scholar with AI-ranked relevance
Structured data extraction - automatically pulls populations, interventions, outcomes from each paper
Literature review table builder for side-by-side comparison of findings across multiple studies
Full-text paper search for questions that require reading beyond the abstract
Systematic review support with inclusion/exclusion criteria filtering
AI summary of each paper's methodology, results, and limitations
Export to CSV or Google Sheets for further analysis in external tools

Use Cases

PhD students synthesizing related work for their dissertation literature review in hours instead of weeks
Scientists conducting systematic reviews to assess evidence quality across clinical trials
Policy analysts gathering evidence on specific interventions from peer-reviewed research
Medical professionals quickly assessing current evidence on treatment options or drug interactions

Pros

Structured data extraction from papers is unmatched - no other tool automatically pulls outcome data into tables
Grounded in real academic literature, not web content - dramatically lower hallucination risk
Free tier with 12 credits/month lets students use it regularly at no cost

Cons

Coverage limited to Semantic Scholar - misses papers not indexed there, particularly recent preprints
Credit system makes heavy systematic review usage expensive - large reviews can exhaust monthly credits quickly
Search quality drops for niche or interdisciplinary topics with limited indexed literature