The AI research assistant for academic literature — 125M+ papers, structured extraction.
Elicit is purpose-built for academic literature research. It searches over 125 million papers using semantic AI understanding (not just keyword matching), automatically extracts research findings, methods, sample sizes, and outcomes into structured comparison tables, and synthesizes across papers for literature reviews.
Elicit is the go-to AI tool for serious academic literature research. Its search covers 125M+ papers using semantic embeddings — meaning it understands the meaning of your question and finds papers that address it conceptually, even without exact keyword matches. The killer feature is structured data extraction: for any set of papers, Elicit automatically extracts pre-defined fields — intervention, outcomes, study design, sample size, findings — into a comparison table, which previously required hours of manual data extraction. AI summaries synthesize across studies. Researchers use Elicit to map the literature on a new topic, conduct systematic reviews, and identify methodological gaps. Highly valued by evidence-based practitioners in medicine, policy, and social science.
Search a research question across 125M papers, screen relevant results, and automatically extract key data points (design, sample, outcomes) into a structured table that would take weeks to build manually. The foundation of a systematic review done in hours.
When entering a new research domain, use Elicit to quickly map the key papers, major findings, methodological approaches, and leading researchers. Get a structured overview of what the field knows and doesn't know in a fraction of the time of manual literature searching.
Synthesize the evidence base for a specific intervention, policy, or clinical question. Elicit extracts effect sizes, study designs, and conclusions across studies, enabling rapid evidence-based assessment for healthcare providers, policy analysts, and practitioners.
Elicit uses AI embeddings (semantic vector search) rather than keyword matching. It understands the meaning of your research question and finds papers that conceptually address it — even without using your exact search terms. This is significantly more effective for complex research questions than keyword-based databases like PubMed or Google Scholar.
For any set of papers, Elicit automatically extracts specific data points into a structured table — research design, sample size, intervention, outcomes, key findings, and more. You can also add custom columns to extract any specific piece of information from each paper. This replaces the manual data extraction step that typically takes days in systematic reviews.
Elicit is better for comprehensive systematic literature reviews requiring structured data extraction — it has more papers (125M vs Consensus's 200M, though Consensus focuses on scientific consensus), better extraction tables, and more researcher-focused workflow features. Consensus is better for quick scientific Q&A — it's faster for single-question answers with confidence ratings. For a full literature review process, Elicit is the right tool.
The gold standard for AI research — 50-100 sources, one cited report.
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