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Elicit

by Elicit

AI Application
Low Confidence

Elicit is a web-based research assistant for searching academic literature and running literature and systematic reviews, with an API and MCP server for automated use. Researchers in universities, pharma, medtech and policy use it to find papers, screen them against inclusion criteria, extract data into tables and write evidence reports. The vendor says it searches over 138 million papers and clinical trials drawn from Semantic Scholar, PubMed and OpenAlex, and that language models suggest screening criteria and decisions, extract quantitative and qualitative data with supporting quotes, and generate reports with sentence-level citations. The vendor says users can agree with, edit or add to the AI's screening suggestions, and that dual review can pair two human reviewers with AI assistance or use the AI as the second reviewer.

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Transparency Score

Weighted across four pillars · updated October 19, 2026

Overall grade
60% weighted
ABCDF
Model
—
Not yet assessed

Capabilities

Literature Search
Paper Screening
Data Extraction
Summarization
Report Generation
Answer Generation

Vendor Information

Complete information about the vendor/provider of this AI application

ElicitView all products →
Elicit Research, PBC(Trading as: Elicit)
Contact Information
elicit.com
Registered Address
Oakland, CA, US

Infrastructure

Platforms hosting this application — each inherits its own transparency grade.

Amazon Web Services (AWS)
Amazon
F
OpenAI API
OpenAI
F
Anthropic API
Anthropic
F
View full infrastructure →

EU AI Act Provider Information

Verification Status:
Unverified
Elicit Research, PBC(Trading as: Elicit)
Oakland, CA, US
Publishes a channel for reporting security incidents or vulnerabilities: https://elicit.com/operations/api-terms (read 19 October 2026).
Elicit says its language models can miss the nuance of a paper or misunderstand what a number refers to, that it summarises the findings of a weak study just as it summarises a good one because it cannot yet judge whether one paper is more trustworthy than another, and that it works best for empirical research such as randomised controlled trials (https://support.elicit.com/en/articles/14757928-elicit-s-limitations, read 19 October 2026).

Potential Risks

1 considerations identified

1

Review recommended before use

These considerations are automatically identified based on publicly available information about the vendor and AI catalog data. Actual risks may vary based on your specific use case and implementation.

Supply Chain Network

Visual representation of the vendor's digital supply chain relationships

Compliance & Risk

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Added: February 18, 2026
Updated: October 19, 2026

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