Elicit
Search, summarize, and extract data from 138 million academic papers with AI
- Freemium
- New
Elicit exists for a problem every researcher knows by heart: reading hundreds of papers just to find the handful of facts you actually need. Ask your question the way you’d ask a colleague — no Boolean gymnastics required — and it returns real papers from a corpus of more than 138 million studies, each with a summary and the option to pull structured data from many papers at once.
Where it differs from a general tool like Perplexity: Elicit is built exclusively around academic papers, and it ships a complete systematic-review workflow rather than a list of search results.
What sets it apart
- Natural-language research questions: describe what you’re looking for conversationally; it translates that into a precise database query
- Structured data extraction: define your columns — methodology, sample size, key finding — and it fills them across dozens of papers automatically
- Systematic review workflow: screen thousands of papers against your inclusion criteria, a step that takes weeks by hand
- A citation behind every answer: every result and summary links to the source paper, one click away
- Chat with your own uploads: drop in a PDF and ask pointed questions about its contents
How to use it
- Create a free account at
elicit.com - Write your research question in plain language instead of keywords
- Review the suggested papers — open any of them to see its summary and how well it matches your question
- To extract data across multiple papers, add extraction columns (extended usage needs Pro)
- For a full systematic review, run the dedicated workflow and let it screen against your criteria
Who it’s for
A good fit if you’re a graduate student or researcher running a literature review, you need comparable data extracted from a large set of papers rather than reading them all yourself, or your field leans on documented evidence — medicine, social sciences, engineering.
Not a good fit if your questions are general rather than academic (use Perplexity), your sources are files you’ve collected yourself rather than published literature (use NotebookLM), or your research is primarily in a language other than English.
Limits and warnings
Built for English-language academia. Its corpus is overwhelmingly English-language papers, and comprehension drops noticeably with other languages.
Automated reports aren’t free-form. Search and summaries are unlimited, but the Research Agent and systematic reviews require a paid plan once usage gets serious.
Verify anything that matters. Extractions are more accurate than a generic chatbot’s answers, but every consequential research decision still deserves a trip back to the original paper.
Alternatives
NotebookLM fits better when your sources are documents you supply yourself — reports, contracts, a specific file set. Perplexity fits better when your question isn’t confined to published academic literature.
✅ Pros
- Ask research questions in plain language and get real papers back, each result linked to its source
- Pulls specific data points from dozens of papers into a structured table instead of one-by-one manual reading
- Built for systematic reviews — a dedicated workflow screens thousands of papers against criteria you define
- The free plan allows unlimited search and summarization; only automated Research Agent reports are capped
❌ Cons
- Automated reports and systematic-review workflows are limited on the free tier — serious use means upgrading
- The corpus is English-language academic publishing, so non-English research is poorly served
- Designed strictly for academic and scientific questions, not general web research like Perplexity
- Pro ($49/month) covers most independent researchers, but the gap between free and Pro is about limits, not features
💰 Pricing
| Plan | Price | Features |
|---|---|---|
| Basic | $0 | Unlimited search, summaries, and chat with papers; limited use of the Research Agent and automated reports |
| Pro | $49 / month (or $588 / year, 35% off) | Standard Research Agent usage and systematic reviews (screening up to 5,000 papers), up to 20 data-extraction columns, custom alerts, API access |
| Scale | $169 / month (or $2,028 / year, 39% off) | 5× base usage, extraction from figures inside papers, direct team collaboration, up to 30 extraction columns |
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❓ Frequently Asked Questions
How is it different from Perplexity or NotebookLM?
Elicit is purpose-built for scientific research. Its corpus is 138 million academic papers, and its signature capability is extracting specific data points — methodology, sample size, outcome — from dozens of papers into a single structured table. Perplexity searches the open web generally, and NotebookLM only works over documents you upload yourself. If your work is a literature review or academic study, Elicit is the right tool.
Is the free plan enough?
For searching, summarizing, and chatting with papers — yes, those are unlimited. What's capped is the Research Agent, automated reports, and systematic-review workflows. Those are exactly the features you need for a full literature review or bulk data extraction, so heavy users hit the ceiling quickly.
Can I extract data from dozens of papers at once?
Yes — that's Elicit's headline feature. You define extraction columns (methodology, sample size, main outcome), and it fills them across your paper set automatically. Extended bulk extraction requires a Pro plan or above.
Can it run a full systematic review?
There's a dedicated workflow for this on Pro and above: it screens thousands of papers against your criteria (date, methodology, outcome) and surfaces the ones that match. It dramatically speeds up the screening phase — but the final quality judgment on each paper remains human work.
Will it invent findings or misread a paper?
Less often than a general chatbot, because every claim links back to its source and you can verify any row with one click. But it can still over-summarize a passage or miss a nuance in a long, complex paper. For any consequential research decision, go back to the original paper before trusting the summary.