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Everway Opco Limited / Scholarcy (Antrim, United Kingdom)Scholarcy
Turns articles, book chapters and PDFs into interactive summary cards with key findings, figures, tables and references. Useful for quickly screening a large stack of literature and exporting to your reference manager.

- Provider based outside the EU
- Data in the EU according to the provider
Last checked: 27 October 2026 · How we check
Is this your tool? Fill in missing detailsWhat it does
Spotlight on key findings and concepts
Library with references, figures and tablesComparable in research & science
Cradleon request (licence, no royal…
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Keeniousfree plan (3 conversations per day)
Castoron request
WISKIon requestWhich models are used is not public; storage on servers within the EEA, encrypted at rest and in transit. The provider does not explicitly state whether input is used for training. Many tools are built on a large model from, for example, OpenAI or Anthropic. If in doubt, ask for the data processing agreement: it states where your data goes.
Questions about Scholarcy
- Which AI model does Scholarcy use?
- The maker of Scholarcy does not disclose which AI model sits underneath. Which models are used is not public; storage is via servers within the EEA, encrypted at rest and in transit. The provider does not explicitly state whether inputs are used for training.
- How much does Scholarcy cost?
- free Article Summarizer (1 summary per day); Plus €8.99 per month or €81 per year; 7 days free trial. Check the current price on the provider's website; rates often change.
- Who is Scholarcy for?
- Scholarcy is aimed at research & science. It fits work such as literature searching and exploration with semantic search and citation networks, screening titles, abstracts and full texts for systematic reviews, and extracting and summarising data and study characteristics from papers.
- What should I look out for with AI in research & science?
- Never use AI as a source: every reference must be traceable to the original article, as fabricated or wrongly attributed citations are the most common error. Unpublished data, research proposals and peer-review manuscripts are confidential or commercially sensitive, so do not enter them into a consumer account without a data processing agreement, and check whether the provider stores your input or uses it for training (publishers usually prohibit this explicitly for peer review). If you work with personal data from research participants, the GDPR and your own ethics review apply: running models locally or using an EU-hosted tool with data minimisation is then the safe route. Finally, document your search strategy and your AI use: journals and funders increasingly ask for this, and a review must remain reproducible.
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