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PubGenius Inc. (Milpitas, United States)SciSpace
Broad platform for literature research: searching hundreds of millions of papers, chatting with PDFs with references to the exact passage, extracting data from articles and writing with citation help.

- 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
Chat with PDF, with answers linked to passages
Literature Review with tables in custom columns
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WISKIon requestwhich LLMs are used is not public; own claims: AES-256 encryption, uploads are not used for model training and deleted after 30 days of inactivity, processing in EU data centres. There is a SOC 2 badge on the site, but the trust centre is not publicly accessible. Many tools build on a large model from, for example, OpenAI or Anthropic. When in doubt, ask for the data processing agreement: it states where your data goes.
Questions about SciSpace
- Which AI model does SciSpace use?
- The maker of SciSpace does not disclose which AI model sits underneath. Which LLMs are used is not public; the provider's own statement: AES-256 encryption, uploads are not used for model training and are deleted after 30 days of inactivity, processing in EU data centres. An SOC 2 badge appears on the site, but the trust centre is not publicly accessible.
- How much does SciSpace cost?
- free plan; Premium $20 per month or $12 per month on annual billing ($144 per year); Advanced $90 per month or $70 per month annually; Teams from $15 per user per month, billed annually. Check the current price on the provider's website; prices change often.
- Who is SciSpace for?
- SciSpace 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 Unpublished data, research proposals and peer-review manuscripts are confidential or commercially sensitive, so do not enter them into a consumer account without a data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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