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Causaly Ltd (London, United Kingdom)

Causaly

Agentic AI platform for life sciences that brings together biomedical literature, databases and proprietary company data in a knowledge graph and answers research questions with cited evidence. Aimed at pharma and biotech R&D.

Go to website Causaly alternatives
Shared spot8of 18 professional tools with a GDPR profileOur summary of what the provider itself publishes about your data: whether the data is stored in the EU (2 points), whether there is a certification or GDPR guarantee (1), and whether the provider is established in the Netherlands or the EU (1). 3 or 4 points is Strong, 2 Good, 1 Limited, 0 Unknown. How we measure in research & science
GDPR: Limited
  • Provider based outside the EU
  • Publishes certification/GDPR safeguards

Each block is a professional tool in research & science, with the strongest GDPR profile on the left. 8 professional tools, like Causaly, have a limited profile: they share this spot. Click for that tool.

Priceon request (enterprise, via demo)
Free trialYes
BasedLondon, United KingdomOutside the EU
ModelNot publicthe language model used is not public; proprietary retrieval system (SIRS) on top of a biomedical knowledge graph. Customer data is not used to train AI models (according to the provider); ISO 27001-certified (2022) (according to the provider); data may be stored in the UK, Greece or the US, transfers under standard contractual clauses (according to the provider)

Last checked: 9 November 2026 · How we check

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What it does

AI copilot for researchers
Biomedical knowledge graph (500M facts)
Answers with traceable sources
Index your own documents and data
AI agents: planning, searching, concluding
Bio Graph: visualise pathways
Pipeline Graph: competitive analysis
Quality control and governance

Comparable in research & science

Cradleon request (licence, no royal…ASReview LABfree (open source)Scholarcyfree Article Summarizer (1 same…SciSpacefree planKeeniousfree plan (3 conversations per day)Castoron request
All 18 professional tools for research & science

Causaly compared

Causaly vs CastorCausaly vs BiolevateCausaly vs Aily Labs
Underlying model

the language model used is not public; proprietary retrieval system (SIRS) on top of a biomedical knowledge graph. Customer data is not used to train AI models (according to the provider); ISO 27001-certified (2022) (according to the provider); data may be stored in the UK, Greece or the US, transfers under standard contractual clauses (according to the provider). Many tools build 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.

GDPR check for research & science

Questions about Causaly

AI for research & scienceAI for pharmaceutical industry
Which AI model does Causaly use?
The maker of Causaly does not make public which AI model sits underneath. Proprietary retrieval system (SIRS) on top of a biomedical knowledge graph. Customer data is not used to train AI models (according to the provider); ISO 27001-certified (2022) (according to the provider); data may be stored in the UK, Greece or the US, transfers under standard contractual clauses (according to the provider).
How much does Causaly cost?
On request (enterprise, via demo). Check the current price on the provider's website; prices change often.
Who is Causaly for?
Causaly is aimed at research & science and pharmaceutical industry. It fits work such as searching and exploring literature 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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