Epsilon - AI Search Engine for Scientific Research

What

Epsilon is an AI search engine for scientific research that helps researchers find evidence, publications, patents, and summaries across a large academic corpus. It is designed for people doing literature reviews, citation gathering, proposal development, meta-analysis support, patentability-related research, and topic exploration.

The core workflow is question-based research: a user asks a research question, Epsilon searches a dataset of more than 200 million papers, identifies relevant documents, and generates a summarized answer with inline citations. It also supports publication and patent search, extraction of information across multiple papers, and organization of uploaded papers into searchable libraries, positioning it as a research productivity tool for academic and professional knowledge work.

Features

• Question answering with inline citations: Users can ask a research question and receive a synthesized answer that references underlying source passages, which helps with traceability and evidence review.
• Large-scale paper discovery: Epsilon searches a dataset from Semantic Scholar covering over 200 million academic papers, improving breadth for scientific topic exploration.
• Publication and patent search: The platform can surface both papers and patents, then group results into latest research, key texts, and most relevant articles to speed up source triage.
• Multi-paper information extraction: Users can enter a question or claim and have Epsilon scan top search results to pull relevant information from each document, which is useful for citation finding and structured evidence checks.
• Paper upload and library search: Uploaded papers can be summarized by introduction, results, and conclusion, then saved into libraries that users can search across later for synthesis work.
• Research organization support: Libraries and saved papers help teams or individuals organize trusted materials for ongoing projects, onboarding, and repeated topic analysis.

Helpful Tips

• Verify summary outputs against cited passages: Inline citations improve transparency, but researchers should still inspect the referenced text before relying on conclusions in formal work.
• Use it first for narrowing, then for close reading: Tools like this are most effective for reducing search and screening time before deeper evaluation of methodology and evidence quality.
• Test it on a defined research question: Adoption is easier when teams begin with a bounded use case such as citation gathering, early literature review, or claim validation.
• Check data handling requirements internally: The site states that search queries are sent to third-party providers such as OpenAI, so organizations should review whether that fits their research privacy expectations.
• Clarify patent workflow needs before buying: The page mentions patent search and an organization plan with patent analysis, but it does not fully describe depth of patent-specific review features, so buyers should confirm those details.