Research Institute: Navigating the Literature Jungle with AI

An independent research institute with 120 scientists in materials science and chemistry publishes around 180 journal articles per year. Every article requires a systematic literature overview: what is known? What is contested? What is missing?

A systematic literature review across three databases (Scopus, Web of Science, PubMed) takes 2–4 weeks — manually reading 500–800 abstracts, assessing relevance, extracting key findings, identifying contradictions. For early-career researchers, it is often the most time-consuming phase of their work.

Unpublished Research Data Must Not Leave the Institute

Research data, raw experimental results, and unpublished findings are subject to strict confidentiality obligations — towards funders, collaboration partners, and scientific competitors. Using external AI services for this data was out of the question.

Local Literature Review Assistant with SoverIQ

SoverIQ Stack runs on the institute server. The system supports the literature review process:

• Exported paper databases (BibTeX, RIS) are imported and indexed semantically
• Enter research question → rank relevant papers by semantic similarity
• Cluster analysis: which research directions can be identified?
• Contradiction detection: where do papers reach opposing conclusions?
• Automatically generated review summary: state of research on a question, with citations
• Research gap identification: what has not yet been investigated?

Results

Average duration of a systematic literature review fell from 3.5 weeks to 8 days. Scientists now read more targeted papers and less often miss important work. Three research projects were initiated directly from AI-identified research gaps.