Literfy

The platform connects directly to academic databases including Google Scholar, PubMed, and Semantic Scholar to surface relevant papers based on research topics, rather than generating content from training data alone.

This database connection is the critical design choice that separates Literfy from general AI writing tools for academic use, because it grounds the literature review in actual published papers rather than hallucinated citations.

The search and organization capabilities help researchers identify the scope of existing research on their topic before writing begins.

Literfy generates searchable keyword terms from the research topic, highlights pivotal papers from the results, and organizes findings into topic clusters that surface research gaps and missing areas.

The cluster visualization allows researchers to understand the structure of existing literature and identify where their own research contribution sits relative to prior work.

The review outline generation produces structured literature review sections based on actual papers found in the database search, with each section linked to the specific citations supporting it.

This citation linking is maintained throughout the outline, allowing researchers to trace every claim in the generated structure back to the underlying paper. The drag-and-drop reordering of sections allows researchers to adjust the outline structure without losing citation links.