Keenious: Find Relevant Literature with Free AI || Get Research Ideas & Explore Further | Hindi 2024

Keenious is positioned as a free, AI-assisted literature discovery platform that turns a researcher’s “base paper” and “seed paper” into a targeted set of relevant, similar papers—then helps users narrow results by topic, publication window, citation counts, and even highlighted text. The practical payoff is faster literature review: instead of manually scanning search results and bibliographies, users upload a PDF (or paste a PDF URL) and get a curated pathway to papers that match the uploaded content.

The workflow starts with account creation, then selecting what to feed the system. Users can upload a base paper PDF and a seed paper PDF, or provide a direct PDF URL. Once the documents are processed, Keenious activates filters and displays a list of relevant articles—often hundreds of pages worth of results—ranked around the uploaded material. From there, the platform supports topic-wise exploration, letting users click into a specific theme (for example, “Artificial Intelligence” or narrower subtopics) and then see related and similar articles clustered around that selection.

Keenious also emphasizes structured filtering for research needs. Users can constrain results by publication dates (the transcript mentions filtering to a range such as 2021–2023), set citation thresholds (e.g., minimum and maximum citation counts), and apply additional filters tied to the research area. A notable feature is “highlight search”: while reading an article, users can select a sentence or phrase of interest, and the system searches for other papers containing similar highlighted text. The resulting papers then appear as a focused subset, helping researchers follow a more specific thread than broad keyword search.

For citation management and reuse, the platform includes bookmarking and citation-related options, plus download capabilities. The transcript also notes that results can be exported in different formats (including a mention of “11” as a selectable format option), and that users can copy and download citation information.

Pricing and access are framed around free individual use, with institutional access potentially requiring contact for coverage. The transcript contrasts Keenious with heavier, paid “AI literature” platforms that monetize subscriptions and highlights that Keenious aims to reduce friction for students, researchers, and librarians.

Overall, Keenious is presented as a privacy-friendly, document-driven research tool: upload or link a PDF, then use AI-driven similarity plus filters and highlight-based search to generate research ideas, find relevant literature, and speed up the next steps of a literature review—especially when users already have a promising base paper but need the surrounding papers to read next.