Keenious
AI-powered academic research recommendations integrated directly into your writing workflow.
The precision-engineered AI research assistant that eliminates hallucinations with verifiable citations.
Afforai is a sophisticated AI-powered reference manager and research assistant designed to handle complex document analysis at scale. By 2026, it has solidified its position in the market by utilizing a proprietary multi-step Retrieval-Augmented Generation (RAG) pipeline that prioritizes source integrity over generative creativity. Unlike standard LLM interfaces, Afforai's technical architecture revolves around a 'Triple-Check' verification system that cross-references every AI-generated claim against the user's uploaded data corpus, providing clickable citations that lead directly to the source text. This makes it an essential tool for high-stakes environments like academia, legal discovery, and medical research. The platform supports over 100 languages and allows users to compare information across hundreds of documents simultaneously. Its infrastructure is built to support various state-of-the-art models including GPT-4o, Claude 3.5 Sonnet, and Gemini Pro, allowing researchers to toggle between models depending on the required reasoning depth or context window size. As of 2026, Afforai serves as a centralized hub for managing bibliographies, conducting thematic analysis, and automating the synthesis of disparate data points into coherent summaries.
A proprietary verification loop that forces the AI to provide evidence from the uploaded corpus before finalizing an answer.
AI-powered academic research recommendations integrated directly into your writing workflow.
Transform authoritative academic data into structured educational narratives and story-driven research summaries.
Transform scholarly research into grounded narratives and professional audio stories with source-centric AI.
Evidence-based AI search engine that finds answers in peer-reviewed research.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Ability to query up to 100 documents simultaneously using a high-dimension vector database.
Users can switch between OpenAI, Anthropic, and Google models within the same chat interface.
Directly ingest and index live web pages for real-time data synthesis alongside static PDFs.
Automated extraction of metadata (DOI, ISSN, Authors) to create formatted citations.
Support for 100+ languages for both document ingestion and AI querying.
Automatically categorizes document segments based on latent Dirichlet allocation (LDA) and semantic clustering.
Manually reviewing 200+ papers for a meta-analysis is time-prohibitive.
Registry Updated:2/7/2026
Export the summary as a draft for the research paper.
Identifying hidden liabilities across thousands of contract pages.
Comparing patient symptoms against rare disease journals.