Keenious
AI-powered academic research recommendations integrated directly into your writing workflow.
Professional-grade browser intelligence and document synthesis agent for research-intensive workflows.
Casper AI is a sophisticated AI-driven research assistant designed to optimize information processing for professionals, researchers, and students. Architecturally, Casper AI functions as a context-aware browser layer that leverages Retrieval-Augmented Generation (RAG) to analyze web content, complex PDFs, and digital documents in real-time. By 2026, Casper AI has transitioned from a basic summarization tool into a multi-modal synthesis engine, capable of cross-referencing information across multiple browser tabs and local files to generate high-fidelity reports, executive summaries, and structured data exports. Its technical stack is optimized for low-latency interactions, utilizing advanced LLMs (like GPT-4o and Claude 3.5 variants) to provide semantic understanding of dense technical jargon and legal prose. The platform addresses the 'information overload' problem by allowing users to query their current digital environment through a persistent sidebar, ensuring that critical insights are extracted without context switching. Positioned as a direct competitor to tools like Perplexity and Otter.ai, Casper AI differentiates itself through its deep integration into the browsing experience and its focus on outputting professional-ready drafts directly into existing productivity suites like Gmail and LinkedIn.
Aggregates context from all open browser tabs to answer queries that require cross-referencing multiple sources.
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.
The precision-engineered AI research assistant that eliminates hallucinations with verifiable citations.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Utilizes OCR and semantic chunking to analyze large, text-heavy PDF documents directly in the browser.
Generates content based on the active tab's context while mimicking the user's previously learned tone.
Identifies and pulls key data points like dates, figures, and names into a structured JSON or Table format.
Optional local processing mode where metadata is scrubbed before being sent to LLM endpoints.
UI modification that hides non-essential page elements while highlighting Casper's key findings.
Vector-based search of all previously summarized pages to find information using natural language.
Analysts need to parse dozens of startup websites and pitch decks quickly.
Registry Updated:2/7/2026
Researchers struggle to find specific methodologies in long scientific PDFs.
Attorneys need to find specific 'Force Majeure' clauses in long contracts.