Cited Q&A
Ask any question about your papers and get answers grounded in the source text, with page-level citations you can verify.
The Research Assistant reads your PDFs, understands them with vector search, and answers your questions with real citations — no more scrolling through 30 pages to find one fact.
Five AI-powered tools built on a production RAG pipeline — from instant Q&A to full literature reviews.
Ask any question about your papers and get answers grounded in the source text, with page-level citations you can verify.
Generate brief, comprehensive, or technical summaries with extracted key findings, methodology and limitations.
Synthesize a whole topic across papers — key themes, research gaps and future directions, generated automatically.
An intent-routing LangChain agent picks the right tool for each request and chains steps to reason through complex asks.
Drop a PDF and watch it get parsed, chunked, embedded and indexed into vector storage in real time.
Semantic + keyword search over 384-dim embeddings finds the most relevant passages, not just keyword matches.
A transparent RAG pipeline — no black boxes.
Every response is backed by the exact passages it came from. Click a citation to jump straight to the source — perfect for literature reviews, thesis work and staying honest with your references.
Async Python backend, managed vector storage and blazing-fast inference.