Uploads a PDF document, extracts its full text via OCR and native parsing, then sends the content to an LLM for a condensed summary highlighting key points and insights. Most processing runs locally; specific features use cloud AI.
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PDF AI Summariser lets you uploads a PDF document, extracts its full text via OCR and native parsing, then sends the content to an LLM for a condensed summary highlighting key points and insights. Most processing runs locally; specific features use cloud AI.. It works on any device with a modern web browser.
Uploads a PDF document, extracts its full text via OCR and native parsing, then sends the content to an LLM for a condensed summary highlighting key points and insights. Most processing runs locally; specific features use cloud AI. It runs entirely in your browser — no software installation or data uploads required.
Paste or type your content directly into the provided text area.
No. PDF AI Summariser requires an internet connection because it uses cloud-based processing for its core functionality.
The tool extracts text from your PDF (native text or OCR for scanned docs), then sends it to an LLM which generates a condensed summary highlighting key points, main arguments, and actionable insights.
Yes. For scanned documents, the tool uses OCR to extract text before summarization. Image quality affects OCR accuracy — clear scans produce better summaries.
PDFs up to 50 pages are supported for optimal summarization. Longer documents may be truncated or summarized in sections.
PDF text is sent to the AI API for summarization. No PDF files are stored permanently. Check the privacy policy for API-specific data handling.
Bullet-point summary, paragraph summary, and executive summary. Choose the format that matches your use case — quick reference, detailed review, or high-level overview.
AI Chat PDF
Chat with your PDF documents. Ask questions, get answers, and extract insights from any document. Extract text and use intelligent search to find relevant information instantly.
AI Document Chat (RAG)
Indexes uploaded PDFs, Word files, and plain-text documents into a vector store and lets you ask natural-language questions about their contents.