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Page-Level Citation Grounding
Every summary point and RAG chat answer references exact source page numbers and original text snippets from vector retrieval, eliminating hallucinations.
MULTIMODAL AI DOCUMENT INTELLIGENCE PLATFORM
Transform Complex Documents Into Grounded Synthesis & Actionable Knowledge
SummaMind Studio is a high-precision document intelligence engine. Upload PDFs, scanned images, audio files, text documents, or website URLs to generate 11 structured summary formats backed by 100% page-level citation grounding.
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Multi-LLM Orchestration
Select optimal models dynamically: Smart AI Router, OpenAI GPT-4.1 Turbo for legal structure, Anthropic Claude Sonnet 5 for narrative synthesis, or Google Gemini 2.5 Pro for multimodal charts.
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11 Output Summary Formats
Generate Short, Medium, Detailed Summaries, Bullet Points, Key Takeaways, Action Items, FAQs, Timeline & Chapters, MCQ Quizzes, and Structured JSON in one click.
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TLS 1.3 & Zero Retraining
Your sensitive documents are processed over encrypted channels and indexed in isolated Supabase pgvector stores. Your data is never used to train public LLMs.
How SummaMind Ingestion Works
Four seamless steps from raw document upload to verified knowledge extraction.
01
Upload or Link
Drop PDFs, scanned photos, MP3 audio, text files, or paste article URLs directly into the Reading Room.
02
Chunking & Indexing
PyMuPDF, Tesseract OCR, and OpenAI Whisper extract text into 1536-dim embeddings stored in Supabase pgvector.
03
Format Selection
Select from 11 specialized summary readouts or ask custom natural language queries in the RAG Chat drawer.
04
Citation Verification
Hover over citation pills in any summary or chat answer to verify the original document snippet and page number.
Supported Document Formats & Processing Engines
| Format Type | Extensions | Ingestion Module | Processing Capability |
|---|---|---|---|
| PDF Documents | PyMuPDF + OCR Fallback | Page-level structure extraction, table parsing, embedded image OCR | |
| Scanned Images | .png, .jpg, .webp | Tesseract OCR Engine | Full text extraction from high-resolution photos of printed pages |
| Audio Recordings | .mp3, .wav, .m4a | OpenAI Whisper STT | Automatic speech-to-text transcription with timestamp alignment |
| Plain Text / Notes | .txt, .md | Direct Markdown Parsing | Raw text ingestion for documentation, notes, and code repositories |
| Website URLs | https://... | Automated Web Crawler | Strips boilerplate HTML and extracts article text for instant synthesis |
Frequently Asked Questions
What types of files can I upload to SummaMind Studio?
SummaMind Studio accepts PDF documents (including scanned PDFs via OCR), images (PNG, JPG, WebP), audio recordings (MP3, WAV, M4A), plain text files (.txt, .md), and direct website URLs. Each format is handled by a specialized ingestion engine.
How does page-level citation grounding work?
When you run summarization or ask questions in the RAG Chat, every answer is constructed from retrieved source chunks stored in Supabase pgvector. Each chunk tracks exact page numbers and snippet locations rendered as interactive citation pills.
Which AI models are available for summarization?
You can select between four engines: Smart AI Router (auto-selects best model), OpenAI GPT-4.1 Turbo (best for legal/technical extraction), Anthropic Claude Sonnet 5 (ideal for narrative synthesis), and Google Gemini 2.5 Pro (best for multimodal charts).
Is my uploaded document data kept private?
Yes. All uploads are encrypted with TLS 1.3. Vector embeddings reside in an isolated Supabase pgvector schema tied exclusively to your account. We enforce zero model retraining policies with all underlying LLM API providers.
What summary formats can SummaMind generate?
SummaMind generates 11 distinct formats: Short Summary, Medium Summary, Detailed Summary, Bullet Points, Key Takeaways, Extracted Details, Action Items, Generated FAQ, Timeline & Chapters, MCQ Quiz, and Structured JSON data.