From documents to answers, with full traceability
Our platform transforms your static documents into an intelligent knowledge base. Upload PDFs, research papers, contracts, or any text — and get accurate, cited answers to your questions. Every response references its sources, making it perfect for academic research, legal work, and professional analysis.
Your documents pass through a structured, AI-driven processing pipeline to become searchable and fully citable knowledge assets.
Upload PDF, DOCX, TXT, or Markdown files. Batch uploads are fully supported.
AI-powered parsing extracts text, tables, and structural hierarchy with high accuracy.
Images, charts, and diagrams are analyzed and converted into searchable textual representations.
Authors, publication dates, DOIs, references, and structural information are automatically extracted.
Documents are split into intelligent, structure-aware chunks enriched with contextual headers.
Each chunk is transformed into a vector embedding for semantic similarity search.
Embeddings are stored in our managed, or your own (BYOK) vector database together with full metadata for fast and precise retrieval.
Upload PDF, DOCX, TXT, or Markdown files. Batch uploads are fully supported.
AI-powered parsing extracts text, tables, and structural hierarchy with high accuracy.
Images, charts, and diagrams are analyzed and converted into searchable textual representations.
Authors, publication dates, DOIs, references, and structural information are automatically extracted.
Documents are split into intelligent, structure-aware chunks enriched with contextual headers.
Each chunk is transformed into a vector embedding for semantic similarity search.
Embeddings are stored in our managed, or your own (BYOK) vector database together with full metadata for fast and precise retrieval.
We use Docling (AI-native document parsing) to extract structured content from PDFs, tables, and complex layouts. Contextual Chunk Headers (CCH) preserve document hierarchy, while multimodal extraction processes images, charts, and visual elements.
Unlike basic RAG systems that retrieve once and answer, our agentic approach performs multi-step reasoning, iterative retrieval, and source synthesis.
The AI analyzes your question to identify key concepts and sub-questions that need answers.
Relevant document chunks are retrieved from the vector database based on semantic similarity.
The AI evaluates retrieved information and identifies missing information or contradictions.
Additional retrieval rounds fill in gaps, ensuring comprehensive coverage of your question.
All information is combined into a coherent answer with precise citations to every source.
The AI connects information across multiple documents to build comprehensive answers.
Multiple retrieval cycles ensure no important information is missed.
Every claim in the response is traceable to its source document.
Every answer includes precise, traceable citations in APA 7th edition format. Perfect for literature reviews, meta-analyses, and academic writing.
Automatically formatted, always accurate, fully traceable to source documents.
Every citation is verified against the original document to ensure accuracy.
Inconsistent metadata is automatically normalized to APA 7th edition standards.
Authors, DOIs, publication dates, and journal names are automatically extracted.
Click any citation to see the exact content of the source document.
Our citation system follows APA 7th edition guidelines — the standard for social sciences, education, and business research. Support for Chicago and MLA styles coming soon.