Enables LLMs to chat with long PDFs using a reasoning-based, tree-structured document index that navigates content like a human would, without requiring vector databases or hitting context limits.
Enables enterprise document retrieval using graph-based reasoning and knowledge graphs. Allows agents to search and extract information from scattered documents through structured entity and relationship extraction.
Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.
Enables LLMs to search and retrieve information from large technical documentation (OpenAPI specs, markdown) via intelligent chunking and semantic search.
Enables working with large documents of any size by intelligently segmenting them and using TF-IDF search to retrieve only relevant fragments, preventing context window saturation. Provides 31 domain-agnostic tools for document ingestion, semantic analysis, epistemological validation, and extraction verification across formats like PDF, EPUB, and HTML.
