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 querying Confluence or Kubernetes documentation through hybrid search and an agentic RAG pipeline, returning structured answers with citations.
Enables agentic document retrieval over markdown, CSV, and JSONL using BM25 and tree navigation, without vector databases or embeddings, allowing AI agents to search, browse, and retrieve structured document sections.
Enables reading and analyzing Confluence documentation content through the Confluence REST API. Supports extracting and converting text, tables, code blocks, and images from Confluence pages for Q\&A and analysis.
Turns Confluence documentation into an AI-powered knowledge base, enabling natural language questions about your systems with answers retrieved from your actual documentation through semantic search.
Enables AI assistants to read Confluence Cloud pages as markdown, browse page trees, download image attachments, and diff content against local documentation.
