Key Features

• •Semantic Search with AI Embeddings: Uses vector embeddings to enable AI assistants to find relevant documentation based on conceptual meaning, not just keywords, ensuring accurate results even when query terminology differs.
• •CLI-First & Portable Skills: Install the Yavy CLI to search docs from the terminal or generate portable skill packages for offline AI use, integrating knowledge directly into projects without a server connection.
• •MCP Server for Real-Time Querying: Provides a Model Context Protocol server that allows AI assistants to query your indexed content in real-time, delivering always-fresh, structured JSON responses optimized for LLMs.
• •Automatic Re-Indexing & Sync: Continuously crawls and re-indexes content sources on a configurable schedule (daily, hourly, or real-time) to ensure the knowledge base stays current as documentation changes.
• •Multiple Source Support: Ingests content from various sources including public websites, GitHub repositories, Notion workspaces, and Confluence spaces, handling discovery and intelligent extraction.
• •Secure Access & Authentication: Supports OAuth 2.1 authentication for secure access, enabling both public sources and private projects for teams with enterprise-grade security controls.
• •Structured Context Delivery: Intelligently chunks content based on semantic headers and returns data in clean JSON format, optimized for consumption by large language models and AI agents.
• •Flexible Integration Paths: Offers choice between CLI/Skills for offline, fast access and MCP Server for real-time querying, compatible with AI tools like Claude Code, Cursor, and Windsurf.

Use Cases

• •Developer Documentation: Index API references, SDK guides, and framework docs so AI coding assistants provide answers grounded in the current, correct version, preventing deprecated API suggestions and saving debugging time.
• •AI Knowledge Base for Support: Transform help centers and support articles into a searchable base for chatbots, ensuring customer queries about policies or procedures receive accurate answers linked to source articles.
• •Internal Documentation: Make company wikis, onboarding guides, and process documentation AI-searchable for employees, enabling quick, accurate information retrieval through AI assistants within tools like Slack or MS Teams.
• •Educational Content: Allow educational platforms to index course materials and tutorials, enabling AI tutors or study assistants to provide precise, contextually relevant explanations and answers to learners.
• •Legal & Compliance Docs: Enable AI systems to accurately query indexed legal documents, compliance manuals, or policy pages, ensuring responses are strictly based on the official, up-to-date text.

Who is this for?

Yavy targets developers, technical teams, and organizations that create and maintain documentation, help content, or internal knowledge bases. Primary users include software engineers, DevOps professionals, technical writers, and support teams who need to ensure AI tools like Claude, Cursor, and VS Code have access to accurate, up-to-date information from their sources. It is also for product managers and educators who want to ground AI interactions in factual content from websites, GitHub repos, Notion, or Confluence. The platform serves both individual developers and growing teams or enterprises needing reliable, scalable knowledge grounding for AI workflows.