AI knowledge base software helps teams store documents and answer questions with LLMs using RAG. Unlike traditional wikis with keyword search, AI-native knowledge bases use semantic search and generation. This guide lists what buyers and builders should evaluate.
Support for PDF, Office, Markdown, HTML, and connectors (SharePoint, Google Drive, tickets) reduces friction. Ask how parsers handle tables, images, and scanned PDFs (PDF to knowledge base).
Enterprises need per-user or per-group visibility so retrieval never leaks restricted docs. Enterprise RAG & security covers isolation patterns. WeKnora includes multi-tenant support architecture.
Software should surface which passages supported each answer. This improves trust and auditability—critical for support and compliance use cases in use cases.
REST APIs, webhooks, and SDKs let you embed Q&A in existing apps. Review rate limits, auth models, and streaming for chat UX (Chat with your documents).
Look for query logs, thumbs-up/down on answers, and export for offline evaluation. Without metrics, improving chunking and embeddings is guesswork.
WeKnora is open source (MIT), includes document understanding, semantic retrieval, agent mode, and MCP integration, and is used in production at scale. Compare options in WeKnora vs alternatives.