VectifyAI/OpenKB

Self-hosted retrieval-augmented generation that lets you point a language model at your own document corpus and get accurate, source-cited answers without shipping any data to a third party. You bring your documents, configure your preferred LLM backend, and the system handles chunking, embedding, indexing, and query retrieval in one deployable package. The appeal is practical: most enterprise-grade RAG offerings lock you into a vendor's infrastructure, pricing tier, or data processing agreement. OpenKB sidesteps all of that. Because you control the stack entirely, it fits naturally into air-gapped environments, regulated industries, or any situation where a data processing addendum with an AI vendor is not politically feasible. Concretely useful for internal developer documentation portals, legal or compliance teams indexing policy documents, or any small engineering org that wants a Notion AI-style experience over private wikis without the SaaS dependency. The honest reservation is operational: self-hosting RAG means you own the maintenance burden, embedding pipeline reliability, and model hosting costs. It is not a plug-and-play SaaS replacement. -> Best for: technical founders or DevOps-minded teams building internal knowledge tools in compliance-sensitive environments.