RAG Knowledge Base Builder

name: rag-knowledge-base description: Build RAG (Retrieval-Augmented Generation) knowledge bases for businesses — turn documents, SOPs, policies, and product manuals into AI assistants that answer questions with source citations and confidence scores. The pipeline ingests and preprocesses corpora, generates embeddings, stores vectors in scalable vector databases, and orchestrates retrieval + LLM components (e.g., LlamaIndex, OpenAI) to produce citation-backed responses. Key capabilities include document connectors, chunking with metadata preservation, vector search, citation-aware answer generation, monitoring, periodic reindexing, and integration with chat, web, and Slack. Designed to reduce support load, deliver more grounded answers (fewer hallucinations), speed knowledge access, and provide a clear commercial path with build and maintenance SLAs.