Enterprise RAG Explained: The Fastest Way to Search Company Knowledge

The Problem With Scattered Company Knowledge

Most organizations have information scattered across PDFs, wikis, internal documentation, knowledge bases, and shared folders. Traditional search relies on keywords — you find documents that contain the words you typed, not documents that answer the question you asked.

RAG (Retrieval-Augmented Generation) enables search based on meaning. When a user asks a question, relevant documents are found, relevant passages are retrieved, and AI generates an answer based on that retrieved content. This dramatically improves accuracy, especially for complex or nuanced queries.

How RAG Works

The process has three steps:

1. Documents are indexed by converting text into vector embeddings stored in a vector database
2. User queries are embedded using the same model, and semantically similar passages are retrieved
3. The retrieved passages are passed to a language model, which generates a grounded answer

The key insight is that the retrieval layer does most of the heavy lifting — the language model only needs to synthesize what was already found.

Recommended Architecture

• Open WebUI — user interface
• Qdrant — vector database for semantic retrieval
• PostgreSQL — document and metadata storage
• Langfuse — query monitoring and evaluation

Do You Need a GPU?

Surprisingly, no. Many organizations already use external LLM providers for generation. In this scenario, the retrieval layer is far more important than local model hosting. The recommended NexNodo deployment is a Cloud Compute VPS XXL:

• 16 vCPU
• 32 GB RAM
• 640 GB SSD
• $0.208/hr or $152/month

This makes enterprise RAG accessible to organizations that aren't ready to invest in GPU infrastructure.

Build Your Knowledge Platform

Deploy the Enterprise RAG Platform Template directly from the NexNodo Marketplace and start indexing company knowledge immediately.