RAG Server (RAGTY)

The Problem RAG Solves

LLMs like GPT-4 or Claude are powerful but have critical limitations:

1. Knowledge cutoff — they don't know about your internal documents, policies, or recent data

2. Hallucination — without grounding, they invent plausible-sounding but wrong answers

3. No access control — public LLMs can't respect your organization's data permissions

4. No citations — you can't verify where an answer came from

How RAG Works

Instead of relying solely on the LLM's training data, RAG retrieves relevant passages from your knowledge base and augments the LLM's prompt with them before generating a response.

Real-World Examples

Why a Dedicated RAG Server?

Building RAG properly requires solving many hard problems:

• Document parsing — PDFs with tables, scanned pages, complex layouts need vision-based parsing (not just text extraction)

• Chunking — documents must be split into meaningful pieces that preserve context

• Hybrid search — combining semantic search (understands meaning) with keyword search (finds exact terms like product IDs)

• Multi-tenancy — each team/customer sees only their own documents

• Scalability — handling thousands of documents and concurrent users

• Observability — knowing why a particular answer was generated

The RAGTY server solves all of these as a production-ready platform.

Backend

Frontend