RAG Pipeline Development for Enterprise: Architecture, Vector DBs & Real Performance

Retrieval Augmented Generation is the most practical way to give LLMs access to your company's knowledge base. Here's how to build one that works in production.

Why RAG?

LLMs hallucinate. RAG solves this by retrieving relevant documents from your knowledge base and including them in the LLM's context window. Result: accurate, sourced answers grounded in your actual data.

Architecture

• Document Ingestion — Parse PDFs, docs, HTML, databases into text chunks
• Embedding — Convert chunks to vectors using OpenAI Ada-002 or Cohere Embed V3
• Vector Storage — Store embeddings in Milvus, Pinecone, or Weaviate
• Retrieval — Hybrid search (semantic + keyword) with reranking
• Generation — Pass retrieved context + user query to GPT-4/Claude
• Evaluation — Automated accuracy testing with ground truth datasets

Performance Metrics We Achieve

• Answer accuracy: 92-97% (vs 60-70% for vanilla LLM)
• Retrieval precision@5: 85-95%
• Response latency: 1.5-3 seconds end-to-end
• Hallucination rate: <3%

Enterprise Considerations

• Data privacy — Self-hosted models (Llama 3.1) for sensitive data
• Access control — Document-level permissions in the retrieval layer
• Scalability — Milvus handles 100M+ vectors with sub-100ms retrieval
• Cost — Self-hosted embeddings reduce costs by 80% vs API-based

Development Timeline & Cost

• POC (single data source): 3-4 weeks, $15,000-30,000
• Production (multi-source, auth): 8-12 weeks, $50,000-100,000
• Enterprise (self-hosted, compliance): 12-16 weeks, $100,000-200,000

TelGates specializes in building production RAG systems for fintech, legal, and healthcare enterprises.

Ready to Scale? Contact TelGates today to discover how our engineering team can build robust, scalable solutions tailored to your unique requirements.