RAG Architecture Design & Implementation
LLM systems powered by your corporate data with hallucination control.
RAG (Retrieval-Augmented Generation) is the most effective approach to feed LLMs with your corporate knowledge. Yazansoft builds production-grade RAG systems from chunking strategy to vector database selection, retrieval optimization to evaluation frameworks.
What We Offer in RAG Systems
Technologies We Use
RAG Use Cases
Corporate Knowledge Base Assistant
Source-cited Q&A over company documents, wiki pages, and knowledge base.
Legal & Compliance Assistant
Intelligent search and analysis over regulations, contracts, and policy documents.
Technical Documentation Assistant
Developer assistant over API docs, user guides, and technical knowledge base.
Customer Support Automation
Tier-1 customer support automation fed with product info and support history.
RAG Systems FAQ
How accurate are RAG system results?
With proper chunking, embedding, and retrieval strategy, 90%+ faithfulness and relevancy scores are achievable. Continuous evaluation and improvement via RAGAS framework.
Which vector database should we use?
If you use PostgreSQL, pgvector is the most practical choice. For high scale, Qdrant or Pinecone can be evaluated. We recommend based on your project needs.
Can you add RAG to our existing LLM?
Yes. Adding a RAG layer to your existing chatbot or AI system to enhance it with corporate knowledge is possible.
Let's Discuss Your RAG Project
Let's determine the most effective way to power your corporate knowledge base with AI.
Schedule RAG AssessmentLet's Discuss Your Project
The initial assessment meeting is free. Let's evaluate your technical needs together.