I will build a custom rag or multi agent ai pipeline using llms, faiss, and AWS bedrock

Want to build an AI system that retrieves accurate, relevant answers from your data not hallucinations? I specialize in building production-grade RAG (Retrieval-Augmented Generation) systems and multi-agent AI pipelines powered by real LLMs.

What you get:

• FAISS or vector DB setup for fast semantic search over your documents
• LLM integration (Groq, Ollama, OpenAI, or AWS Bedrock)
• RAG strategies: standard RAG, RAG Fusion, HyDE, or Corrective RAG
• Multi-agent pipeline with discrete roles (planner, retriever, responder)
• FastAPI backend with clean REST endpoints
• React frontend for real-time query and response
• AWS Bedrock or SageMaker deployment
• Evaluation framework to benchmark retrieval accuracy

Why me? I built a full RAG comparison system from scratch indexing the CRAG web corpus with FAISS and implementing 4 retrieval strategies (RAG Fusion, HyDE, Corrective RAG, Graph RAG) with a live benchmarking dashboard. I'm AWS Certified with hands-on experience in Amazon Bedrock and SageMaker. You're getting a real AI engineer, not a wrapper builder.

Send me your use case (document Q&A, customer support bot, knowledge base, etc.) and I'll recommend the best architecture.