I will build langchain rag pipeline and ai agents


About this gig
Generic AI chatbots hallucinate. RAG-powered systems don't because they answer from your actual data.
I'm a Principal Software Engineer specialized in building production-grade AI systems using LangChain, LangGraph, RAG pipelines, and multi-agent architectures. I've achieved 92% retrieval accuracy processing 1,000+ documents daily in enterprise environments.
What you get:
- RAG pipeline with document ingestion (PDF, DOCX, CSV, web pages)
- Vector embedding & semantic search (Pinecone / Weaviate / pgvector)
- LangChain chains, agents & custom tools
- LangGraph multi-agent orchestration
- LLM integration OpenAI GPT-4, Claude, Gemini, or open-source
- MCP (Model Context Protocol) server integration
- Hallucination control & response quality tuning
- REST API wrapping for any frontend
Real projects delivered:
- Auxee 92% retrieval accuracy, 1,000+ docs/day, 50+ enterprise clients
- ShopFloorGPT multi-agent RAG platform on Microsoft Marketplace
- AI Marketing Automation LangChain + OpenAI boosting conversions by 20%
- I don't build demos. I build AI systems that work reliably in production.
Get to know Uzman Khan
Principal Software Engineer
- FromPakistan
- Member sinceApr 2026
- Avg. response time1 hour
Languages
English
FAQ
Which LLMs do you support?
OpenAI GPT-4/4o, Anthropic Claude, Google Gemini, and open-source models via OpenRouter or Ollama.
Which vector database do you recommend?
Pinecone for managed simplicity, Weaviate for schema-based retrieval, pgvector if you're already on PostgreSQL.
How do you prevent hallucinations?
Through strict context grounding, prompt engineering, confidence scoring, and response validation layers.
LangChain or LangGraph — which do I need?
LangChain for single-agent pipelines. LangGraph for complex multi-agent workflows with state management. I'll advise based on your use case.

