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I will build a custom rag system which chat with your documents
Muneeb Shah
Über diesen Service
Looking for a custom RAG system that lets you chat with your own documents and data? You're in the right place.
I build production-ready Retrieval-Augmented Generation (RAG) pipelines and AI chatbots that answer questions from YOUR knowledge base accurately, with sources, and zero hallucinations.
What I build
- Custom RAG system / AI chatbot over your docs (PDF, Word, web, Notion, SQL)
- Vector database setup (Pinecone, Chroma, FAISS, pgvector)
- LangChain / LlamaIndex pipelines with OpenAI or Claude
- Chat API (FastAPI) + optional web chatbot UI
- Source-cited, grounded answers
Perfect for: internal knowledge assistants, customer support bots, document Q&A, research tools, and SaaS features.
️Tech stack: Python, LangChain, LlamaIndex, OpenAI, Claude, Pinecone, Chroma, FAISS, pgvector, FastAPI.
I'm an AI engineer who ships real RAG systems end-to-end clean code, clear docs, and a walkthrough on handover.
Message me your use case before ordering so I can confirm scope and recommend the best plan for your RAG system.
Lerne Muneeb Shah kennen
Muneeb Shah
Introduction
- AusPakistan
- Mitglied seitDez. 2024
- ⌀ Antwortzeit1 Stunde
Sprachen
Paschtunische Sprache, Urdu, Englisch
Hi, I am Muneeb, an AI Agent & RAG Developer with 2+ years of experience and 20+ AI systems delivered for startups and international clients.
I build production-grade AI agents, RAG chatbots, and multi-agent systems using LangChain, LangGraph, and vector databases — that actually reason over your data and ship reliably, not just demos that break.
Free consultation included to scope your project the right way and recommend the best approach for your goals.
Message me now to get started.
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FAQ
What is a RAG system and why do I need one?
RAG (Retrieval-Augmented Generation) lets an AI answer using your documents instead of guessing. It retrieves the most relevant content from your knowledge base, then generates an accurate, source-backed answer — so no hallucinations.
What documents and data can it use?
PDFs, Word docs, text, websites, Notion, Google Docs, CSVs, and SQL databases. If you have a data source not listed, just ask.
Which models and tools do you use?
OpenAI (GPT) or Anthropic (Claude) for the LLM; LangChain/LlamaIndex for the pipeline; Pinecone, Chroma, FAISS, or pgvector for the vector database. We pick what fits your budget and scale.
Will the answers be accurate?
Yes — answers are grounded in your data and can include source citations, so you always know where each answer came from.
Do I own the code?
100%. You get clean, documented code, setup instructions, and a walkthrough on handover.
Can you deploy it for me?
Yes — the Premium package includes deployment, or add it as an extra. I can deploy to AWS, GCP, Azure, or your preferred host.
