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I will build a custom ai chatbot with rag, langchain and vector database
Nisar Ahmad
About this gig
Looking for a production-ready AI chatbot that actually answers from YOUR data not generic AI guesses? I build custom RAG chatbots using LangChain, LangGraph, and vector databases (pgvector, Pinecone, Qdrant).
WHAT YOU GET: RAG pipeline (document ingestion smart chunking embeddings hybrid retrieval reranking cited answers); your data sources (PDFs, docs, websites, FAQs, databases); production backend (FastAPI, background workers, Redis caching answers in under a second); guardrails (hallucination reduction, multi-tenant data isolation with JWT); clean, embeddable chat widget for your website.
WHY ME: I'm an Applied AI Engineer with 3+ years building production LLM, RAG and agentic AI systems (Python, LangChain/LangGraph, FastAPI).
PACKAGES: Basic ($90): RAG chatbot on up to 50 documents, single data source, 7-day delivery. Standard ($200): up to 500 documents, multiple sources, website widget, 14-day delivery. Premium ($400): unlimited-scale RAG, custom retrieval tuning, analytics dashboard, priority support, 21-day delivery.
Message me before ordering I'll confirm the best package for your use case.
Get to know Nisar Ahmad
Nisar Ahmad
Applied AI Engineer, LLM Apps, RAG Pipelines and AI Chatbots
- FromPakistan
- Member sinceNov 2026
- Avg. response time1 hour
Languages
Urdu, English
I'm Nisar Ahmad, an Applied AI Engineer with 3+ years of experience building production AI systems. I build LLM-powered applications, RAG pipelines, AI chatbots, and voice AI agents using Python, LangChain, LangGraph, and FastAPI. My work includes a bilingual voice-AI platform answering callers in under a second, document-intelligence systems with retrieval guardrails, and a computer-vision model live in production. I ship clean, working AI features from prototype to production.
FAQ
Which LLMs do you work with?
OpenAI, Anthropic, Gemini — your choice.
Do I need technical knowledge?
No, I deliver a working chatbot plus simple setup instructions.
Can it speak Urdu or other languages?
Yes, multilingual support is available.
What is RAG and why does my chatbot need it?
RAG (retrieval-augmented generation) makes the chatbot answer from YOUR documents instead of guessing — fewer hallucinations, always up to date with your data.
Which documents can the chatbot learn from?
PDFs, Word docs, websites, FAQs, databases — almost any text source.
Do you deploy the chatbot on my server?
Yes, I can deploy on your server or cloud (Docker/AWS/GCP), or hand you the full source code.
