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I will build rag ai chatbot with citations, knowledge gap and analytics with live demo
Mohammad S
About this gig
I'll build a production-ready AI chatbot that answers questions from YOUR documents with source citations so users can verify every answer.
WHAT YOU GET:
- AI chatbot trained on your PDFs, docs, and help articles
- Citation-backed answers (every response shows which document it came from)
- Real-time analytics dashboard (conversation volume, sentiment, top questions)
- Knowledge gap detection (AI tells you what content is missing)
- Deploy to website widget, WhatsApp, Telegram, or REST API
- Full source code you own everything, no vendor lock-in
HOW IT WORKS:
- You send me your documents (PDFs, Word files, text, FAQs)
- I build and train the RAG pipeline on your content
- I deploy it to your preferred channel(s)
- You get a live chatbot + admin dashboard + analytics
TECH STACK:
FastAPI + Next.js + PostgreSQL + Pinecone + Groq/OpenAI with LLM failover
TRY THE LIVE DEMO: rag.fenloai.com
Chat with it, browse conversations, view analytics everything works.
I've built enterprise-grade AI systems with workspace isolation, rate limiting, RBAC auth, and semantic caching. Message me with your use case and I'll give you a custom quote.
Get to know Mohammad S
Mohammad S
Research Assistant
- FromBangladesh
- Member sinceJan 2021
Languages
Bengali, English, Hindi, Chinese
Full-stack AI developer, 4+ years in production ML, computer vision, and web apps. Research Assistant at NSU Robotics Lab, former AI Engineer at NITEX (multi-modal systems, triton, OCR, data scrapping, trend analysis), Google Summer of Code '22 alum (TensorFlow), research intern at DeepPavlov.ai. I build: AI chatbots with RAG, custom AI agents, ML pipelines & model deployment, computer vision systems, GenAI solutions (ComfyUI, Stable Diffusion), workflow automation (n8n, Zapier), full-stack web apps (FastAPI, Next.js, React).
Live demo: rag.fenloai.com
Portfolio: shoaib6174.github.io
FAQ
What is a RAG chatbot and how is it different from ChatGPT?
RAG (Retrieval-Augmented Generation) means the chatbot only answers from YOUR documents — not from the internet. Every answer includes citations showing which document it came from. Unlike ChatGPT, it won't hallucinate or make up information. It only knows what you teach it.
Can I try it before ordering?
Yes! Visit rag.fenloai.com to try the live demo. You can chat with the bot, view the analytics dashboard, browse conversations, and see the knowledge base — all live and interactive.
What file formats do you support?
PDF, Word documents (.docx), and plain text files. I handle all the parsing, chunking, and embedding. Just send me your files and I'll take care of the rest.
Do I get the source code?
Yes, every package includes full source code (frontend + backend). You own everything. No monthly fees, no vendor lock-in. You can modify, extend, and host it wherever you want.
What channels can the chatbot be deployed to?
Website widget (floating chat bubble), WhatsApp Business, Telegram bot, and REST API for custom integrations. The Basic package includes the website widget. Standard adds one more channel. Premium includes all channels.
How does the analytics dashboard work?
The dashboard shows conversation volume over time, sentiment analysis (positive/negative/neutral), quality scores, top questions asked, and lead scoring. Available in Standard and Premium packages. You can see a live demo at rag.fenloai.com/analytics.
Can I update the knowledge base after delivery?
Absolutely. The admin dashboard lets you upload new documents, delete old ones, and monitor knowledge gaps (questions the bot couldn't answer). The system automatically re-indexes when you add new content.
What tech stack is this built with?
Backend: FastAPI (Python 3.12) with PostgreSQL and Redis. Frontend: Next.js 15 with React 19. Vector search: Pinecone. LLM: Groq (primary) with OpenAI failover. Everything is containerized and deployment-ready.
Do you provide hosting/deployment?
The Premium package includes full AWS deployment with CI/CD. For Basic and Standard, I deliver the source code with a deployment guide, and you can deploy on your own server, AWS, or any cloud provider. I can help with deployment setup as an add-on.
