Browse categories
Explore
Fiverr Pro
English
$
USD
I will build a custom ai chatbot with rag using your own documents and data
Saad Irfan
About this gig
Your business has knowledge. Your customers have questions. I build the AI assistant that connects them.
I build custom RAG AI assistants that retrieve answers directly from your documents, knowledge base, website, or product catalog. Every response is grounded in your real data, not generic AI guesswork.
What I build:
AI chatbots that read your PDFs, answer from your documentation, and function as internal knowledge assistants.
Key features:
RAG pipeline with LangChain
FAISS vector database
PDF, DOCX, TXT, and web ingestion
Source-attributed answers
OpenAI, Claude, or open-source models
Streamlit or custom UI
Use cases:
Document Q&A, customer support, internal knowledge base, product assistant, FAQ chatbot, website AI assistant, AI-powered search.
You receive:
Working system, clean documented code, ingestion pipeline, vector setup, deployment guide, and full walkthrough.
Why me:
I engineer RAG systems on real business data, not demo wrappers. Clean architecture, transparent communication, solutions built around your use case.
Message me with your use case and the documents you want your AI to know. I will outline the best approach before we start.
Get to know Saad Irfan
Saad Irfan
AI Developer, AI Agents, Automation and Web Development
- FromPakistan
- Member sinceMay 2015
- Avg. response time1 hour
Languages
English, Urdu, Spanish
Hi, I'm Saad - an AI Developer specializing in chatbots, RAG systems, and AI agents (Python, LangChain, Groq, FAISS). I also build full-stack web apps (React, Next.js) and offer automation, dashboards, and API integrations. I focus on shipping working systems fast, with clean, documented code and clear communication throughout.
My Portfolio
FAQ
What file formats do you support for the knowledge base?
PDF, DOCX, TXT, Markdown, CSV, and website URLs are supported. Custom formats can be added on request.
What is RAG and why does it matter?
RAG (Retrieval-Augmented Generation) lets the AI retrieve relevant information from your documents before generating an answer. This produces accurate, source-grounded responses instead of generic guesses
Can the chatbot be added to my website?
Yes. Depending on your package, I can deploy it as a web app, embeddable widget, or API endpoint that connects to your site.
Which AI models do you use?
I can integrate the AI model that best fits your project, including Groq-powered models, OpenAI, Anthropic Claude, and open-source or locally hosted models through Ollama. The choice depends on your use case, privacy, performance, and budget.
How many documents can the chatbot handle?
The Basic package supports a small set for MVP testing. The Standard and Premium packages are designed for larger, multi-source knowledge
Will the chatbot show where it got the answer from?
Yes. Source attribution is included so users can see which document or section the answer was retrieved from.
Can the chatbot be deployed on my own server?
Yes. Deployment support is included in the Standard and Premium packages.
Do you provide the source code?
Yes. You receive clean, documented source code and full ownership of the system.
Can the chatbot handle follow-up questions?
Yes. Conversational memory is included in the Standard and Premium packages, allowing the assistant to understand context across multiple messages.
What if my requirements are unique?
Send me a message with your use case. I will review it and propose a tailored approach before you commit to a package.
