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I will build a rag ai chatbot using your documents and knowledge base
Abdullah
About this gig
Need an AI chatbot that can answer questions from your own documents and knowledge base?
I will build a custom RAG AI chatbot that retrieves relevant information from your PDFs, documents, website, FAQs, manuals, or business data before generating answers.
What I offer:
- Custom RAG chatbot development
- Document and PDF Q&A
- AI knowledge base chatbot
- Business knowledge chatbot
- Website data integration
- Document ingestion and processing
- Embeddings and vector search
- Vector database integration
- Source citations and grounded answers
- Custom chatbot interface or API
- LangChain / LlamaIndex integration
- OpenAI, Gemini, Claude and other LLMs
Your RAG chatbot can be used for customer support, internal company knowledge, product documentation, research, employee assistance, FAQs, SaaS support, and document-based question answering.
I can build a RAG system around your existing knowledge base and connect it with your website, application, or business workflow.
Send me your documents, knowledge base, or project requirements before ordering so I can recommend the right RAG architecture for your use case.
Get to know Abdullah
Abdullah
AI Engineer LLM Fine Tuning, RAG and Custom AI Chatbots
- FromPakistan
- Member sinceSep 2026
- Avg. response time1 hour
Languages
English, Punjabi, Spanish, German, French, Italian
I’m an AI Engineer specializing in LLM solutions that solve real business problems. I provide AI model fine-tuning for more accurate, task-specific results, RAG systems that securely connect AI to private knowledge, intelligent chatbots that automate customer interactions, and custom LLM applications built around your workflow. I focus on reliable, scalable solutions that improve efficiency, reduce manual work, and deliver better AI experiences.
My Portfolio
FAQ
What is RAG?
RAG allows an AI model to retrieve relevant information from an external knowledge base before generating its response.
Can you build RAG from PDFs?
Yes. I can process compatible PDF documents, create embeddings, store the information in a vector database, and connect retrieval to an LLM.
Can I use my own documents?
Yes. Your private documents can be used as the knowledge source for the RAG system.
Do I need fine-tuning for RAG?
Not necessarily. RAG and fine-tuning solve different problems. RAG is generally used to provide an LLM with external/private knowledge, while fine-tuning is used to adapt model behavior, style, or task performance.
Can you integrate RAG with my existing chatbot?
Yes. A RAG pipeline can be integrated with a compatible existing chatbot or AI application.
Can you use a specific vector database?
Yes, depending on the project's requirements and compatibility.
