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I will build a rag ai chatbot for your documents and knowledge base
Jeet Majumder
About this gig
Turn your documents and business knowledge into an AI assistant that can actually answer questions from your data.
I will build a custom RAG (Retrieval-Augmented Generation) AI chatbot that retrieves relevant information from your documents, website, FAQs, manuals, knowledge base, or other approved sources before generating answers.
What I can build:
- Document Q&A assistants
- Business knowledge-base chatbots
- Customer support assistants
- Internal company knowledge assistants
- Research/document assistants
- Website knowledge chatbots
- Custom RAG applications
RAG capabilities can include:
- Document ingestion & processing
- Embeddings & vector search
- Retrieval pipeline
- Source citations
- Conversation memory
- Metadata filtering
- Database/vector database integration
- API integration
- Custom chat interface
I can work with PDFs, DOCX, websites, FAQs, manuals, reports, and other structured or unstructured knowledge sources.
Every project is built around your data, use case, and required workflow rather than using a generic chatbot template.
Have a knowledge base you want to turn into an AI assistant? Let's build it.
Get to know Jeet Majumder
Jeet Majumder
AIML Engineer
- FromIndia
- Member sinceSep 2026
- Avg. response time1 hour
Languages
English, Bengali, Hindi
I am an AI/ML Engineer with 2+ years of experience building and deploying AI systems using Python, LLMs, RAG, and Agentic AI. I specialize in designing end-to-end retrieval pipelines, multi-agent workflows, and tool orchestration using LangGraph, FastAPI, and LangSmith to deliver reliable production-grade AI solutions.
My Portfolio
FAQ
Can the AI answer strictly from my own knowledge base?
Yes. I can configure the RAG system to retrieve information from your provided documents, website, or knowledge base before generating an answer. I can also add source citations so users can see where the information came from.
What happens when the answer isn't available in my documents?
The system can be configured to recognize when relevant information cannot be found instead of generating an unsupported answer. I can also define a custom fallback response for questions outside your knowledge base.
Can I update the knowledge base after the chatbot is built?
Yes. I can design the system so new or updated documents can be processed and added to the knowledge base without rebuilding the entire application. The exact update workflow depends on your project and package.
Can you connect my existing website, database, or business system?
Yes. Depending on the project, the RAG system can connect with websites, databases, APIs, internal applications, or other approved knowledge sources. Integration requirements are scoped according to the selected package.
How do you handle large or multiple document collections?
I can design the retrieval pipeline around the size and structure of your knowledge base, including document processing, chunking, embeddings, vector search, metadata filtering, and retrieval optimization. Larger or more complex knowledge bases are handled in the higher-tier packages.
