Customizable AI-Powered PDF Chatbot
Budget: ₹12,500 – ₹37,500 INR
Title: AI-Powered PDF Chatbot Using RAG (Retrieval-Augmented Generation)
Description:
I built an intelligent PDF chatbot that lets users upload any document and get instant, accurate answers — no more manually searching through pages of text.
How it works:
The bot uses Retrieval-Augmented Generation (RAG) to combine the power of large language models with your own document data. Instead of relying on generic AI knowledge, it retrieves the most relevant sections from the uploaded PDF and generates precise, context-aware answers grounded in that specific content.
Key features:
Upload any PDF (contracts, reports, research papers, manuals, etc.)
Ask questions in natural language and get instant, accurate answers
Semantic search using vector embeddings — finds relevant info even if exact keywords don't match
Reduces hallucinations by grounding responses in the actual document
Scalable architecture — can handle single files or large document libraries
Tech stack:
LangChain / LlamaIndex • OpenAI / Claude API • Vector Database (Pinecone / ChromaDB / FAISS) • Python • [Streamlit / React frontend]
Use cases:
Perfect for legal document review, customer support knowledge bases, research assistants, internal company wikis, or any business drowning in PDFs that needs fast, reliable answers.
I can customize this solution for your specific documents, industry, and use case — from a simple single-file Q&A bot to a full enterprise-grade knowledge assistant.
Description:
I built an intelligent PDF chatbot that lets users upload any document and get instant, accurate answers — no more manually searching through pages of text.
How it works:
The bot uses Retrieval-Augmented Generation (RAG) to combine the power of large language models with your own document data. Instead of relying on generic AI knowledge, it retrieves the most relevant sections from the uploaded PDF and generates precise, context-aware answers grounded in that specific content.
Key features:
Upload any PDF (contracts, reports, research papers, manuals, etc.)
Ask questions in natural language and get instant, accurate answers
Semantic search using vector embeddings — finds relevant info even if exact keywords don't match
Reduces hallucinations by grounding responses in the actual document
Scalable architecture — can handle single files or large document libraries
Tech stack:
LangChain / LlamaIndex • OpenAI / Claude API • Vector Database (Pinecone / ChromaDB / FAISS) • Python • [Streamlit / React frontend]
Use cases:
Perfect for legal document review, customer support knowledge bases, research assistants, internal company wikis, or any business drowning in PDFs that needs fast, reliable answers.
I can customize this solution for your specific documents, industry, and use case — from a simple single-file Q&A bot to a full enterprise-grade knowledge assistant.