You will get an AI Document Q&A System with RAG Pipeline & Source Citations
Project details
Upload your documents. Ask questions in plain English. Get accurate answers with exact source citations — no more digging through 200-page PDFs to find one paragraph.
I'll build you a custom document Q&A system powered by LangChain and OpenAI. Your team uploads PDFs, Word docs, or any text-based files, and the system indexes them into a searchable knowledge base. When someone asks a question, it pulls the right information and points them to the exact section it came from.
This works for internal knowledge bases, customer support docs, legal contracts, research papers, HR policy manuals — anything where people waste time searching through documents manually.
What you get:
• Clean chat interface for asking questions about your documents
• RAG pipeline that actually retrieves relevant content (not hallucinated answers)
• Source citations so users can verify every answer
• Document upload and management
• Deployed and ready to use
I've already built and deployed this exact type of system — check my portfolio for a live demo. I can start within 48 hours of project kickoff.
Send me a message with details about your documents and use case, and I'll confirm which tier fits best.
I'll build you a custom document Q&A system powered by LangChain and OpenAI. Your team uploads PDFs, Word docs, or any text-based files, and the system indexes them into a searchable knowledge base. When someone asks a question, it pulls the right information and points them to the exact section it came from.
This works for internal knowledge bases, customer support docs, legal contracts, research papers, HR policy manuals — anything where people waste time searching through documents manually.
What you get:
• Clean chat interface for asking questions about your documents
• RAG pipeline that actually retrieves relevant content (not hallucinated answers)
• Source citations so users can verify every answer
• Document upload and management
• Deployed and ready to use
I've already built and deployed this exact type of system — check my portfolio for a live demo. I can start within 48 hours of project kickoff.
Send me a message with details about your documents and use case, and I'll confirm which tier fits best.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, Conversational AI, Natural Language GenerationAI Development Language
PythonAI Models
ChatGPT, GPT-3, GPT-4What's included
| Service Tiers |
Starter
$800
|
Standard
$1,500
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 10 days |
Number of Revisions | 2 | 3 | 3 |
AI Model Integration | |||
Batch Normalization | |||
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | - | |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | - | - | |
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | |
Natural Language Processing | - | - | |
NLP Tokenization | - | - | |
Pre-Training | - | - | |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Anurag
AI-Powered SaaS Developer | LangChain, OpenAI, RAG
Ahmedabad, India - 12:21 am local time
What I deliver:
→ AI/LLM Integration: Custom GPT-powered features, RAG pipelines, LangChain agents, and OpenAI API integrations that actually work in production
→ Full-Stack SaaS: End-to-end development with React/Next.js frontends and Node.js/Express backends, deployed and scalable
→ API Architecture: Clean, well-documented REST and GraphQL APIs built for performance and maintainability
Recent builds:
• AI Research Assistant: RAG-based app using LangChain + Pinecone that processes 500+ documents with sub-second retrieval
• SaaS Starter Kit: Multi-tenant platform with Stripe billing, auth, and role-based access, used as a foundation for 3 client projects
• Telecom Research Portal: Delivered full platform from scratch in 6 months
I work async-first, communicate proactively, and ship on time. Every project includes clean code, documentation, and a smooth handoff.
Let's talk about what you're building →
Steps for completing your project
After purchasing the project, send requirements so Anurag can start the project.
Delivery time starts when Anurag receives requirements from you.
Anurag works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Document Review
We'll hop on a quick call or async chat to understand your documents, your users, and what "good answers" look like for your use case. You send me sample documents.
RAG Pipeline Setup
I build the document processing pipeline — chunking, embedding, and vector storage. I test retrieval quality against your actual documents and tune it until answers are accurate.