I will build a custom rag system with vector database for your app


About this gig
Normal chatbots invent facts. RAG doesn't. It searches your documents first, then answers only from what it found. Real answers, with sources.
WHAT YOU GET
- Chatbot trained on your PDFs, docs or database
- Semantic search that understands meaning
- Vector embeddings MongoDB Atlas, Pinecone, pgvector
- Redis caching for fast replies
- REST API that fits any frontend
- Multi-tenant setup for SaaS
- Full source code, yours forever
Support bots Knowledge assistants Document Q&A Product recommendation SaaS AI features
Node.js TypeScript React Next.js MongoDB Vector Search PostgreSQL Redis Docker AWS OpenAI, Claude & OpenRouter.
WHY ME
Full stack engineer, 3+ years in production systems. I already shipped a live RAG platform with vector search, multi-tenant isolation and under 1.5s response time. I am not learning this on your project.
Message me before ordering. Tell me your data and your goal. I'll tell you honestly which package fits or if you don't need RAG at all.
Get to know Mehedi
Full Stack Software Engineer
- FromBangladesh
- Member sinceMay 2026
- Avg. response time1 hour
Languages
English
My Portfolio
FAQ
What is RAG in simple words?
Normal AI answers from memory and sometimes invents facts. RAG makes the AI search your documents first, then answer only from what it found. Accurate and traceable.
Do you use my data to train a model?
No. Your data stays in your own vector database. Nothing is used for training.
Who pays for the OpenAI API costs?
You do, and it stays in your account. I will help you pick a model that keeps costs low.
Can you add this to my existing app?
Yes. I build it as an API so it fits into React, Next.js, WordPress, mobile, anything.
What if I don't know which package I need?
Message me first. I will look at your data and recommend the right one. No pressure.
Do I get the source code?
Yes, full source code on every package.

