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

M
mehediimun
M
mehediimun
Mehedi

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

Mehedi

Full Stack Software Engineer

  • FromBangladesh
  • Member sinceMay 2026
  • Avg. response time1 hour
  • Languages

    English
I am a Full Stack Software Engineer with 3+ years of experience building end-to-end web apps, RESTful APIs, and real-time features. I specialize in Node.js, Express.js, TypeScript, React, and Next.js, with a strong track record of leveraging AI-driven development to ship high-quality code quickly.

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