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English
$
USD
I will build a secure rag ai chatbot app for documents
Praveen
About this gig
Welcome to enterprise-grade AI integration.
Many organizations want to use the power of Large Language Models on internal files, but face two massive issues: public data leaks and mixed-user data crossover.
I build secure, session-isolated RAG (Retrieval-Augmented Generation) web applications that keep your business data completely private and performant.
What this application delivers:
Session Isolation: Prevents data leaks by creating independent runtimes and vector store keys for separate users.
Smart Document Queries: Dynamic interfaces that organize files into distinct sections for clear navigation.
High-Performance Architecture: Fast text-splitting and optimized vector indexing using FAISS/Chroma for precise retrieval.
Conversational Memory: Context-aware streaming chains that remember past messages for natural follow-ups.
Premium Interface: Fully dark-mode optimized dashboard layout built with Gradio/Streamlit.
Please message me with your data structure requirements before ordering so we can map out the perfect solution!
Get to know Praveen
Praveen
Expert AI Engineer and Custom RAG App Developer
- FromIndia
- Member sinceApr 2026
- Avg. response time1 hour
Languages
English
Turn your business documents into a 24/7 AI chatbot that answers instantly using YOUR data only — securely, with zero hallucinations. I build custom RAG (Retrieval-Augmented Generation) chatbots using Python, LangChain/LangGraph, FAISS/ChromaDB, deployed via Streamlit or Gradio. Ideal for founders, consultants, support teams & businesses tired of answering repetitive questions manually. Fully private and production-ready — your data never touches public AI tools. Message me your use case first, and I'll explain exactly how it works before you order.
My Portfolio
FAQ
What technical stack do you use for development?
I build applications primarily in Python, leveraging LangChain and LangGraph for orchestrating chains, FAISS or ChromaDB for vector stores, and Streamlit or Gradio for sleek web interfaces.
How is data isolation handled between different users?
The architecture rewrites vector store logic to securely isolate data streams. Files uploaded by User A are stored in a partitioned vector database instance that cannot be accessed or queried by User B's session runtime.
