QueryNest AI Platform

Intelligent chatbot infrastructure for secure, multi-tenant knowledge access across enterprise and customer-facing environments.

Project Title

QueryNest AI Platform: Intelligent Chatbot Infrastructure for Secure Knowledge Access

Team Name

Technological Coder

Problem Statement

Modern organizations are not constrained by lack of data, but by their inability to operationalize knowledge efficiently and securely across both internal and external touchpoints.

• Customer-facing systems still rely on static navigation, leading to user drop-offs, lower conversions, and reduced engagement.
• Internal teams depend on manual policy retrieval, causing delayed decisions, inconsistent understanding, and compliance risks.

Identified gaps:

1. Absence of unified knowledge access.
2. Lack of context-aware intelligence.
3. Weak data governance in AI systems.
4. Inability to support dual environments (enterprise + public website).
5. Limited integration and deployment flexibility.

Solution Overview

QueryNest AI is a dual-mode, multi-tenant RAG platform designed for secure knowledge operations:

• Internal Compliance Assistant: operates in controlled/offline enterprise environments.
• External Website Chatbot: handles public customer interactions through embeddable widgets.
• Admin-controlled ingestion: only approved data sources are ingested.
• RAG-grounded responses: answers are generated from approved sources to reduce hallucinations.
• Multi-source knowledge support: URLs, PDFs, docs, platform content, and video transcription.
• Tenant-isolated architecture: data remains segregated and secure per organization.

PPT Link

• Project deck : HackMatrix_2026.pptx.pdf

Live Demonstration Link

• Live demonstration (YouTube):
• Optional production URL: ADD_LIVE_WEBSITE_LINK_HERE

Technology Stack

Layer	Technology
Frontend	React, Next.js, Tailwind CSS, shadcn/ui
Backend	FastAPI, Python 3.11
RAG & Orchestration	LangChain, LlamaIndex, FlashRank
AI/LLM	Gemini, Anthropic Claude, OpenAI
Embeddings	Voyage AI (1024-dim), OpenAI fallback
Vector Database	ChromaDB
Data & Auth	Supabase (PostgreSQL, JWT, RLS, RBAC)
Ingestion	Crawl4AI, PyMuPDF/OCR, Whisper transcription
Analytics	Recharts, conversation logging
Deployment	Docker, Railway, Vercel

Team Members

1. Harshal Sudhakar Marathe
2. Vedant Narayan Mehar
3. Mayur R Chikhale
4. Aum Santosh Mishra

Setup Instructions

Prerequisites

• Python 3.11+
• Node.js 18+
• Docker Desktop
• Supabase account
• LLM API key(s): Anthropic or Gemini
• Voyage AI API key (recommended)

1. Clone the repository

git clone https://github.com/<your-team>/<your-repo-name>.git
cd <your-repo-name>

2. Configure environment files

# Root template (documents both frontend/backend variables)
cp .env.example .env

# Backend
cp backend/.env.example backend/.env

# Frontend
cp frontend/.env.example frontend/.env.local

Update these files with your actual keys and URLs.

3. Set up Supabase

1. Create a Supabase project.
2. Open SQL Editor.
3. Run supabase/schema.sql.

4. Start local infrastructure

docker-compose up -d

5. Run backend

cd backend
pip install -r requirements.txt
playwright install chromium
uvicorn main:app --reload --port 8000

6. Run frontend

cd frontend
npm install
npm run dev

deployment

local

Visit http://localhost:3000.

frontend -> Vercel

#backend -> render query-nest-ai-hack-matrix-2026-vedants-projects-745d7440.vercel.app https://query-nest-ai-hackmatrix-2026.onrender.com

Sources on chatbot hosted

1 https://indian-culture-azure.vercel.app/ 2 https://e-commerce-xi-ochre-77.vercel.app/ 3 https://crazy-veins-gym.vercel.app/

License

This project is licensed under the MIT License. See LICENSE.