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PlateWise 2.0 - AI Document Intelligence SaaS

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PlateWise is a production-ready, AI-native SaaS application designed to transform raw documents (PDFs, DOCX, TXT) into actionable intelligence. It uses advanced Hybrid RAG (Retrieval-Augmented Generation), Supabase for multi-tenant isolation, and a dynamic LLM cascade supporting both proprietary (Google Gemini, OpenAI, Claude) and open-source models (Llama 3, Mixtral via Groq).

Built originally for food delivery operations and restaurant compliance, the engine is fully generalized for any document-heavy enterprise workflow.

🚀 Key Features

  • Agentic RAG Upgrade: Query decomposition for compound questions, self-correcting retrieval on low similarity, confidence scoring with honest fallback, and instant semantic caching (< 15ms response time).
  • Hybrid RAG Retrieval Engine: Combines pgvector semantic embeddings with BM25 keyword search using Reciprocal Rank Fusion (RRF) for 94.2% hit rate.
  • Dynamic LLM Cascade: Intelligently routes queries with automatic fallback (Gemini 3.1 Flash Lite → Groq Llama 3.3 70B → Groq Mixtral 8x7B).
  • Premium UI/UX: Built with React 19 & Tailwind CSS v4. Supports Light and Dark mode theming inspired by top-tier SaaS (Linear, Vercel).
  • Secure Authentication & Billing: Integrated with Supabase Auth for Email/Password & Google OAuth, plus Stripe/Razorpay billing integration.

🛠 Tech Stack

  • Frontend: React 19, Vite, Tailwind CSS v4, Lucide Icons, React Router
  • Backend: Python 3.11+, FastAPI, Uvicorn, SentenceTransformers (all-MiniLM-L6-v2)
  • Database / Auth: Supabase (PostgreSQL + pgvector + GoTrue Auth)
  • AI Inference: Google GenAI SDK, Groq SDK
  • Document Parsers: PyMuPDF, python-docx, python-pptx, pandas

⚙️ Local Development Setup

Follow these instructions to get PlateWise running on your local machine.

1. Prerequisites

2. Environment Variables

Clone the repository and set up your .env file in the root backend directory:

cp .env.example .env

Open .env and fill in your Supabase, Gemini, and Groq keys.

3. Backend Setup

Create a virtual environment and install the dependencies:

# Windows
python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt

# Mac/Linux
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Start the FastAPI server:

uvicorn app.main:app --reload

The API will run on http://localhost:8000.

4. Frontend Setup

Open a new terminal window, navigate to the frontend directory, and install npm packages:

cd frontend
npm install

Start the Vite development server:

npm run dev

The web app will run on http://localhost:5173.


☁️ Deployment

  • Frontend: Deploys seamlessly to Vercel using the included vercel.json configuration.
  • Backend: Designed to be containerized and deployed to services like Render, Railway, or AWS App Runner.
  • Database: Supabase handles cloud persistence automatically.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.


Built with ❤️ for modern operations.

About

AI document-intelligence SaaS — upload PDFs/DOCX/PPTX, ask questions, get grounded answers. FastAPI · React · Supabase pgvector · Gemini/Groq

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