AI Visibility Platform

Analyze and optimize product discoverability for AI search engines

An AI-powered platform that helps e-commerce businesses optimize their product descriptions for AI search engines like ChatGPT, Perplexity, and Gemini.

🎯 Problem It Solves

E-commerce products are not optimized for AI search engines. This tool analyzes how discoverable a product is by AI systems and provides actionable optimization suggestions.

✨ Features

• AI Visibility Score (0-100): Comprehensive scoring based on 4 key components
• Semantic Analysis: Uses sentence-transformers for semantic similarity
• Competitive Ranking: Compare your product against competitors
• LLM-Powered Optimization: Automatically rewrite descriptions for better AI visibility
• Weakness Detection: Identify missing specs, keywords, and clarity issues
• Before/After Comparison: See the impact of optimizations

🏗️ Architecture

[ Streamlit Frontend ]
          |
          v
[ FastAPI Backend ]
          |
  ---------------------
  |   ML Services     |
  |-------------------|
  | Embedding Engine  |
  | Scoring Engine    |
  | Ranking Engine    |
  | LLM Optimizer     |
  ---------------------

🧮 Scoring Formula

Final Score = 
  0.4 × Semantic Relevance +
  0.2 × Keyword Coverage +
  0.2 × Completeness +
  0.2 × Readability

🚀 Quick Start

Prerequisites

• Python 3.8+
• pip

Installation

1. Clone the repository:

git clone <repository-url>
cd ai-visibility-platform

2. Install dependencies:

pip install -r requirements.txt

3. Setup environment variables: Create a .env file in the root directory (use .env.example as a template):

cp .env.example .env

Fill in your GEMINI_API_KEY and other optional keys in the .env file.

Running the Application

1. Start the FastAPI backend:

cd backend
python main.py

The API will be available at http://localhost:8000

2. In a new terminal, start the Streamlit frontend:

cd frontend
streamlit run app.py

The dashboard will open in your browser at http://localhost:8501

📦 API Endpoints

POST /api/analyze-product

Analyze a product and return AI visibility score with detailed breakdown.

Request:

{
  "title": "Sony WH-1000XM5 Wireless Headphones",
  "description": "Premium noise cancelling headphones...",
  "category": "Headphones",
  "brand": "Sony"
}

Response:

{
  "ai_visibility_score": 75.5,
  "score_breakdown": {
    "semantic_relevance": 80.2,
    "keyword_coverage": 65.0,
    "completeness": 70.5,
    "readability": 85.0
  },
  "weakness_analysis": {
    "missing_specs": ["battery life", "warranty"],
    "suggestions": ["Add more technical specifications"]
  }
}

POST /api/rank-products

Rank your product against competitors.

POST /api/optimize-description

Optimize product description using LLM.

🧪 Example Use Case

Input:

• Product: "Noise cancelling headphones"
• Description: "Good headphones for music"

Analysis:

• Score: 56/100
• Rank: #4 out of 8
• Missing: Battery life, use cases, compatibility

After Optimization:

• Score: 82/100
• Rank: #1
• Improvements: Added specifications, use cases, and relevant keywords

🛠️ Tech Stack

Backend:

• FastAPI
• Pydantic
• Uvicorn

ML/AI:

• sentence-transformers (all-MiniLM-L6-v2)
• scikit-learn
• textstat
• OpenAI API (optional)

Frontend:

• Streamlit
• Plotly

📁 Project Structure

ai-visibility-platform/
├── backend/
│   ├── main.py
│   ├── routers/
│   │   └── analyze.py
│   ├── services/
│   │   ├── embedder.py
│   │   ├── scorer.py
│   │   ├── ranker.py
│   │   └── optimizer.py
│   ├── schemas/
│   │   └── product.py
│   └── core/
│       └── config.py
├── frontend/
│   └── app.py
├── requirements.txt
└── README.md

🚀 Deployment

Backend Deployment (Render/Railway)

1. Connect your GitHub repository
2. Set environment variables (OPENAI_API_KEY if using LLM)
3. Deploy with command: python backend/main.py

Frontend Deployment (Streamlit Cloud)

1. Push to GitHub
2. Connect to Streamlit Cloud
3. Set main file path: frontend/app.py
4. Configure API_BASE_URL to your deployed backend

🎯 Future Enhancements

• CSV upload for bulk product analysis
• Chrome extension for real-time analysis
• Multi-language support
• User authentication and history
• Amazon product scraper integration

📝 License

MIT License

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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