🧠 NexAI Research

🚀 AI Use Case Generator for Products

A powerful AI tool that helps companies discover innovative AI use cases for their existing products

---

✨ Features

• 🤖 AI-Powered Use Cases: Generate innovative AI implementation ideas for existing products
• 📊 Market Research: Analyze potential applications in your industry
• 🔍 Dataset Discovery: Find relevant datasets via Kaggle API integration
• 🌐 Company Analysis: Automatic information retrieval via web scraping
• 📝 Use Case Documentation: Well-structured AI implementation suggestions

🛠️ Tech Stack

• 🐍 Python
• 🧠 Cohere API (AI use case generation)
• 📊 Streamlit (UI)
• 📁 Kaggle API (dataset discovery)
• 🔍 BeautifulSoup4 (web scraping)

📷 Screenshorts

📥 Installation

# Clone the repository
git clone https://github.com/manojkp08/NexAI_Research.git
cd NexAI_Research

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys

⚙️ Configuration

Before running the tool, update the following in your .env file:

COHERE_API_KEY=your_cohere_api_key
KAGGLE_USERNAME=your_kaggle_username
KAGGLE_KEY=your_kaggle_key

🚀 Usage

# Start the Streamlit interface
streamlit run app.py

📋 How It Works

1. Input Company Information: Enter your company name and product details
2. AI Analysis: The system analyzes your product through Cohere API
3. Use Case Generation: Creates innovative AI implementation scenarios
4. Dataset Discovery: Finds relevant datasets through Kaggle API
5. Implementation Plan: Generates a structured plan for each use case

📁 Project Structure

NEXAI_RESEARCH/
├── __pycache__/
├── market_research_env/
├── .env
├── .gitignore
├── app.py
├── database.py
├── dataset_finder.py
├── docker-compose.yml
├── Dockerfile.streamlit
├── requirements.txt
├── research_agent.py
└── use_case_generator.py

🔍 Key Components

• app.py: Main Streamlit application interface
• research_agent.py: Core AI research orchestration
• use_case_generator.py: AI use case generation logic
• dataset_finder.py: Kaggle dataset discovery module
• database.py: Data storage and retrieval

📊 Use Case Examples

• Predictive Maintenance: AI monitoring of equipment to predict failures
• Customer Segmentation: Advanced clustering for targeted marketing
• Natural Language Interfaces: Voice or text interfaces for products
• Computer Vision Integration: Visual recognition capabilities
• Recommendation Systems: Personalized suggestion engines

---

⭐ Star this repository if you find it useful! ⭐

Made with ❤️ by Manoj