AI Insight Engine v2.0

The fastest way to test, compare, and integrate AI services from AWS, Azure, and Google Cloud

🌟 What's New in v2.0

• ✅ Complete React Migration - Modern TypeScript codebase
• ✅ 5 Analysis Types - Sentiment, Entities, Key Phrases, Language, Classification
• ✅ Interactive Code Viewer - Copy-paste ready examples
• ✅ Multi-language Support - English & French
• ✅ Rate Limiting Protection - Cost management built-in
• ✅ Mobile Optimized - Works perfectly on all devices

🚀 Live Demo

Try it now: ai-insight-engine.vercel.app

📊 AI Analysis Types

Analysis	Description	Providers
😊 Sentiment	Emotional tone detection	AWS, Azure, Google
🔑 Key Phrases	Main topics extraction	AWS, Azure, Google
👥 Entities	People, places, organizations	AWS, Azure, Google
🌐 Language	Automatic language detection	AWS, Azure, Google
📁 Classification	Content categorization	AWS, Azure, Google

💻 Code Examples

Each analysis includes ready-to-use code snippets for:

• AWS SDK with Comprehend
• Azure SDK with Text Analytics
• Google Cloud with Natural Language

🛡️ Built-in Protection

• Rate Limiting: 50 requests/hour per IP
• Text Validation: 10-5000 characters
• Cost Management: Intelligent caching
• Error Handling: User-friendly messages

⚙️ Setup (Essential Step)

To use this project, you must provide your own API keys. Here is how to get them.

• AWS :

  • Log in to your AWS console and navigate to the IAM service.
  • Create a new user and grant a_c_c_e_s_s to the Amazon Comprehend service on the permissions tab
  • Create Access Keys on the Security Credentials
  • At the end of the process, copy your Access Key ID and Secret Access Key.
  • You will maybe also need your AWS Region (e.g., us-east-1).

• AZURE :

  • On the Azure portal, create a new resource of type "Azure AI Services".
  • Once the resource is created, navigate to the "Keys and Endpoint" section.
  • Copy one of the keys and the endpoint.

• GOOGLE CLOUD PLATFORM :

  • On the Google Cloud console, create a new project.
  • Go to "APIs & Services" and enable the "Cloud Natural Language API".
  • Go to "Credentials" and create an "API Key". Copy this key.

• Once you have all these keys, create use the sample.env file to create an .env file in the root of the backend folder, based on the provided .env.example file.

Launch the project

git clone
cd backend
npm install
node api/analyze |  vercel dev (depend on your local runtime environment)
---
cd frontend
npm install
npm run dev
Enjoy