Synaptic Insight Engine

Live Demo: synaptic-insight-engine.vercel.app

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An AI-powered web application designed to analyze scientific papers and tech case studies. The engine scrapes content from a provided URL, identifies potential red flags, uncovers legitimate opportunities, and generates a strategic MVP blueprint based on its findings.

This project was built from the ground up, demonstrating a full-stack development process from front-end UI/UX design to backend AI integration and serverless deployment.

Key Features

• Real-Time Web Scraping: Utilizes a Node.js backend to fetch and parse article content directly from user-provided URLs.
• AI-Powered Analysis: Leverages the Google Gemini API to perform a deep contextual analysis of the scraped text, identifying:
  • Exploits & Unscientific Claims
  • Legitimate Opportunities & Core Technologies
  • Knowledge Gaps & Missing Data
  • Underlying Growth Models
• Interactive UI: A polished, fully responsive front-end with interactive elements like a domain carousel, toast notifications, and dynamic content highlighting.
• Blueprint Generation: Automatically generates a downloadable MVP (Minimum Viable Product) blueprint based on the AI's analysis.
• Serverless Deployment: A modern full-stack architecture deployed on Vercel, using serverless functions for the backend API.

Tech Stack

Category	Technology
Frontend	HTML5, CSS3 (with Flexbox & Grid), JavaScript (ES6+)
Backend	Node.js (adapted for serverless functions)
Database	Supabase (PostgreSQL)
Deployment	Vercel, Git & GitHub (for version control)
AI	Google Gemini API (gemini-1.5-flash)
Libraries	Axios, Cheerio, @google/generative-ai

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Local Setup & Installation

To run this project on your local machine, follow these steps:

1. Clone the repository:

   git clone [https://github.com/rguid31/synaptic-insight-engine.git](https://github.com/rguid31/synaptic-insight-engine.git)
   cd synaptic-insight-engine

2. Install dependencies: This project uses Node.js. The package.json file in the root directory lists all necessary dependencies.

   npm install

3. Set up your Environment Variables: The backend requires a secret API key for the Google Gemini API.

   • Create a new file in the root directory named .env.
   • Add your API key to this file:

     GEMINI_API_KEY=YOUR_SECRET_API_KEY_HERE
     

4. Run the application: This project uses Vercel's CLI for a local development experience that mirrors the production environment.

   vercel dev

   The application will be available at a local URL provided by the command, typically http://localhost:3000.

Author

Ryan Guidry - A self-taught developer passionate about building innovative and user-centric web applications.

• Portfolio: rguidry.dev
• LinkedIn: linkedin.com/in/rmguidry
• GitHub: @rguid31