Abstract: This project presents an AI-powered website/code generation platform built with Next.js, React, and Tailwind CSS supported by Node.js, Axions for API communication. The system allows users to generate fully functional websites and code structures simply by providing natural language prompts. Unlike traditional website builders that require manual coding or drag-and-drop interfaces, this solution leverages artificial intelligence to automate code creation, reducing development time and making web development accessible to both technical and non-technical users. The platform provides a modern, responsive interface where users can enter their requirements, receive auto-generated code in real time, and preview the resulting website. It includes modular components such as a navigation bar, hero section, feature highlights, pricing modules, and a footer, making it a scalable starting point for more complex applications. The system also incorporates a mock API for demonstration, which can later be extended to integrate advanced AI backends (e.g., Open AI, Firebase functions, or custom ML models). From a business perspective, the project has strong potential as a Software-as-a-Service (SaaS) application, offering users a quick and cost effective way to build websites. It can evolve into an AI website builder, a developer productivity tool, or an enterprise solution for rapid dashboard creation. Future enhancements include user authentication, template libraries, drag-and-drop editing, collaboration features, and one-click deployment to hosting platforms.

Keywords: AI-powered code generation ,Website generator, Natural language to code,Next.js, React, Tailwind CSSNode.js, backend, API-based code generation, Axios communication, Automated web development, Real-time code preview, Modular UI components, Navigation bar / Hero section / Pricing module, Mock API integration, SaaS platform, Developer productivity tool, AI website builder, Rapid website creation, Template library, Drag-and-drop editor, Oneclick deployment, User authentication, Scalable architecture, Enterprise dashboard generation, Machine learning integration.


Downloads: PDF | DOI: 10.17148/IJARCCE.2025.141261

How to Cite:

[1] Karanam Seshagiri Rao, Abdul Khader, Vishal Prajapati, Santosh Kumar G, S Datta Dharma Sai, "CODE GEN AI," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141261

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