Automated Website Creation Platform for Non-Technical Entrepreneurs

Abstract

The increasing demand for digital presence among businesses and entrepreneurs has created a need for simple and accessible website development solutions, especially for non-technical users who lack programming knowledge and design experience. Traditional website development methods require expertise in coding languages, frameworks, and UI/UX design, making the process time-consuming and complex for beginners. To address these challenges, this project proposes an Automated Website Creation Platform for Non-Technical Entrepreneurs that leverages artificial intelligence to automatically generate fully functional and responsive websites based on user inputs. The proposed system utilizes artificial intelligence models integrated with modern web technologies to generate website layouts, design structures, and content based on user prompts and template selections. Users can customize website elements such as text, images, layout, and themes in real-time through a user-friendly interface without requiring technical knowledge. The platform reduces website development time, improves accessibility, and enables entrepreneurs to establish their digital presence quickly and efficiently. The system also ensures responsive design compatibility for multiple devices, enhancing user experience and usability.

Introduction

The text describes an AI-powered automated website creation platform designed to help non-technical users build professional websites without coding knowledge. Traditional web development requires skills in HTML, CSS, JavaScript, and backend frameworks, which makes it difficult and costly for small business owners to create websites. Although drag-and-drop website builders and CMS tools simplified the process, they still require manual effort and design understanding.

The proposed system uses Artificial Intelligence and Natural Language Processing (NLP) to generate complete websites from simple user text prompts. Users specify their website type (such as business, blog, or portfolio) along with preferences like layout, colors, and page structure. The system then analyzes these inputs, extracts key requirements, and automatically generates website content, layout, navigation structure, and design elements such as images, fonts, and themes.

After generation, users can customize the website by editing text, changing visuals, adjusting colors, and rearranging layouts using simple tools. A live preview feature allows users to view the website across different devices (desktop, tablet, mobile) before deployment to ensure responsiveness.

Overall, the methodology integrates user input collection, NLP-based prompt analysis, automated content and layout generation, customization, and real-time preview into a single streamlined system. The goal is to make website development faster, cheaper, and accessible to non-technical entrepreneurs by removing the need for programming skills.

Conclusion

The testing and evaluation of the Automated Website Creation Platform for Non-Technical Entrepreneurs demonstrated that the system can successfully generate fully functional and responsive websites based on user input prompts. The platform was able to process user requirements using Natural Language Processing techniques and automatically generate website layout, content, navigation structure, and design elements without requiring manual coding. The real-time preview and customization features allowed users to modify website content and design easily, improving usability and user experience.The system also successfully converted the generated website into functional code using web technologies such as HTML, CSS, and JavaScript, and allowed users to export and deploy their websites through local hosting, cloud platforms, and GitHub integration. The implementation results showed that the system significantly reduced website development time, minimized technical barriers, and provided an accessible solution for non-technical users to create professional websites. Although some challenges such as prompt interpretation accuracy, responsive design rendering, and code generation optimization were encountered during development, continuous testing and system improvements helped to enhance the overall performance and reliability of the platform.In conclusion, the proposed Automated Website Creation Platform provides an efficient, user-friendly, and time-saving solution for website development. The system helps non-technical entrepreneurs create and deploy websites easily, reducing dependency on professional developers and lowering development costs. The platform also provides a strong foundation for future improvements such as advanced AI design suggestions, automatic SEO optimization, and integration with e-commerce and digital marketing tools.

References

[1] J.-K. Lee, Y. Kim, E. Shin, S. Choo, and S. H. Cha, “An AI-assisted approach for creating and archiving interior design references using 360-degree panoramic images,” Architectural Science Review, 2024. [2] A. Varol, N. H. Motlagh, M. Leino, S. Tarkoma, and J. Virkki, “AI-driven smart spaces: A survey on automated and personalized environments,” arXiv, 2024. [3] R. A. Patil, V. Wankhede, and B. Patil, “Comparative study of generative AI models for interior design,” International Journal of Research in Applied Science and Engineering Technology (IJRASET), 2024. [4] K. Zhou and T. Wang, “AI-driven diffusion models for interior design,” Scientific Reports, 2023. [5] Y. Yang, J. Wang, T. Geng, W. Qiang, C. Zheng, and F. Sun, “DiffDesign: A controllable diffusion model for interior design generation,” arXiv, 2024. [6] Y. Liu and H. Wang, “Mental-Gen: AI-driven brain-computer interface for interior space generative design,” arXiv, 2024. [7] H. Zhang et al., “Interactive interior design recommendation using multimodal reinforcement learning,” arXiv, 2023. [8] “AI-based interior designing application,” International Journal of Innovative Research in Engineering, 2024. [9] “AI-driven interior design innovations,” Journal of Interior Design Research, 2024. [10] “Leveraging machine learning for personalized interior space design,” International Journal of AI in Design, 2024.

Copyright

Copyright © 2026 Daniel Samraj I, Dharun Kumar V, Gnana Vasanth G, Prof. Euodial . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.