Aesthetix - AI Powered Interior Design Application

Abstract

Interior design is instrumental to producing an area that is functional, beautiful, and psychologically comfortable, both for residential and professional purposes. The conventional process of interior design relies heavily on expert knowledge, repeated interactions between clients and designers, and manual visualization techniques, which are usually accompanied by inefficiencies and limited accessibility for non-professional users. Moreover, users often face significant problems during the early stage of planning in expressing their design ideas and visualizing concepts. Recent progress in AI, especially within LLMs and computer vision, has empowered systems to reason effectively over and integrate textual and visual information. This opens up new possibilities in automating challenging design reasoning tasks and supplying intelligent personal recommendations. This paper introduces Aesthetix, a multimodal web application utilizing AI assistance for improving and automating interior design planning. The designed solution enables users to upload images of a room and describe their design requirements using natural language input capabilities. Through the utilization of interior design features using multimodal interaction and cloud-based data synchronization, Aesthetix facilitates a complete solution for interior design, which encompasses room designs, color combinations, furniture selection, and purchase assistance. Experimental testing of the solution verifies that Aesthetix possesses faster response rates, improved usability, and support for higher levels of personalization in contrast to digital interior design software.

Introduction

The text presents Aesthetix, an AI-powered, multimodal interior design web application developed to address the accessibility, cost, and usability limitations of traditional interior design services. Interior design plays an important role in enhancing comfort, functionality, and well-being, yet professional design services are often expensive and difficult for users to visualize through technical drawings or verbal explanations, leading to inefficiencies and dissatisfaction.

Existing digital interior design tools rely heavily on fixed templates, rule-based logic, and professional involvement, offering limited automation, poor understanding of user intent, weak real-world image interpretation, and minimal support for iterative refinement. To overcome these challenges, Aesthetix integrates computer vision, natural language processing, and interactive design tools into a single, user-friendly platform.

The proposed system allows users to upload room images and describe their preferences in natural language. Using multimodal AI processing and a large language model, the system analyzes spatial constraints and user intent to generate personalized design recommendations, including layouts, furniture, color schemes, and shopping suggestions. An interactive design canvas enables users to refine designs through drag-and-drop functionality with real-time visual feedback, while cloud synchronization ensures persistent data storage and version control.

Aesthetix follows a scalable client–server architecture with a lightweight frontend, a Node.js backend, and Firebase Firestore for real-time data management. Key modules include user authentication, AI-based personalized suggestions, an AI interior assistant, a design canvas, and a creator community hub that encourages collaboration and inspiration sharing.

Experimental results demonstrate that Aesthetix significantly reduces design time, improves visualization and user satisfaction, and delivers coherent designs aligned with user preferences. While performance depends on the quality of user input and high-resolution image processing may introduce minor latency, the system validates the practicality and effectiveness of AI-driven, multimodal interior design assistance.

Conclusion

Aesthetix is a multimodal AI-based interior design system that automates and personalizes the design process. By combining AI-driven reasoning, cloud synchronization, and an interactive interface, the system reduces time, cost, and dependence on professional designers. Experimental results demonstrate improved usability, faster early-stage design, and enhanced user satisfaction, highlighting Aesthetix as a promising solution for accessible and user-centric interior design.

References

[1] S. Chaillou, AI & Architecture: Towards a New Approach, 2020. [2] T. Brown et al., “Language Models Are Few-Shot Learners,” in Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS), 2020. [3] Y. Huang et al., “Indoor Scene Understanding Using Computer Vision,” IEEE Access, vol. 7, pp. 150326–150337, 2019. [4] J. Li et al., “Deep Learning for Aesthetic Evaluation,” Computer Vision and Image Understanding (CVIU), vol. 206, 2021. [5] Google, “Firebase Documentation.” [Online]. Available: Google Firebase Official Documentation. [6] Groq, “Groq API Documentation.” [Online]. Available: Groq Official API Documentation. [7] D. A. Norman, The Design of Everyday Things, Revised and Expanded ed., New York, NY, USA: Basic Books, 2013.

Copyright

Copyright © 2026 Ms. Uma R, Kishanth A, Naren Karthick M, Nithin Kumar R, Shai Shmithesh M R. 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.