Brokrly AI Virtual Staging Assistant

Inspiration

Virtual staging is an essential part of property marketing for real estate agents and an affordable alternative to real staging. It boosts the conversion from online browsing to offline showing by picking the interests from viewers. At the moment agents spend on average $20-$200 per image for virtual staging and wait for more than 48 hours with multiple edits. We hope to bring the cost to near $0 by creating AI-powered virtual staging within seconds.

What it does

By uploading the room images and telling the AI-assistant the style of the interior design, function of the room and if you want to remove the furniture, the AI-assistant will be able to generate AI-powered virtual staging within seconds.

How we built it

Our project was developed using a modular architecture that integrates various technologies to ensure scalability, performance, and user engagement:

• Backend and Frontend Architecture: The backend was built using .NET Core MVC Web API, which provided a robust and secure foundation for our application. The frontend was developed using Next.js, ensuring a modern, responsive, and user-friendly interface. We utilized AWS for our cloud infrastructure, leveraging its capabilities to deliver a scalable and reliable platform.
• AI and Machine Learning Integration: We incorporated Python-based AI and machine learning solutions, utilizing the OpenAI API, Retrieval-Augmented Generation (RAG), and vector databases to optimize data handling and model performance. Hugging Face was employed for model deployment, while LangChain was used to effectively chain model outputs, enhancing the overall efficiency of our AI pipelines. DreamBooth and Stable Diffusion models were meticulously fine-tuned to cater to specific project requirements, enabling us to achieve high levels of accuracy and customization.
• Data Management: MS SQL served as the primary database for relational data, while vector databases were implemented to manage high-dimensional data, ensuring efficient storage and retrieval for AI model training and inference.

Challenges we ran into

Throughout the development process, we encountered and overcame several challenges:

• Integration Complexity: Merging diverse technologies, such as .NET Core MVC Web API, Next.js, and Python-based AI services, posed significant integration challenges. Ensuring seamless communication between the backend, frontend, and AI components required advanced coordination and engineering.
• Model Fine-Tuning: Fine-tuning AI models like Stable Diffusion and DreamBooth demanded substantial computational resources and precision to achieve an optimal output
• Resource Management: Balancing computational resources during model training and inference, particularly when deploying models like DreamBooth and Stable Diffusion, was challenging. Ensuring that resource-intensive tasks did not degrade overall system performance required careful planning and optimization.
• Performance Optimization: Achieving low-latency, high-performance interactions between the user interface and the AI models, particularly when dealing with complex pipelines such as LangChain, was a significant technical hurdle.

Accomplishments that we're proud of

Despite the challenges, we achieved several notable accomplishments that underscore the success of our project:

• Successful AI Integration: We effectively integrated advanced AI models with our core application, achieving real-time interaction between backend services and AI components. This integration has allowed us to enhance the overall user experience significantly.
• Enhanced User Experience: The use of Next.js and contemporary frontend technologies has enabled us to deliver a responsive, intuitive, and engaging user interface that complements the sophisticated AI-driven backend.
• Robust Error Handling: We developed advanced error handling mechanisms that enhance system reliability and maintain seamless operations even under unexpected conditions. This has significantly reduced downtime and improved user trust in our platform.
• Adaptive Learning Capabilities: By integrating adaptive learning features, our AI models can continuously learn and improve based on new data inputs, making our system smarter and more effective over time. This continuous learning loop is a key differentiator of our platform.

What's next for Brokrly AI Virtual Staging Assistant

We will incorporate this AI-powered virtual staging into our AI-powered copilot for real estate agents, as well as working on the API for virtual staging and open this to developers and brokerage systems to enable them to perform virtual staging in their own IT systems.

Built With

• .net-core-mvc-web-api
• amazon-web-services
• dreambooth
• hugging-face
• langchain
• ms-sql
• next.js
• openai-api
• python
• python-**ai/ml-technologies**:-openai-api
• retrieval-augmented-generation-(rag)
• stable-diffusion-**cloud-services**:-aws-**databases**:-ms-sql
• stablediffusion
• vectordatabase