Predictify

Turning Insights into Action: Real-Time Status Analysis Solution For CBRE

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Inspiration

Out inspiration to start this project originated from the recognition of CBRE's standing as a global leader in the commercial real estate sector. We saw an opportunity to create a tool that could further enhance the decision-making process. By developing a status analysis web application, we aim to provide real-time insights and data analytics that can help clients and stakeholders make informed decisions, optimizing the operating cost and readiness.

What it does

Predictify serves as a visual analytics tool for CBRE maintainers and stakeholders to effectively manage and track their assets. Through a user-friendly dashboard, users can monitor asset status and open work orders, facilitating streamlined operations and maintenance management. Moreover, the application offers visualization features for analytics, allowing for a more intuitive and insightful data analysis experience. While the current version of the web application does not incorporate the ML model developed for predicting potential maintenance requirements, it lays the groundwork for future integrations that can further enhance predictive analytics and proactive asset management.

How we built it

Web Application: Frontend Development: We utilized Next.js to build the user interface, ensuring a seamless and responsive user experience. TailwindCSS was employed to style the application, facilitating a clean and modern design.

Visualization Components: To enhance the data visualization capabilities of our application, we integrated the TailAdmin library, which offers a range of components that make it easier to present data in a visually appealing and insightful manner.

Backend Development: For the backend, we chose Supabase, leveraging its powerful PostgreSQL database management system to handle data storage and management. This choice not only facilitates smooth integration with the frontend but also ensures the robust handling of data, thanks to Supabase's easy usage.

Machine Learning: Although not integrated into the web application at this stage, we developed a machine learning model using Python and the sklearn library. This model is designed to predict potential maintenance requirements, paving the way for future enhancements to the application where predictive analytics can play a significant role in asset management.

By combining these technologies, we have created a comprehensive solution that stands to revolutionize asset management and analysis in the commercial real estate sector, aligning with CBRE's innovative approach to the industry.

Challenges we ran into

During the development of the application, we encountered several challenges that tested our adaptability and problem-solving skills.

Library Switch: In the midst of the project, we realized that the initial library we were using was considerably heavy and unresponsive, affecting the overall performance of the application. This necessitated a switch to a more efficient library, which, although time-consuming, was a crucial step to ensure the responsiveness and usability of the application.

Integration with ChatGPT: We envisioned integrating ChatGPT to provide a Generative AI experience that could analyze and interact with the dataset dynamically. While the user interface for this feature was developed, we faced difficulties in connecting it to the API, which prevented us from realizing this innovative aspect of the project within the given timeframe.

Machine Learning Model Integration: Developing a machine learning model to predict potential maintenance requirements was a significant part of our project. However, due to the time constraints, we were unable to integrate this model into the web application. This remains a valuable component that we plan to incorporate in future iterations to enhance the predictive analytics capabilities of the application.

Despite these challenges, our team persevered, adapting to the changing circumstances and making critical decisions to keep the project on track.

Accomplishments that we're proud of

Despite the complexities and challenges encountered during the project, we are immensely proud of several accomplishments.

Developing a Functional MVP: We successfully developed a Minimum Viable Product within the time frame. This MVP showcases the potential of the application to assist in managing and analyzing company's assets more effectively.

Collaborative Effort and Problem-Solving: Throughout the project, our team demonstrated excellent collaboration and problem-solving skills. Despite encountering challenges, we were able to adapt and find solutions, showcasing our resilience and determination to deliver a product.

What we learned

During the course of this hackathon project, we have gleaned several invaluable lessons that have not only enriched our technical prowess but also advanced our problem-solving and collaborative skills.

Time Management and Prioritization: The time constraints of the hackathon taught us the critical importance of time management and prioritization. We learned to focus on developing a functional MVP first, before venturing into more advanced features, ensuring that we had a solid base to showcase at the end of the hackathon.

Collaborative Synergy: Working as a team, we learned the value of collaborative synergy. Each member brought unique skills and perspectives to the table, fostering a rich environment of learning and innovation.

Technical Skill Enhancement: Through hands-on experience, we enhanced our technical skills, particularly in utilizing modern technologies like Next.js, TailwindCSS, and Supabase. This project served as a practical learning ground to deepen our understanding of these technologies and their applications in real-world projects.

What's next for Predictify

The next step involves further development, including the integration of the machine learning model to enhance predictive analytics capabilities, potentially assisting workers in anticipating maintenance needs and other critical aspects of asset management.

We hope that Predictify serves as an inspiration to CBRE, showcasing the potential of leveraging modern technologies to streamline operations and enhance data-driven decision-making, paving the way for further innovations in the industry.

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