In the competitive world of real estate rentals, understanding user sentiment and quickly addressing concerns with AI-powered real estate platforms is critical. National Association of Realtors highlights that AI tools like ChatGPT help real estate agents streamline tasks such as lead generation, contract management, and sentiment analysis.
The main goal of this case study is to visualize a concept of a PropTech AI solution that could quickly process large volumes of user comments and provide actionable insights without compromising the user experience.
ORIL designed a backend solution using Nest.js and Node.js to integrate OpenAI’s ChatGPT for conducting comment analysis. The backend analyzes each comment submitted on the platform, extracting key sentiment analysis metrics such as emotion, tone of voice, tags, and inappropriate content. By providing real-time insights, this backend functionality helps rental owners adjust their listings based on user feedback and provides renters with a clear understanding of apartment listings.
ORIL prioritizes an in-depth User Experience (UX) design phase. This involves crafting a clear user flow and interface layout based on the needs of both apartment owners and renters. By mapping out user journeys, our team ensures the platform is intuitive and enhances user satisfaction. The design process is focused on creating a clean and accessible interface that supports seamless interactions and simplifies the rental process. Strong UX design bridges the gap between technology and users, fostering better engagement and reducing friction.
To deliver a seamless user experience, ORIL developed a simple and intuitive user interface using React and Material UI components. The interface allows users to easily submit comments, view analysis results, and quickly access feedback. This user-centered design approach ensures that both apartment owners and renters have easy access to the insights they need to improve communication and renting decisions.
Recognizing the need for secure and reliable data handling, ORIL implemented basic rate-limiting and CORS protection to prevent misuse or excessive API requests. This ensures the system can handle large amounts of feedback without performance degradation.
Objective: Understanding the landlord’s need to access renter sentiment analysis quickly for better communication and faster decision-making.
Approach: This involved setting up workflows that allowed landlords to assess renter sentiment in various chat states.
Chat Functionality: The user (landlord) can start a conversation with a renter, send or receive messages, and analyze them in real-time.
Automated Analysis: When a message is received, it can be analyzed instantly for keywords, emotional tone, sentiment, and other metrics such as feedback percentages, emotional evaluations, and intent recognition.
Detailed Analysis View: A more comprehensive analysis view is available, which includes trend analysis, emotional highlights, and key points for further decision-making.
UI Design with Material UI: The PoC shows UI screens with clear chat windows, analysis sections, and graphical representations of sentiment analysis.
User-friendly Chat Window: Integration of analysis features that display real-time sentiment, key metrics, and graphical data representations.
Cross-Browser Compatibility: The screenshots depict different layouts for Safari and Chrome, indicating cross-browser optimization in the UI.
API Integration with OpenAI: The backend likely includes endpoints developed in Nest.js to interact with OpenAI’s ChatGPT API. These endpoints fetch data on the sentiment and emotion metrics from user comments and feed them back into the frontend.
Security Measures: Basic security setups such as rate-limiting and CORS were implemented to prevent abuse of the API.
Decision-making Process: The flow diagram created by ORIL’s Development and Design Team visually illustrates how users interact with the platform. For example, it shows decision points like whether to write a message, view analysis, or explore further detailed analysis options.
The process involves several testing loops with real user data to refine the AI’s accuracy in analyzing the sentiment and improving feedback delivery based on landlord needs.
Ensuring ChatGPT consistently provides accurate sentiment analysis across different user contexts and languages.
Managing API calls without exceeding usage limits imposed by OpenAI.
Handling user data securely, especially personal chats and feedback, to comply with privacy regulations.
Making sure the UI delivers analysis results quickly without overwhelming the user with excessive data.
It is a real life example of a possible AI utilization in Proptech and Real Estate Industry by using OpenAI API. The potential benefits of this PropTech Solution are:
Insights made easier: increased lead generation & contract management.
Informed decision-making: gaining a better understanding of user feedback and behavior for property managers with real-time insights;
Increased Customer satisfaction: accelerate the feedback loop to improve user satisfaction;
Improved communications: bridge communication gaps between apartment owners and renters, reducing misunderstandings, improving the operations & rent cycles.
This project demonstrates how RealTech companies can harness the power of AI to enhance their platforms and services with ORIL. By automating the comment analysis process, the solution improves communication between apartment owners and renters, speeds up decision-making, and enhances customer satisfaction. The AI-powered insights offer a clearer understanding of user behavior, allowing both parties to make more informed decisions in real-time.
ORIL continues to drive innovation by developing cutting-edge SaaS solutions that integrate AI, providing companies with the tools they need to stay ahead of market demands.
Through the successful integration of advanced comment analysis using ChatGPT for real estate agents, ORIL can help clients to transform their property management platforms, enabling faster and more effective feedback management.
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