Loading Please Wait...Loading Please Wait...TrueSpaces is a dynamic mobile application that simplifies the apartment rental process in the US market. It connects tenants and listers, allowing listers to list their properties and tenants to search, filter, and apply for apartments. Features include property search with filters (location, rent, rooms, etc.), apartment videos, property tours, background checks, document sharing, contact signatures, and secure payments for deposits and rent.
1. Handling Large Listings:
The application needed to handle thousands of apartment listings displayed on a map, causing significant system lag.
2. Managing Video Playback and Memory:
Efficient video management was crucial for smooth playback, without overwhelming the system’s memory.
3. Real-Time Data Integration:
Incorporating real-world, up-to-date apartment data posed a challenge in providing accurate, live listings to users.
4. AI Chat bot:
The chat feature needed to filter and provide accurate apartment listings based on user input. Implementing real-time chat functionality that could query the database and return the best results was a challenge via AI.
1. Optimized Listings with Pagination:
We restricted the number of apartment listings displayed on the map (100 to 250 per view) and implemented pagination. This, along with reusing existing views rather than generating new views each time, significantly improved performance.
2. Efficient Video Caching:
To manage video playback, we optimized the video loading process by caching the first few seconds of the video for each apartment. If a user selects a video to view, the other videos in the listing would be pre-cached for smoother playback.
3. Real-Time Data with Third-Party SDKs:
To overcome the challenge of providing accurate data, we integrated third-party SDKs like IDX, which provided real-time apartment listing updates, ensuring that the app delivered the most current and relevant data to users.
4. AI Integration:
We integrated an Open API-powered chatbot that enabled tenants to interact and filter listings based on their preferences (location, price, number of rooms, etc.). This solution allowed users to get the best matches directly from the system, making the experience more personalized and efficient. Integrating the Open API was a significant learning experience that enhanced the app’s functionality.
Implementation:
The development was carried out in multiple sprints, with each sprint focusing on a core functionality and specific screens. Initially, the client provided the design and basic instructions. We followed these guidelines while continuously updating the client on progress, gathering feedback, and making necessary adjustments. After completing development and internal testing for each sprint, the QA team conducted thorough testing before sending the build to the client. Feedback from the client was reviewed, and improvements were incorporated into the next sprint, ensuring constant refinement of the app.
The solution significantly improved the application’s performance and user experience. Handling large data sets and integrating real-time information resulted in smoother operations, faster load times, and a more engaging user experience. The introduction of the chatbot API further personalized the user experience, making it easier for tenants to find apartments that fit their needs. The client was highly satisfied with the outcome, particularly appreciating the seamless communication, collaboration, and the successful resolution of performance issues.
1. Managing large datasets both on the backend and client side is crucial for app performance. Implementing effective data management strategies like pagination and caching helped streamline the app.
2. Leveraging Redux effectively for state management contributed to smoother user interactions and data consistency.
3. Integrating third-party services, like IDX, added real-time data capabilities and enhanced user experience, making the app more engaging and reliable.
4. API chat integration was a great learning experience that added a personalized touch to the user journey, allowing tenants to easily filter listings based on their preferences.
Front End: ReactNative ,Typescript ,NextJS
Backend: NestJS ,MySQL , Ubuntu
Third party tools: Open AI , AWS Services , Plaid , Equifax API , Stripe , IDX Property scraper , Paywall Subtraction , Nearby subway station
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