To provide users with a plan on how to approach their property-buying process, we built a recommendation engine that calculates the savings goal based on user data.
Here’s how it works.
When users share their income, preferred property location, and other key info, the data moves to the database. The system processes this data and generates a personalized roadmap for users’ property-buying journey. This roadmap shows things like how much users still need to save, their monthly savings target, when they’ll reach their goal, and more.
As FirstHomeCoach processes large amounts of data, we implemented a serverless architecture using AWS Lambda. It scales automatically based on demand without the need for manual adjustments, saving resources and costs.
To improve scalability and fault tolerance, we structured the system into separate microservices. Plus, it allows us to independently update or scale components without disrupting the whole system.
FirstHomeCoach collects, processes, and stores sensitive user data, so keeping everything secure was critical for us. The database doesn’t have a public gateway, meaning it’s only accessible through services. These services are protected by a Web Application Firewall (WAF), safeguarding user data from unauthorized access and potential security threats. Additionally, we implemented server-side validation and access control to safeguard the system.
To identify potential vulnerabilities, we regularly conduct penetration testing and isolate development and testing environments behind a VPN.
