Fintech startup evolves into a scalable business selling B2C and B2B solutions

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.

Customizing white-label apps is a complex process, requiring a balance between scalability and flexibility. While the core recommendation engine behind the new digital coaches remains largely the same, our team must tailor each application to meet the specific needs of individual clients, all while continuing to support existing apps.

We automate the app creation workflow. Our DevOps pipeline automatically configures infrastructure, including app code, database, and authentication user pool. This way, we optimize the process of customizing white-label apps, saving time and resources.

We integrated AI to provide personalized, accurate responses based solely on approved internal content. It securely considers each user’s context, tailors answers to their specific needs, and ensures every interaction meets strict privacy and compliance requirements.