End-to-End Managed Delivery of a Vertical Social Media App

Our team designed the architecture and built the Minimum Viable Product (MVP) for an iOS app, and after a while, proceeded with the MVP for an Android application.

Our DevOps practices included implementing backup solutions to safeguard against data loss, utilizing autoscaling for optimal resource management, implementing cost-optimization measures, proactively monitoring errors in production environments, optimizing hardware consumption, and establishing continuous integration and continuous deployment (CI/CD) pipelines based on Infrastructure as Code (IaC) principles. Automated unit, integration, and acceptance tests on the backend maintain high code quality and reliability.

The application is structured upon a microservices architecture, which allows for modularity and scalability. The approach enhances flexibility, ease of maintenance, and performance, ensuring smooth interactions and rapid development while delivering a robust and responsive user experience.

We rigorously adhere to all data security and compliance requirements, including compliance with SSPA and GDPR regulations. Our commitment to these standards ensures the protection of data for both U.S. and European users.

Beginning in the second month of the project, we introduced manual testing into our workflow to ensure comprehensive quality assurance while maintaining cost-effectiveness.

The client’s primary objective was to develop a high-quality Minimum Viable Product (MVP) that was production-ready. Our approach to architecture design placed a strong emphasis on ensuring readiness for full-scale operation and scalability, thus minimizing the need for significant alterations after the full release.

Our mobile app development strategy began with prioritizing iOS, given its significant market popularity. Subsequently, we developed the Android version. Both apps were crafted using native Android and iOS tools. Recon fully harnessed the native capabilities of each platform, including features such as fingerprint recognition, camera usage, Bluetooth connectivity, facial scanning, core ML, and camera functionalities. Additionally, we placed a strong focus on efficient utilization of device storage capacity and optimizing battery usage.

We leveraged Core ML technology to automatically identify and recognize food photos within the user’s library. By utilizing metadata tags, we accessed location information, enabling precise estimation of where these food photos were captured.

Efficient handling of overlapping areas within an optimal square was a key aspect of optimizing location-based searches. Through various optimizations, we ensured precise data retrieval while minimizing unnecessary database resource consumption.

Efficiently displaying user feed updates was a challenge we addressed by introducing a microservice that preprocessed user feeds as new posts were added. This ensured immediate access to prepared feeds when users opened the application, eliminating the need for real-time computation.

User feedback played a crucial role in our development process. In response to user preferences for home-cooked meals, we removed geotags for such content and stripped metadata from photos to protect user privacy.

We took full responsibility for the production rollout, managing app deployment, updates for both iOS and Android, customer support, account creation, application review, and design aesthetics. This end-to-end approach ensured a seamless transition to the production environment.