The client required a vertical social app that was not only highly functional but also cost-effective and capable of handling heavy user traffic within a tight timeframe. Lacking technical expertise on their end, they entrusted Zoolatech with the entire process, beginning with defining functional requirements and culminating in post-release maintenance.
from an idea to end-to-end implementation
Based on non-functional requirements and the expected user loads, we decided on the architecture, tech stack, and the MVP features. Our key goal was to build core functionality while managing costs and minimizing technical debt.
In our initial phase, we conducted limited market research, with a focus on analyzing competitors, similar offerings, platform selection, target demographics, feature requirements, cloud platform options, and technology choices. Our approach prioritized functional requirements, considering factors such as speed, scalability, engineering costs, regional availability, and the dynamics of the engineering job market.
The result of this phase was a comprehensive proposal that encompassed user flows, technology stack recommendations, cloud solutions, staffing plans, team recommendations, pricing details, and a project timeline spanning 2-3 years. This plan also took into consideration potential future integration needs and tools required for upcoming features, as well as long-term cost-efficiency.
Throughout the project, we actively solicited user feedback and incorporated it into subsequent app versions to enhance the user experience.
MVPs for iOS and Android apps:
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.
Best DevOps Practices:
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, 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.
Microservices Architecture:
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.
Data Security: SSPA and GDPR Compliance:
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.
Quality Assurance:
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.
Mobile App Development:
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.
Leveraging Core ML for Food Photo Recognition and Location Estimation:
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.
Optimizing Location-Based Searches:
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.
Streamlining User Feed Updates:
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.
Feedback-Driven Enhancements:
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.
Comprehensive Production Rollout:
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.
