Give’N Get – AI‑Powered Exchange Platform

Our Solution: We took an iterative, data-driven approach to algorithm development. Initially, we built the matching system based on comprehensive user personas and expected behaviour patterns, incorporating multiple factors including post content, user preferences, geographical relevance, and historical interaction patterns. Once the platform launched, we implemented extensive feedback mechanisms that allowed the algorithm to learn from real-world interactions. We conducted continuous A/B testing of different matching strategies, measuring not just match volume but match quality through user engagement metrics. Working closely with data scientists, we refined the algorithm through multiple iterations, incorporating user feedback directly into the model. This disciplined approach resulted in a 30% improvement in match relevance based on measured user satisfaction and interaction rates. The algorithm became progressively smarter, learning which types of matches generated genuine exchanges versus which were ignored, creating a virtuous cycle of continuous improvement.

Our Solution: We implemented robust real-time synchronisation using WebSockets and Firebase to ensure instant data updates across both web and mobile platforms. This architecture allowed users to see changes immediately, whether they were on a browser or mobile app — when someone accepted an offer or updated their wishlist, all connected clients received updates within milliseconds. We optimised database queries and implemented intelligent caching strategies to handle large volumes of data efficiently without creating bottlenecks. The system was designed to queue and batch non-critical operations whilst prioritising time-sensitive updates like match notifications and transaction status changes. We implemented comprehensive error handling and retry logic to maintain 99.9% accuracy even during network interruptions or peak traffic periods. Load testing and performance monitoring ensured the system remained responsive even during high-activity periods. The result was a platform that felt instantaneous to users — reducing perceived latency by 40% compared to traditional architectures — whilst maintaining data consistency and system stability under load.

Our Solution: We architected a clear separation between business logic (backend) and presentation logic (frontend), with React for web, React Native for mobile, and Node.js providing a unified API layer that both platforms consumed. This ensured the AI matching algorithm, data processing, and transaction management operated identically regardless of which platform initiated the request. We built comprehensive API documentation and established clear contracts between frontend and backend teams, preventing drift between implementations. Shared component libraries where appropriate (using React Native Web principles) reduced duplication whilst allowing platform-specific optimisations where needed. Comprehensive integration testing across both platforms ensured feature parity, whilst platform-specific usability testing allowed us to optimise each experience for its context. This architecture delivered consistency without sacrificing the unique strengths of each platform.