When AI Scientific Tools approached us, they needed a powerful yet intuitive environment for research-grade image labeling.
The goal was to simplify complex facial landmark annotation while maintaining high accuracy, performance, and scalability. Researchers required a seamless workflow that reduced manual effort without compromising dataset integrity or model reliability.
We designed a structured annotation system focused on speed, clarity, and real-time ML validation.
The platform enables precise facial landmark mapping with organized labeling groups and template-based workflows. Built for iPad with Swift 5 and MVC architecture, the interface integrates Google ML Kit for real-time landmark detection and validation. Local storage with Realm and secure Firebase sync ensure fast, reliable data handling across sessions.
Finally, we strengthened the technical backbone to support performance at scale.
The system combines Swift 5 architecture, Firebase cloud sync, Realm local storage, Lottie animations, and Google Vision for accurate facial recognition. Each layer was engineered to maintain responsiveness while handling high-volume image datasets.
With ML-assisted precision and optimized workflows, annotation time was reduced by 40 percent while improving accuracy by 25 percent. Thousands of high-quality datasets were generated to strengthen model training and validation processes.
This project demonstrates how thoughtful UX combined with strong engineering can transform complex AI workflows into efficient, research-ready systems. The result is a labeling platform that feels controlled, scalable, and built for serious AI development.
