A leading AI company specializing in vision AI, tasked with developing an innovative retail application for a major global client, faced a significant challenge. Their client, a prominent figure in the retail sector, needed an AI system capable of analyzing images of refrigerators to detect and classify different brands of ice creams by quantity and arrangement.

To train the AI effectively, the company required a vast amount of visual data depicting various refrigerator environments, ice cream brands, and storage conditions. The challenge was compounded by the unique specification of the fridges and the specific brands involved, making it impractical to gather this data through traditional means. The team initially had to travel across Istanbul, capturing images in numerous shops—an approach that proved to be both time-consuming and costly.

DAI.Synth offered a groundbreaking solution through its synthetic data generation platform. We developed a custom synthetic data generation engine specifically tailored for the client's needs, creating diverse fridge environments and ice cream brand arrangements without the need for physical data collection.

Utilizing Apple's object capture API, DAI.Synth produced photorealistic 3D models of the ice creams. These models were seamlessly integrated into our synthetic dataset engine, enabling the rendering of varied scenarios. This included different stacks and orientations of ice creams, as well as randomizing the contents of each compartment, all while ensuring the highest quality and realism in the visual data.

The synthetic data was designed to directly feed into the YOLO AI model, complete with pre-labeled datasets that required no additional processing. The comprehensive data package was delivered in a zip format that allowed the AI team to start training immediately, significantly reducing the lead time and labor involved in preparing the data.

The implementation of DAI.Synth's synthetic data resulted in:

• ✓Efficiency: Elimination of the need for physical data collection and manual labeling, saving weeks of effort and operational costs.
• ✓Accuracy: Enhanced model performance due to high-quality, varied datasets that better prepared the AI system for real-world applications.
• ✓Scalability: Ability to quickly adapt and expand the dataset for future requirements or different projects without additional overhead.

DAI.Synth proved instrumental in transforming the AI development process for retail applications. By leveraging our synthetic data generation platform, the AI company not only met their client's specific needs but also set a new standard in efficient and scalable AI training. This success story exemplifies how DAI.Synth can serve as a critical tool in overcoming the traditional barriers of AI model training across various industries.