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Solutions: AI Workflows
Build scalable solutions to identify and stop shrinkage at the point of sale.
Retail product shrinkage is a $142B per year challenge, with over 65% of shrinkage—and growing—due to theft. With NVIDIA’s retail loss prevention AI workflow, you can quickly build and deploy applications designed to prevent theft. Leveraging the NVIDIA Metropolis microservices and models pretrained on hundreds of the most-frequently stolen goods, this AI workflow provides few-shot, active learning to quickly scale to recognize hundreds of thousands of products, and provide intelligent alerts with actionable information.
Built on cloud-native NVIDIA Metropolis microservices—a low-or no-code way to build AI applications—this AI workflow delivers pretrained AI models along with the applications needed to jump-start development, rapidly index hundreds of thousands of store products for cross-camera and barcode-scan identification, and detect ticket switching and theft.
The AI models in this workflow are pretrained to recognize hundreds of products most frequently lost to theft—including meat, alcohol, and laundry detergent—and to identify them in varying sizes and shapes. With synthetic data generation from NVIDIA Omniverse, retailers and independent software vendors can customize and further train the models to cover hundreds of thousands of store products. The workflow is based on a state-of-the-art, few-shot learning technique developed by NVIDIA Research which, combined with active learning, identifies and captures any new products scanned by customers and sales associates during checkout to ultimately improve model accuracy.
Please complete this short application for early access to the retail loss prevention AI workflow.
Please note that you must be a registered NVIDIA Developer to join the program. Log in using your organizational email address. We cannot accept applications from accounts using Gmail, Yahoo, QQ, or other personal email addresses.
The AI models in this workflow are pretrained to recognize hundreds of products most frequently lost to theft—including meat, alcohol, and laundry detergent—and to identify them in varying sizes and shapes.
The workflow features a state-of-the-art variation of few-shot learning designed to adapt continuously with limited new product data using object characterization and self-supervised learning algorithms. This unique method of active learning identifies and captures new products and packaging changes scanned during checkout for future recognition through similarity search.
Delivered through cloud native microservices, this AI workflow allows users to jump-start development, easily customize for their solution, rapidly index hundreds of thousands of store products for cross-camera and barcode-scan identification, and enable scalable production deployment.
AI workflows accelerate the path to AI outcomes. This AI workflow provides a reference for developers to rapidly start creating a flexible and scalable loss prevention solution.
Best-in-class AI software streamlines development and deployment of AI solutions.
Frameworks and containers are performance-tuned and tested for NVIDIA GPUs.
Prepackaged, customizable reference applications with cloud-native, deployable packaging.
Scale business-critical AI projects to production across stores.
Read more about how NVIDIA is helping the retail industry tackle its $100 billion annual shrinkage problem.
Learn more about how the retail AI workflows address highly complex application development challenges and provide the initial building blocks necessary for an effective solution.
Want to get started? Apply for early access and get a jump start on creating a flexible and scalable loss prevention AI solution.