The retail store analytics AI workflow enables developers to create end-to-end retail vision AI applications for store analytics using custom dashboards. Developers can leverage and customize the workflow for a variety of analytics, including queue analytics, shopper occupancy, dwell time and trajectory, heat mapping of the customer journey, proximity, line crossing, and regions of interest.
These attributes can be easily modified to include information about specific use cases that are customized to individual stores. Stores can use this information to optimize staffing, enhance store merchandising and layout, and improve customer experience to maximize sales.
Built on cloud-native NVIDIA Metropolis microservices — a low- or no-code way to build AI applications — this AI workflowdelivers pretrained AI models along with the applications needed to jump-start development and deliver actionable store insights.
>Pretrain models for detection and creation of feature embeddings.
>Fully customizable reference application for deploying in production.
>Behavior analytics and learning.
>Guidance on how to train and customize your AI workflow.
>Customizable analytics dashboards to deliver insights including queue occupancy,dwell time, heat maps, and more.
This fully modularized, cloud-native microservice architecture can be deployed, managed, and scaled using Kubernetes and Helm.
