Numina

TraxSales is a retail traffic intelligence and sales accountability system designed to help businesses accurately measure customer activity and improve performance without increasing foot traffic. It combines a camera-based people counting system with advanced reporting tools, capturing a time-stamped photo of every person entering a store to create a verifiable record of each sales opportunity. It allows managers to review and refine this data by removing non-customer entries such as employees or delivery personnel, resulting in near 100% accurate traffic counts that go beyond traditional estimation-based counters. It integrates this verified traffic data into a comprehensive analytics environment, where businesses can track key metrics such as conversion rates, revenue per guest, staffing efficiency, and long-term performance trends. It provides detailed reports on hourly, daily, and historical traffic patterns, enabling companies to optimize staffing.

Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases. With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required.