Powered by augmented reality (AR), On-Device Image Recognition (IR) delivers real-time shelf insights and enables global brands to take immediate corrective actions – even offline
Trax, the technology company revolutionizing retail through data-driven AI, announces the launch of its AR-powered On-Device IR, a new solution for consumer packaged goods (CPG) companies to understand and respond to shelf conditions in real-time. Leveraging the industry’s leading AI-based image recognition platform, On-Device IR collects massive amounts of proprietary data and signals at the shelf, with no interruption from Internet connectivity or obstacles, so brands can make quick and cost-effective decisions about their store-level execution.
“Our vision is to give CPGs immediate, actionable insights into shelf conditions, so they can respond to what’s happening in stores the moment it matters most,” said David Gottlieb, Chief Revenue Officer of Trax. “Brands need clear, real-time visibility into the aisles to address issues quickly and capture sales that might otherwise be lost. By equipping merchandisers, sales representatives and store managers with this solution, we’re providing our global CPG partners with a powerful advantage in retail execution and laying the foundation for even more advanced data solutions in the future.”
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Bringing Augmented Reality to Retail
On-Device IR is built on Trax’s technology, which goes beyond classical image recognition to provide insightful metadata via an augmented reality scan, rather than just a static image, and without the need for WiFi connectivity. On-Device IR also manages the recognition process in 3D space while tracking product location and physical dimensions. When combined with Trax’s existing AI Platform capabilities, this technology delivers:
- Real-time, on-device recognition capabilities without the need for Internet connectivity
- High-accuracy product identification through video stream and depth sensors
- An immersive AR user experience with resilience to interruptions
- Interactive capabilities for field teams
How It Works
- Data Collection: Using Trax’s proprietary mobile app or SDKs, field teams of manufacturers, independent auditors, or store managers and merchandisers identify the product section to capture and scan entire shelves, displays, coolers, or aisles.
- Digitization and Reporting: As field teams scan, the app simultaneously generates a list of critical corrective actions. Representatives can immediately correct or report on any issues, eliminating the need to wait for reports. Importantly, the system captures detailed information about shelf layouts, product placement, and inventory levels—even when internet connectivity is unavailable.
- Delivering Granular Data and Results: All data is centralized in a singular dashboard with easy-to-interpret visualizations and API, enabling users to monitor KPI performance down to the product level. CPG brands can examine store-level details and visualize shelves exactly as they appear in-store, with highlights for both their own SKUs and competitors’ products.
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“We’ve been using On-Device IR in key stores in Portugal for just a few months, and already 100 percent of our users have said that On-Device IR helps them make faster decisions in store,” said Daniela Silva, Head of Customer Marketing & Field at Unilever FIMA. “This is operational efficiency and action in real time. On-Device IR is a simple-to-execute, out-of-the-box solution that works effectively across regions, stores, and teams. The interactive and actionable insights we’ve gleaned help us support our sales forces to be more productive, efficient, and accurate.”
On-Device IR is available as an add-on to existing live projects or as a standalone solution and is already being used by top CPG brands. Global consumer goods manufacturers and retailers already leverage Trax’s in-store execution, store monitoring, and retail analytics solutions to better manage on-shelf availability and optimize merchandising. These solutions are powered by proprietary fine-grained image recognition and machine learning algorithms that turn photos of retail shelves into granular, actionable shelf and store-level insights.
