AUSTIN, Texas, June 30, 2026 (GLOBE NEWSWIRE) -- Pensa Systems, the industry’s first scalable Vision AI solution for shelf planning and in-store execution, today announced two newly issued, foundational AI patents that address some of the biggest barriers to deploying AI, at scale, in physical retail. Retail stores present a difficult challenge for any AI system. A typical grocery or general merchandising store carries 30,000-50,000 discrete products, many of them visually similar on the shelf. Shelf assortments and layouts are historically planned at headquarters by hand but rarely match what is actually in the aisle. Today, retail associates and brand representatives still maintain and audit shelves largely by hand and by barcode-scanning of individual items one at a time. This activity is tedious and error-prone, but necessary to check for stockouts, misplaced items and to ensure merchandising, promotional displays and pricing are set properly. Early computer vision and AI approaches struggled to keep up with the constant packaging changes and product turnover, producing high inaccuracy rates and the confident-but-wrong outputs now widely known as AI “hallucinations” within Large Language Models (LLMs), and in earlier digital image recognition technologies. The common workaround has been to quietly put remote workers behind the scenes, often referred to as Human in the Loop (HITL), to correct the AI before its data or actions are released. That approach is expensive, still inaccurate, and causes crucial lags precisely where immediate, trustworthy AI would have its largest impact: in the store, during execution. “AI that hallucinates isn’t just wrong, it destroys the trust of those who depend on it,” said Richard Schwartz, President and CEO of Pensa Systems. “Retail is a complex environment. Getting AI right, at scale and exactly when and where it is needed is not optional. The foundational work signaled by these patents is