Iveda Launches Real-Time Zero-Shot AI Detection, Enabling Users to Instantly Build Custom AI Models With a Single Prompt

About this update from Iveda Solutions, Inc.

New capability gives retailers an instant AI model for shoplifting, loss prevention, and more — activated in seconds with a single prompt MESA, Ariz.--(BUSINESS WIRE)-- Iveda® (NASDAQ: IVDA) a global leader in AI-driven video analytics and smart city technologies, today announced a major advancement within its IvedaAI platform: real-time zero-shot AI detection powered by natural language prompts. For the first time, retailers and security operators can type a single word or phrase — "shoplifting," "suspicious behavior," or "graffiti" — and instantly activate a custom AI detection model that begins analyzing live video feeds on the spot, with no model training, data labeling, or deployment lag required. The announcement marks a fundamental shift in how AI-powered video surveillance works. Traditionally, building a detection model required extensive datasets, weeks of training, and significant technical resources. With IvedaAI's new capability, that process is compressed to seconds. The moment a user submits a prompt, a custom AI model is constructed in real time and immediately applied to live or recorded video — turning a natural language instruction into an active, accurate detection engine. "This is the greatest leap forward yet in real-time AI video analytics," said David Ly, CEO and Founder of Iveda. "For years, building an AI detection model meant collecting data, labeling it, and waiting. Now, a retailer can type 'shoplifting' and, in seconds, have a fully functioning AI model running live across their camera network. That's never been possible before — and the accuracy of what we're detecting is remarkable." A Game-Changer for Retail Loss Prevention Shoplifting has long been one of the most difficult behaviors to address through traditional AI surveillance. It manifests in countless forms — concealment in bags, pockets, clothing, or carts — across a wildly diverse range of individuals and store environments. No training dataset could ever completely capture every variation, which made automated detection unreliable at best. IvedaAI's zero-shot approach sidesteps that problem entirely. By leveraging advanced Vision Language Models (VLMs) alongside Iveda's decade of pre-trained object detection models, the platform can interpret the intent and context behind behaviors, not just the static objects in frame. The capability is already being evaluated wi...

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