This London Startup Just Raised $9M to Stop Retail Shrink at the Source

Jan de Vries · 2026-08-10

Retailers lose billions of dollars every year to theft, fraud, and operational errors. It's a problem that has plagued physical stores for decades, and most solutions have been clunky, expensive, or require sending sensitive data to the cloud. But what if the answer wasn't in the cloud at all? What if the answer was right on the sales floor? That's the bet Edgify is making. The London-based edge AI infrastructure company just announced a $9 million Series A+ round, bringing its total funding to $25 million. The round was backed by Rank Ventures and Mangrove Capital Partners. The fresh capital will accelerate the rollout of their platform, which is designed to combat in-store loss and transform how physical retail uses artificial intelligence. ### The Core Problem: Retail Shrink Retail shrink is the industry term for inventory loss. It happens through shoplifting, employee theft, administrative errors, and supplier fraud. For grocers and convenience stores, the margins are already razor-thin. Losing even a small percentage of inventory to shrink can wipe out profits for the entire quarter. Traditional loss prevention methods have relied on CCTV, security tags, and occasional audits. These are reactive measures. They catch some incidents, but they don't prevent them in real time. They also don't scale well across hundreds or thousands of store locations. Edgify's approach is fundamentally different. Instead of watching from a central command center, it puts the intelligence directly on the devices already in the store. ### How Edgify's Edge AI Works Think about a typical grocery store. It's full of smart devices: self-checkout kiosks, overhead cameras, digital scales, and point-of-sale systems. Each of these devices collects data. In most systems, that data gets sent to a central server for processing. That creates latency, costs money, and raises privacy concerns. Edgify flips that model. Their platform connects, orchestrates, and trains AI models directly across those in-store edge devices. The hardware learns locally. It shares insights across the network without ever sending raw data to the cloud. This means decisions happen in milliseconds, not seconds. The company's technology can recognize produce at the self-checkout, identify scan avoidance, spot product-switching, and detect cart-based loss. It assists shoppers and staff in real time, preventing loss before it happens rather than just documenting it after the fact. > "Intelligence should live where data is created, and devices should learn as one," says Nadav Israel, CEO and Co-founder of Edgify. "A store is a fleet of machines that can see, decide and learn together, without a single byte leaving the building." ### Why This Approach Beats the Cloud Most AI systems rely on heavy infrastructure, constant connectivity, and centralized control. That doesn't scale in the real world. Competitors often require expensive servers in dedicated spaces that take months to install. That's impractical for a standard grocery store with tight budgets and limited backroom space. Edgify's edge-based approach reduces cloud infrastructure costs dramatically. It lowers latency because data doesn't have to travel anywhere. And perhaps most importantly, it ensures customer and operational data never leaves the store. That's a huge selling point in an era of increasing data privacy regulation. The platform is also hardware-agnostic. It runs across existing in-store equipment from numerous manufacturers, including major partners like Zebra Technologies and Bizerba. Retailers don't need to rip out their current systems to benefit. They can leverage what they already have. ### Beyond Grocery: The Bigger Vision While loss prevention is the immediate use case, Edgify's leadership sees this as just the beginning. The operational friction they've solved in grocery stores can be replicated in any industry where fleets of devices meet the physical world. The company is already expanding into quick-service restaurants (QSR), distribution centers, and apparel. Mitchell Goldman, COO at Edgify, explains that retail is the ultimate testing ground because it has more smart devices per site than almost any other industry. "Loss prevention is where the value shows up first," Goldman says. "But the real breakthrough is the underlying platform: translating isolated in-store hardware into a unified system for future AI applications." Over the next few months, Edgify will expand its platform to manage the complete lifecycle of AI models across physical retail. That means not just training and deployment, but ongoing monitoring, updating, and optimization. The goal is to turn millions of isolated machines into self-learning, real-time networks that operate independently of the cloud. ### What This Means for Retailers For retailers in the United States and Europe, this funding signals that edge AI is moving from experimental to essential. Edgify is already working with leading grocery retailers across both continents. The $25 million in total funding gives them the runway to scale aggressively. The practical takeaway is simple. Retailers no longer have to choose between cost, latency, and privacy when implementing AI. Edgify's platform delivers on all three fronts. It runs on existing hardware, makes decisions in real time, and keeps sensitive data on-site. As shrink continues to pressure margins, solutions like this will become increasingly critical. The companies that adopt edge AI early will have a significant competitive advantage. The ones that wait may find themselves stuck with outdated, expensive, and less effective centralized systems. Edgify's journey is a reminder that sometimes the smartest technology isn't the one that's the most centralized. Sometimes, it's the one that's closest to the action.