On a busy Saturday, a shopper in an apparel store finds the right jacket but not the right size. The associate checks the store system. It says one medium is available. But the jacket isn’t on the rack. It may be in a fitting room, folded on the wrong table, sitting in the stockroom or gone because of theft that was never reflected in the inventory file. To the shopper, it’s out of stock. To the store manager, it’s a missed sale. To headquarters, it may still look like inventory.
That is the gap Spencer Hewett, founder and CEO of RADAR, has spent years trying to close. “I chose to pivot to inventory because that’s a product that you could generate a huge amount of value for the retailers out of the gate,” Hewett told PYMNTS CEO Karen Webster, explaining why RADAR moved from its original idea of autonomous checkout toward inventory intelligence.
The timing is unusually favorable. RADAR recently raised $170 million in Series B funding, co-led by Gideon Strategic Partners and Nimble Partners with participation from Align Ventures, bringing the company’s valuation to $1 billion.
The company’s pitch is direct: Physical stores have long lacked the data layer that eCommerce takes for granted. RADAR says it is bringing real-time precision to the 80% of commerce that still happens in stores, with 99% item-level inventory accuracy and continuous visibility into where products are and whether they are available. Deployed in more than 1,400 stores, including American Eagle and Old Navy, RADAR is trying to turn the store into a living system of record at the same moment retailers are under pressure to run leaner, fulfill online orders from stores and use artificial intelligence in practical ways.
The Simple Truth About Data
The business model is built around a simple premise: AI is only as good as the data it can use. Retailers have no shortage of AI pilots, but many still depend on manual counts, outdated inventory files and store systems that cannot say exactly where an item is. RADAR’s vertical integration matters here. The company supplies proprietary overhead sensors, software and analytics as one system rather than as a loose set of tools. Ceiling-mounted sensors read tagged items across the sales floor, stockroom and fitting rooms, capturing a full inventory snapshot every eight seconds and translating raw signals into tasks: restock this item, route this order from that store, flag this missing product, fix this misplaced display.
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RADAR+ extends that foundation from “What do we have?” to “What is happening?” The AI analytics platform is designed to show retailers SKU-level behavior: which items customers pick up, which go into fitting rooms, which convert to sales and which sit in the wrong place too long. It also recommends replenishment priorities, alerts staff when a product is out of place and gives corporate teams frequent updates on dwell time, on-floor availability and recovery time. In other words, it gives the store some of the feedback loops that websites have always had.
The near-term question is what retailers value most: better inventory management or loss prevention. Hewett’s answer is that the two are closely linked. Theft matters not only because the product is gone, but because the system may still believe it is available.
“So you might have a stock out for an entire year,” he told Webster, describing how stolen goods can remain as phantom inventory until the next physical count. But when retailers buy RADAR, he said, the clearest return is usually out-of-stock reduction. “Once you predict and prevent those out-of-stocks, you just sell more product and you sell it at a higher margin,” he said. The first use case is availability; the later uses include assortment, labor, fulfillment and merchandising.
Asking Better Questions
That makes RADAR an AI story, but not in the abstract sense. Hewett described RADAR+ as a way of displaying information that would otherwise require people to monitor a store “24/7.” Online, he said, this is easy because a company can build software logs. “We are now doing that in the store.” Once those logs exist, retailers can ask better questions. How often was this sweater picked up? What did it go into the fitting room with? What did it beat or lose to? Which product is strong but sitting in a weak location? The next step is prescriptive: not just showing what happened, but recommending where to place items to increase conversion.
Hewett’s longer view is more ambitious. In three years, he expects stores to become more contextual. Recommendations could change based on what is actually in stock. Sales associates could have tools that help them advise shoppers in the aisle. Returns could become more automated. Checkout could begin to fade into the background.
He also sees a future in which the wall between shopping and the physical world becomes thinner: If augmented reality takes hold, the world itself could become more shoppable.
Webster ended the conversation by raising the counterpoint that every retailer should keep in mind. Much of physical shopping is about serendipity. AI is often directed: Find me a blue blazer, and it finds a blue blazer. But stores also help people discover what they didn’t know they wanted until they saw it. She suggested RADAR could create “a more efficient layer of serendipity.”
Hewett’s answer was simple: “Absolutely.” He noted the renewed interest in physical-world businesses and the fact that younger consumers are still showing up in stores and at the movies. The point isn’t to make stores feel like websites. It’s to give retailers the accuracy of online commerce without stripping away the surprise, touch and discovery that make physical retail worth visiting.
For more from the video interview:
Hewett’s background: He discusses selling designer bags on eBay as a teenager, interning at eBay and how that early exposure to eCommerce shaped his question about why physical stores had not progressed as quickly as online stores. Jay Schottenstein as investor: Webster asks about Schottenstein as both a seasoned retailer and an investor. Hewett says Schottenstein pushed RADAR to keep thinking about consumer-facing uses and the value of a single platform across the store. Why RADAR started: Hewett explains that he originally wanted to solve checkout lines, then pivoted after learning how much value retailers could gain from improving in-stock rates and realizing inventory intelligence could create immediate value.
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