What Standard Cognition’s Big Play Means for Autonomous Retail
If autonomous checkout systems ever go mainstream, it will be because retailers finally figured out how to effectively harness in-store cameras to determine where customers are and what items they’re holding in real-time.
Reaching that goal has proven elusive to AI technology providers thus far, but a San Francisco-based startup called Standard Cognition is hoping that its recent acquisition of Explorer.ai, a mapping and computer vision firm, will be the catalyst that’s necessary to accelerate growth and expand into new retail verticals.
Standard Cognition’s purchase of Explorer.ai comes at a time of massive growth, both for Standard Cognition and the autonomous checkout market as a whole. Standard Cognition is fresh off a $40 million funding round in November. The company has raised more than $51 million to date.
While short-term goals for Standard Cognition include expanding its team and accelerating global expansion, the company’s ultimate success still hinges on its ability to sell big-box retailers on its vision of autonomous checkouts. That part of the equation has proven to be a challenge, in particular because mapping large format stores is more difficult, from a technological standpoint, than smaller shops.
Amazon Go has dominated headlines with its AI-driven, cashier-free stores, but those outlets are typically smaller in size than a typical department store or home improvement chain, like Lowe’s or Home Depot. Standard Cognition’s technology works differently than what’s being used in those stores, as well.
Standard Cognition uses what it considers to be a more streamlined approach to autonomous retail. Cameras identify customers by shape and movement, instead of facial recognition. Shoppers inside stores open mobile apps on their smartphones, allowing in-store cameras to tie them to their payment methods. Shoppers who don’t want to download any new apps to their phones can still shop at participating stores by paying at special kiosk screens, thus overcoming one of the biggest barriers to widespread adoption of autonomous checkout technology.
In order to determine where people are, what’s in the store, and who has what items, Standard Cognition needs to have a semantically meaningful map of each store. The map is necessary to identify where cameras are placed and how they interact with each other. While this type of mapping is relatively easy in a smaller store—like a clothing boutique or a bodega—it becomes exponentially more complex as the store size increases.
“In a small store, mapping is relatively easy. As store size increases, the number of people and number of items also increases,” explains Evan Shiue, head of strategy at Standard Cognition.
If Standard Cognition is going to beat out its competitors—which include startups like Zippin, AiFi, and Trigo Vision—it will need to find a way to make its platform available to retailers of all sizes.
Explorer.ai’s mapping and computer vision platform, which was initially developed for autonomous vehicles, will now be used for in-store mapping. Processes that previously took hours to complete should be down to just minutes. With the acquisition, Standard Cognition becomes better positioned to map larger stores, like department stores and other retail verticals. The company also hopes that having Explorer.ai’s team on board will accelerate its road map by several months.
“Mapping is a continuous process, as we always need to be tracking who has what,” says Shiue. “If we can’t do it quickly—in minutes—we don’t have an accurate picture of who has what.”
Amazon Go, and virtually every other technology provider that’s attempted to bring autonomous checkout solutions to market, has faced the same challenge with larger stores.
“This is a challenge that all autonomous checkout companies, including Amazon Go, have faced,” Shiue says. “Whoever solves it first as the best shot at working with large-format retailers.”
Stephanie Miles is a senior editor at Street Fight.