In this edition of Localogy’s Local Radar series, we examine location data player Augmodo.
A two-decade-old ambition in local search has been to find out exactly what’s on the shelf at nearby stores. That vision has taken on various forms from text search and Google shopping results to indoor wayfinding that overlays directional arrows in your field of view to guide you through your shopping list.
Sticking with the simpler and more prevalent text search modality, players such as Krillion, Milo (acquired by eBay) and a few others have historically assembled point-of-sale (POS) data to come fairly close to bringing this vision to life. Google Shopping has also come close, but no one has perfected it.
Beyond tapping into the right data sources, the reality of a quickly changing world throws a wrench into things. In other words, consumer-facing real-time information about what’s on the shelf at any given moment remains an unsolved problem. Whoever can make this happen will see a long-awaited payoff.
Camera Ready
With that backdrop, another attempt at this longstanding local search challenge crossed our desks today. Seattle-based Augmodo is using computer vision to map store shelves. Without endorsing or handicapping this approach, we will say that it’s different than anything we’ve seen yet.
Specifically, the company plans to work directly with retailers to equip store personnel with repurposed automobile dash cams that scan and assess store shelves as they go about their business. The thought is that they can collectively and passively capture holistic 3D scans of a given store and its shelves.
That data can then be fed into an API (our speculation) or otherwise provided to local search apps to elevate their capabilities. Beyond local search, this data could be useful in a B2B sense, in terms of retailers’ ordering, logistics, or just-in-time inventory ambitions. In all cases, a large market awaits.
And like many innovative startups, the idea came from a founder’s personal experience. Former Niantic exec Ross Finman spent hours driving hundreds of miles with his wife to find baby formula during the 2022 shortage. Held down by a lack of inventory transparency, he knew there had to be a better way.
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Common Thread
Beyond Augmodo’s inception, other technical requirements flow from Finman’s background. At Niantic, he helped drive the company’s “planet-scale” mapping endeavors. In short, the company builds 3D maps by crowdsourcing the scanning legwork to legions of Pokémon Go players and their phones.
Those 3D maps are critical for the real-world interactivity of its games – not just Pokémon Go but its Lightship platform built for third-party game developers. Like autonomous vehicles need LiDAR scans to “see” the road, immersive geospatial experiences need to know the geometry of their surroundings.
Augmodo is also analogous to Google Live View. As we’ve examined, it uses the smartphone camera to localize a given device. Cross-referencing that camera feed with its Street View image database, Google can know where you’re standing and then serve up 3D directional arrows to get you to a destination.
The common thread in all the above is using the camera as a data ingestion point. And that could be a big break in this context of real-time local product search. Augmodo could face challenges in the immense BD task of forming retailer partnerships at scale, but consider us intrigued by the approach.
