RADAR has raised a massive $170 million Series B funding round. The fresh cash injection firmly drives the startup, based in New York City, into the exalted realm of unicorn companies, with the startup having a post-money valuation of $1 billion.
The round was co-led by Gideon Strategic Partners and Nimble Partners with additional follow-on investor support from Align Ventures. The financing demonstrates considerable venture capital belief in the area of physical brick-and-mortar technology, an industry segment often riddled with operational shortcomings.
In addition to hitting the cash milestone, RADAR has expanded its senior team by hiring Abi Viswanathan as Chief Financial Officer (CFO). Viswanathan has extensive scale-up experience; he helped grow autonomous vehicles firm Nuro’s financial team to facilitate its exit at more than $8.6 billion and was an original member of Uber’s strategy-focused finance team.
Erasing the Brick-and-Mortar Blind Spot
While online shopping analytics currently predominate, physical commerce is still the overwhelming majority of retail transactions worldwide. Yet, store managers historically operate with delayed or highly inaccurate inventory sheets, often relying on manual midnight stock counts that are outdated the moment the doors open the next morning.
RADAR solves this foundational data disparity by merging proprietary, low-profile ceiling hardware with advanced computer vision and Radio Frequency Identification (RFID) analytics. Rather than requiring employees to manually scan barcode tags or point handheld devices at shelves, RADAR’s autonomous system constantly inventories the entire store footprint in real time.
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The platform boasts a near-perfect 99% item-level inventory accuracy metric. More importantly, the sensors don’t simply detect an item in the building, but they record localised spatial coordinates, so that if a shopper picks up a small black dress in aisle three, walks to the fitting rooms and deposits it on a rack in aisle seven, the RADAR sensor instantly detects the change.
“In 2026, operating without real-time intelligence in physical retail means choosing to leave billions of dollars on the table. RADAR is changing that,” said Spencer Hewett, Founder and CEO of RADAR. “Today, we’re empowering retailers to run stores with the same precision as e-commerce. This round signals market conviction in the scale of the opportunity.”
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Validated on the Retail Floor
RADAR’s path to unicorn status has been paved by intense corporate pilot validation. Retail titan American Eagle Outfitters was the first major brand to quietly roll out RADAR’s ceiling tracking technology at scale. Backed heavily by American Eagle’s CEO, Jay Schottenstein, who is also a principal investor in the startup, the tracking platform has already expanded its operational reach across more than 1,400 physical retail locations.
The practical return on investment (ROI) for these brick-and-mortar networks is instantaneous. By maintaining a perfectly synchronised digital twin of the physical inventory, the software drastically minimises out-of-stock friction points where a customer leaves a store because they can’t locate a specific size or colour. Furthermore, it completely optimises in-store fulfilment pipelines for “buy online, pick up in-store” (BOPIS) services, letting associates know exactly which shelf to walk to.
“The physical world has long been a blind spot in an otherwise data-driven economy,” noted Erik Oros, Chief Investment Officer at Gideon Capital. “RADAR is closing that gap. Starting with retail, the company is delivering clear, measurable ROI today while building a proprietary data advantage that strengthens with every deployment.”
The Capital Allocation Strategy
The fresh $170 million war chest is intended for aggressive hardware manufacturing scalability and international corporate distribution. With physical footprints already locked down in major domestic apparel brands, RADAR intends to expand into complex logistics settings, big-box department ecosystems, and international geographic markets.
Furthermore, the engineering division will focus heavily on deploying predictive AI layers. By analysing real-time patterns of how clothes move from racks to fitting rooms, the platform will soon be able to autonomously feed customer preference insights directly back into supply chain and manufacturing engines, fundamentally revolutionising the retail lifecycle.
