Florida startup Fit:Match chose Houston for its first location of its AI-enabled retail store. Photo via shopfitmatch.com

In November, on the first floor of Friendswood's Baybrook Mall, wedged between the Abercrombie & Fitch and the Apple Store, a small studio popped up. At the window, a bubblegum assortment of balloons replaced the usual spruced-up manakin, and the shop is sparse for racks of clothing.

That's because the Fit:Match studio isn't really trying to sell clothes — it's trying to help you buy them online. By fusing artificial intelligence with retail shopping, Fit:Match makes ordering clothes online more trustworthy. The writing on the walls promised to revolutionize the way that people could: "Shop what fits. Not what doesn't," reads a neon sign. The tech might not only reduce long waits for the dressing room — it could abolish it altogether.

"You never have to try on clothes again," says Haniff Brown, founder of the Florida-native startup.

The store does have a fitting room, but Brown says it's not really for trying on clothes — it's for preparing to "get fitched," the process through which the imaging tech measures a customer's body.

It's fitting that the pop-up sits next to the iPhone giant. Fit:Match uses the same 3D imaging tech as Apple's FaceID, Brown says, which blasts infrared light at thousands of dots at a user's face. Where the light bounces off, the AI technology images the person's face. The sensors at the Fit:Match studio in Baybrook Mall expand this to the rest of the body. In 10 seconds, the AI sensor lets people sketches a customer's shape through 150 measurements.

Those measurements become indicators of how well a piece of clothing will fit the wearer. In the initial phase of the project, Brown's team fitched thousands of women — wanting to keep things neat, the company hasn't ventured into men's fashion yet — and compared the scores of the AI's algorithm with how the women scored their own clothes.

Now, once a customer has been fitched at the Baybrook studio, she can log online through an app or the company site and sift through thousands of clothes that will likely fit her. Each clothing item — mostly smaller brands that range from eclectic pieces and dresses to athleisure right now, Brown says, although he's already working to partner with better-known labels — is rated with a percentage of how well it's likely to fit the individual customer, based on her measurements and on how snug or loose she likes her wear. From the array of brands, she'll get specific matches — clothes that have a 90 percent chance or higher of fitting — that might look completely different from a friend's. Over time, the app will also update her on the latest matches.

"You're going to have this personalized wallet," Brown says, adding that this will also decrease a store's rate of return. "You will see a completely truncated assortment of clothes that are meant to fit you."

The Baybrook Mall hosts Fit:Match's first location. Brown says he chose the Houston area for its size and demographics, calling it a "hotbed to test new ideas, to get traction, to get customer feedback," and is even considering expanding to the Woodlands Mall and other places around Texas, too. It's also not far from the Austin-based Capital Factory, which brought Fit:Match under its wing late last year to help the startup raise $5 million.

In the meantime, the five-member management team at Fit:Match is focused on getting more Houstonians fitched. In the first month of operations, the studio measured more than 1,200 mallgoers, and Brown says the company could fitch a quarter million in the next two or three years.

"We think that the opportunity here is immense," Brown says.

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Houston food giant Sysco to acquire competitor in $29 billion deal

Mergers & Acquisitions

Sysco, the nation's largest food distributor, will acquire supplier Restaurant Depot in a deal worth more than $29 billion.

The acquisition would create a closer link between Sysco and its customers that right now turn to Restaurant Depot for supplies needed quickly in an industry segment known as “cash-and-carry wholesale.”

Sysco, based in Houston, serves more than 700,000 restaurants, hospitals, schools, and hotels, supplying them with everything from butter and eggs to napkins. Those goods are typically acquired ahead of time based on how much traffic that restaurants typically see.

Restaurant Depot offers memberships to mom-and-pop restaurants and other businesses, giving them access to warehouses stocked with supplies for when they run short of what they've purchased from suppliers like Sysco.

It is a fast growing and high-margin segment that will likely mean thousands of restaurants will rely increasingly on Sysco for day-to-day needs.

Restaurant Depot shareholders will receive $21.6 billion in cash and 91.5 million Sysco shares. Based on Sysco’s closing share price of $81.80 as of March 27, 2026, the deal has an enterprise value of about $29.1 billion.

Restaurant Depot was founded in Brooklyn in 1976. The family-run business then known as Jetro Restaurant Depot, has become the nation's largest cash-and-carry wholesaler.

The boards of both companies have approved the acquisition, but it would still need regulatory approval.

Shares of Sysco Corp. tumbled 13% Monday to $71.26, an initial decline some industry analysts expected given the cost of the deal.

Houston researcher builds radar to make self-driving cars safer

eyes on the road

A Rice University researcher is giving autonomous vehicles an “extra set of eyes.”

Current autonomous vehicles (AVs) can have an incomplete view of their surroundings, and challenges like pedestrian movement, low-light conditions and adverse weather only compound these visibility limitations.

Kun Woo Cho, a postdoctoral researcher in the lab of Rice professor of electrical and computer engineering Ashutosh Sabharwal, has developed EyeDAR to help address such issues and enhance the vehicles’ sensing accuracy. Her research was supported in part by the National Science Foundation.

The EyeDAR is an orange-sized, low-power, millimeter-wave radar that could be placed at streetlights and intersections. Its design was inspired by that of the human eye. Researchers envision that the low-cost sensors could help ensure that AVs always pick up on emergent obstacles, even when the vehicles are not within proper range for their onboard sensors and when visibility is limited.

“Current automotive sensor systems like cameras and lidar struggle with poor visibility such as you would encounter due to rain or fog or in low-lighting conditions,” Cho said in a news release. “Radar, on the other hand, operates reliably in all weather and lighting conditions and can even see through obstacles.”

Signals from a typical radar system scatter when they encounter an obstacle. Some of the signal is reflected back to the source, but most of it is often lost. In the case of AVs, this means that "pedestrians emerging from behind large vehicles, cars creeping forward at intersections or cyclists approaching at odd angles can easily go unnoticed," according to Rice.

EyeDAR, however, works to capture lost radar reflections, determine their direction and report them back to the AV in a sequence of 0s and 1s.

“Like blinking Morse code,” Cho added. “EyeDAR is a talking sensor⎯it is a first instance of integrating radar sensing and communication functionality in a single design.”

After testing, EyeDAR was able to resolve target directions 200 times faster than conventional radar designs.

While EyeDAR currently targets risks associated with AVs, particularly in high-traffic urban areas, researchers also believe the technology behind it could complement artificial intelligence efforts and be integrated into robots, drones and wearable platforms.

“EyeDAR is an example of what I like to call ‘analog computing,’” Cho added in the release. “Over the past two decades, people have been focusing on the digital and software side of computation, and the analog, hardware side has been lagging behind. I want to explore this overlooked analog design space.”