Prigigallo has always been fond of watches, especially Rolex, and he even inherited a few from both of his grandfathers. In Italy, especially the island of Sardinia where he was raised, Prigigallo says there’s a strong cultural affinity for fancy timepieces. “Anyone that has made a bit of money and has a good career has at least one good watch,” says Prigigallo.

But watch collectors like Prigigallo face a complex market without a centralized source of pricing data.

“Sometimes you’re completely clueless of the actual value of the watch,” says Prigigallo. “And you might think it’s a beautiful watch, but don’t know anything about the watch, just that it looks beautiful. And you’re going to pay a staggering price.”

As a result, he cofounded EveryWatch, which uses AI to digest data from the sale of around 400,000 watches from auctions and dealers as far back as 1989. EveryWatch’s subscription-based model provides analytics to collectors that want better transparency about the 12-month median auction value, latest sale price, and other pricing details for watches from Rolex, Cartier, and Omega. EveryWatch’s platform incorporates data from more than 250 auction houses and over 180 marketplaces and dealers globally. 

“We provide all the data you need to actually make the best informed decision,” says Prigigallo.

North of Sardinia, in mainland Italy, Carlo Arrigoni is chief marketing officer at eWibe, which is an online marketplace to buy and sell fine wines. “We are halfway between passion and pure investments,” says Arrigoni. EWibe’s users are able to create their own wine collection and then resell it on the market for a possible profit. The startup also handles storage of the wines in their cellar in the Italian city of Como. 

To make the wine investment process a bit smoother for customers, eWibe utilizes ChatGPT’s AI system to create a chatbot that can guide investors as they create their portfolio. “We educate the AI with all the common questions that were made by users and all the data and information that’s available in the platform,” says Arrigoni.

The AI chatbot tool, which launched in December, works by understanding a collector’s preferences including price range for the potential investment, desired wine vintages and region, and risk tolerance. It then peers into eWibe’s wine marketplace and selects the best combination of wines to match the user’s desires.

“The user is able to use the AI to tell him exactly how he expects his portfolio to be built,” says Arrigoni.

Within the luxury resale market, AI is often used for authentication. While researching this story, I visited the office of a public relations agency to try a device created by the startup Entrupy, which uses AI to verify handbags, accessories, and sneakers. I hand over my Prada wallet after taking several close up photos of the wallet with an iPhone that has a microscopic attachment, Entrupy verifies my wallet is authentic within 30 minutes. And I know this is correct, because I purchased it directly from a Prada store in Pennsylvania 15 years ago. 

“Our goal is to be transparent and add trust to the market,” said Entrupy CEO and cofounder Vidyuth Srinivasan. He says counterfeits are a $3 trillion problem globally, affecting everything ranging from luxury goods, to art, auto parts, and pharmaceuticals.

Initially, Entrupy faced challenges actually compiling data from luxury brands because they don’t tend to acknowledge counterfeits exist. “Machine learning is great, but for that you need data,” says Srinivasan. “We went to the brands, they liked what they saw, but said, ‘Come back to us when you have the data.’”

Entrupy responded by collecting data from the secondary market, compiling details from both authentic and fake goods. It proclaims to have a 99.1% accuracy rate. Of that remaining 0.9%, Srinivasan says a sliding scale aims to tag more authentic items as fake, rather than the reverse, which would be more damaging to Entrupy and the resellers and consignment shops it works with. Entrupy vows to cover the costs of any fakes that may slip through the system.

In Tulsa, Okla., Venita Cooper founded Arbit, an AI-powered pricing algorithm that provides pricing guidance for the sneaker marketplace. 

“Pricing was kind of all over the place and very speculative,” says Cooper, especially during the pandemic. “Resellers were selling at whatever price they thought they could get for a pair of shoes. AI felt like the perfect kind of technology to come and make sense of the data.”

Cooper says Arbit’s target user is someone who buys sneakers and knows a bit about the market, but wants to better understand pricing for the hottest Jordan, Nike, or Adidas. Arbit offers details about the fair market value for sneakers and a prediction for the price in 30 days. 

Arbit, which has raised $1 million in pre-seed funding, is still in beta but plans to expand the predictive pricing platform to the luxury handbag market later this year. More men are sneakerheads than women, Cooper says, adding “we want to involve more women in our products.”

Resale marketplace Poshmark last year unveiled Posh Lens, which uses AI-based capabilities to identify the name of a brand or style in a photo uploaded to the Poshmark app. “It’s been useful for both the shoppers and the sellers,” says founder and CEO Manish Chandra.

Poshmark has added about 100 million listings to the database and continues to add more to fine tune the technology. “But there’s a lot more work we can do to make it even more foolproof and even more delightful,” says Chandra. 

He foresees a future where generative AI can be used for more immersive styling, content creation, and greater authenticity of clothing. What if, he posits, a customer took a photo of a sweater in their closet and generative AI was able to detect the brand, and generate a complete listing online for that item to be sold?

“That’s an area where generative AI is truly very, very powerful and can help this industry,” says Chandra. “Because if you put your entire closet online by just scanning it, it’s pretty magical, right?”