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Save this story Save Story Save this story The next time someone asks ChatGPT to find them a designer handbag, the answer could be a previously owned version, even if they weren’t originally looking to shop secondhand.
Rémi Bouchez, who was appointed as Vestiaire Collective’s new CTO in March, says the resale platform is working to make its product listing available to OpenAI’s broader agentic shopping systems, which would allow secondhand pieces to appear alongside new products in AI-generated recommendations. The proposed plan follows Vestiaire Collective quietly launching a dedicated app plugin within ChatGPT last month, allowing shoppers to search its pre-owned luxury inventory and compare products using conversational prompts within the chatbot. A shopper searching for a new Prada bag could now conceivably be shown a pre-owned version at a fraction of the price, or an archival design that is no longer available firsthand.
At a time when luxury consumers are questioning rising prices, this could significantly impact the shopping journey. And this is just one part of a wider transformation in how secondhand fashion is discovered. Resale platforms are simultaneously rebuilding their own search and recommendation algorithms, using AI to understand product images, interpret shoppers’ tastes and determine which listings get seen. As a result, the rules of discovery are changing on two fronts: within resale marketplaces themselves, and through the AI assistants that could increasingly direct shoppers towards them.
Bouchez believes that if AI agents like Muse, Instinct and Duet reach mainstream adoption, it could fundamentally change how luxury shoppers weigh up their options.
“These agents are capable of ingesting a ton of information — way more than human shoppers can. They remember user preferences over time, and they can catch things like discreet terms and conditions or shady seller behaviors and put them in front of a user who would have otherwise missed them,” says Bouchez. He explains how an AI agent could compare a Prada bag across several platforms, assessing its condition, authentication details and seller reliability before recommending the best value option, raising the stakes for resellers.
It’s an opportunity resale platforms have long been waiting for. Finding a particular product secondhand has traditionally meant scrolling through multiple marketplace apps, deciphering inconsistent descriptions and comparing endless individual listings. AI now promises to take much of the legwork out of that hunt.
Across the resale industry, rival platforms are investing in both forms of discovery. Depop has enabled shoppers to search its marketplace through Apple’s Visual Intelligence on supported iPhones. Last month, The RealReal introduced its own AI shopping assistant, Ask TRR, built on Google Gemini AI that enables shoppers to search in natural language — or by uploading images of products they’ve seen for visual search — while learning shoppers’ preferences. Speaking on the company’s third-quarter earnings call last week, CEO Rati Levesque said Ask TRR’s early adopters spend 20% more per transaction than non-users. The RealReal is also preparing for shopping journeys that begin outside its own platform. A spokesperson tells Vogue Business the company is restructuring data on its 1.5 million listed items to make them easier for external AI agents to find and recommend.
Together, this represents an AI-driven sea change for resale. While AI may struggle to replicate the subjective judgement involved in fashion discovery, resale presents a more straightforward opportunity. A shopper searching for a particular archival bag doesn’t need their algorithm to have good taste, just to identify the right model and compare available listings by price, condition and authenticity.
Resale discovery is increasingly moving beyond keyword search. At Vestiaire Collective , visual search can now recognise multiple objects in an image and identify similar listings, while its search systems increasingly interpret natural language requests. AI can also extract information from photographs — including aspects of fit and visible defects — that previously depended on sellers filling in descriptions.
Poshmark is also using AI to bridge the gap between what sellers photograph and what shoppers can find. Its “Smart List AI” tool automatically generates searchable titles, descriptions and product categories from uploaded photographs. Chief product officer Heather Friedland says it makes listing creation less of a “heavy lift” once the photos are up, and that listing on Poshmark is now five times faster than before. Poshmark’s AI tool also automates offers, letting Poshmark negotiate with buyers on behalf of the seller, which she says makes selling 50% faster. “We want sellers to focus most on what they do best: source great items and have Poshmark take care of the rest,” she says.
At Depop, shoppers are increasingly browsing the personalized Suggested For You feed rather than starting with a search, says chief product officer Sonia Biddle. Its algorithm responds to individual preferences and emerging cultural trends, while AI identifies listings by category, color and style, making products discoverable even when sellers haven’t used precisely the right keywords.
“Fashion often starts with an idea: an era, an occasion or someone whose style you love, and that’s not always something you can put into words,” Biddle says. “AI helps us understand what you’re after, not just the words you type.” For sellers, that means some longstanding tactics like keyword hashtags may become less important, while product photography, pricing and reliability are becoming harder to ignore.
Depop’s visual search also allows shoppers to upload a photograph or screenshot — including of something spotted in a first-hand retailer — to find similar secondhand pieces. A new-season jacket might inspire a purchase, but not necessarily from the brand selling it.
Resale execs recommend sellers photograph items in natural light, capturing multiple angles and clearly showing any signs of wear. Photo: Courtesy of Depop
As images become more central to discovery, platforms are also investing in how listings look. Last month, Vestiaire announced a partnership with AI photography company Photoroom to standardise the main photograph of every new listing — around 40,000 items a day — bringing the often inconsistent images uploaded by individual sellers closer to the polished aesthetic of first-hand luxury retail.
The technology removes backgrounds, adjusts lighting, centres products and adds realistic shadows. According to results from a randomized A/B test conducted by Vestiaire Collective and Photoroom, listings processed using the new technology had a 5% higher sell-through rate than those using Vestiaire’s previous in-house photo-editing tool, while unique product page views increased by between 3% and 7%.
Next, Vestiaire is experimenting with much more ambitious AI image enhancements that can make the items themselves look more PDP shoot ready, Bouchez says, like digital ironing.
It’s a delicate balance: AI can make a pre-owned handbag look like something fit for a luxury DTC site, but an algorithm that makes a scuff disappear could also undermine the trust on which resale depends. Yet Bouchez says Vestiaire has seen an approximately 6% improvement in the proportion of enhanced listings selling within seven days since these AI image updates, without a corresponding increase in returns.
For sellers, getting discovered still depends heavily on the algorithms operating within resale marketplaces themselves. So what determines which listings appear first, and can sellers influence their ranking?
At Vestiaire, the answer isn’t simply the lowest price or the newest upload. Bouchez describes a three-stage ranking system that identifies relevant products, reorders them according to marketplace factors and then adjusts results to individual preferences. Those factors include condition, pricing, location and seller reliability. Because Vestiaire operates globally, proximity can matter: a nearby item may mean faster shipping, fewer customs complications and easier returns, which may mean it gets surfaced first.
Vestiaire assesses what constitutes a fair price according to factors including condition, location and whether the seller is a professional offering returns. An implausibly cheap luxury item may raise questions about authenticity, rather than look like an irresistible bargain. Bouchez says that analysing these factors is still a combination of humans and AI.
“Trust is a big pillar for Vestiaire — we invest a lot in that trust, on manual verification, on assessing a listing before it’s published,” he says. “Obviously, we want to keep the same level of trust but lower the investment with AI, but the more we can keep this USP high and even increase the trust, the better our position as a platform.”
Depop’s search algorithm prioritises how closely a listing matches a shopper’s query, including brand, category, size and color. Popularity signals — views, likes and additions to shopping bags — also matter, alongside seller history, location and personal shopping preferences. If a listing is underperforming, she recommends new photographs, a more detailed description and a pricing review.
Shoppers, meanwhile, are becoming more deliberate about influencing what appears in their feeds.
“It’s been really interesting hearing our community talk about ‘training their algorithm,’” Biddle says. “People know that what they like, view and save shapes what they see, and they’re becoming really deliberate about it.” But there’s a downside to this hyper-personalization: the more accurately a feed reflects established tastes, the less likely shoppers may be to encounter something unexpected.
Good product photography is increasingly important for visibility on resale platforms. Photo: Courtesy of The RealReal.
To counter AI’s tendency to reinforce existing preferences, Poshmark introduced a new “Tastemaker” programme in September, enlisting fashion figures including Courtney Grow, Colleen Allen and Jalil Johnson to curate shoppable edits from across its resale inventory. The platform has also established a dedicated human merchandising team to work alongside its AI and machine learning specialists, identifying emerging trends and bringing a more editorial approach to product recommendations.
“On trends, a purely algorithmic system tends to amplify what’s already popular,” Friedland says. “Human curation is our counterweight. Our tastemakers bring in new styles and AI then figures out which shoppers those styles will resonate with.”
For shoppers looking to escape their own personalized recommendations, Biddle suggests sorting search results by price or newest first, too. Visual search can also help uncover pieces that shoppers cannot easily describe. On Poshmark, Friedland recommends saving searches for specific archival pieces and liking items to help refine future recommendations.
And as AI agents take on more of the shopping process, the question of what consumers don’t see becomes even more significant. Vestiaire’s ChatGPT integration allows shoppers to compare up to three selected listings, asking the AI agent to explain differences in price, condition and seller information. But it also introduces another gatekeeper: instead of scrolling through hundreds of listings themselves, shoppers may increasingly rely on an AI assistant to decide which options are worth considering — and which never make it onto their shortlist.
For first-hand luxury brands, the implications go beyond losing the occasional sale to a cheaper pre-owned alternative. Resale platforms are accumulating information about which designs consumers continue to desire, years after they disappear from stores. Depop’s Biddle argues that these signals can be valuable for brands developing future collections.
“Resale tells brands something their own sales data can’t: what people still love years after it left the shop floor,” she says. “When we see searches spike for a particular era or label, it’s often an early sign that something’s coming back.”
Poshmark is similarly using AI to identify patterns across searches, listings, likes and transactions, tracking demand for particular designers, eras, silhouettes and price points. Friedland says combining these signals with scarcity and actual resale prices can help distinguish fleeting trends from enduring desirability — offering brands a measure of a product’s success beyond its initial sellthrough.
For luxury brands, the stakes are particularly high. If AI assistants can connect consumers with archival products as readily as current collections, brands may find themselves competing with their own past successes, without controlling how those products are presented or compared.
As sellers learn to level up their listings, shoppers train their feeds and luxury brands compete for attention, the question is how much influence any of them will retain over what an AI agent ultimately recommends.
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Source: Lifestyle
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