The Wardrobe App That Builds Itself From Your Inbox

01 /The Concept

Haz built an app where users can catalog their entire wardrobe and sell items they no longer want – all in one place.

Getting started is straightforward: users can build their wardrobe from scratch or add existing items. But the fastest onboarding comes from linking an email account. Haz's AI engine scans for purchase confirmation emails and automatically imports items into the wardrobe, complete with photos, descriptions, and original prices. From that point on, new purchases are added automatically as receipts arrive.

The real cleverness, though, lives in two other features: a social feed that automatically surfaces everything friends have recently bought, and an AI engine that continuously calculates and updates the current resale value of every item in the wardrobe, drawing on what the company describes as "millions of signals" that influence secondhand prices by category and brand.

When a user wants to sell something, they mark it in the app. Other users can see the listing and make an offer. Before any purchase is confirmed, the seller is required to record a short video of the item so the buyer can verify its condition. The sale goes through only when the buyer confirms after watching the video – at which point Haz takes a commission.

Haz launched its app in late March and has now raised its first €1.2M in funding.

02 /Why It Matters

There's a third key feature that doesn't appear on the website but came up in a co-founder interview.

Friends who follow a user can see their wardrobe and save items they like – including things the owner hasn't listed for sale. That creates the ability for friends to reach out and make an offer on items the owner wasn't actively trying to move.

Here's where the AI pricing becomes especially interesting. When a user lists something for sale, buyers typically try to negotiate below market value. But when a friend initiates the conversation – coming to the seller unprompted – they're more likely to offer above market value to make it worth the seller's while.

Accurate resale pricing matters for another reason: according to ThredUp, one of the largest secondhand marketplaces, 82% of younger shoppers mentally calculate the future resale value of an item before buying it new. Haz's automatic price tracking keeps that mental model alive for as long as the item stays in the wardrobe.

Croissant – [covered previously](/review/reshaem-staruju-problemu-no-vryvaemsja-na-novyj-rynok) – targets the same behavioral pattern, but at the point of purchase. When a shopper checks out through a Croissant-enabled store, they're immediately offered a fixed buyback guarantee for the next year. Croissant acquires the item at that price, then flips it on a resale marketplace, keeping the spread. The upside for retailers is twofold: higher conversion rates (the guaranteed exit option removes hesitation) and fewer returns – meaningful given that shoppers currently return roughly 20% of clothing purchases. Investors found the model compelling enough to write a $24M first-round check.

The social shopping feed – showing what friends have been buying – is a useful mechanic in its own right. Claim, [covered previously](/review/zarabatyvaj-prjamo-so-starta), built a whole social network for college students around this idea, surfacing what friends are buying at nearby cafes and shops, and adding a coupon-swap layer on top. That earned the startup $6M in funding.

03 /Opportunities

ThredUp projects the secondhand clothing market will reach $350 billion by 2028 – growing three times faster than new apparel. That pace reflects both the economic climate and the generational shift: millennials and Gen Z have proven far more comfortable buying pre-owned than older cohorts.

The data bears this out: 2 in 5 clothing items purchased in the past year were secondhand. On average, shoppers are now spending close to half their clothing budget on pre-owned items.

The big opportunity is obvious: the resale market is large, growing fast, and aligned with how the next generation shops.

Established marketplaces like ThredUp have captured early share, but the classic two-sided marketplace problem – finding buyers and finding sellers – is expensive and slow to solve.

Startups like Haz and Croissant take a different approach: they embed themselves in the natural lifecycle of buying and owning things. That gives them a structural edge in acquiring at least one side of the market cheaply – Croissant locks in sellers at the moment of original purchase; Haz captures both sides through social graphs and wardrobe habits. That kind of embedded acquisition could allow these services to grow faster than marketplace-only competitors.

If this market interests you, the lesson from both companies is the same: find a mechanic that plugs into how people already buy, own, and think about their clothes. The lifecycle moments most worth targeting are the ones that generate natural data – a purchase receipt, a wardrobe photo, a trade-in request – because data is what makes the AI pricing engine defensible over time.