Haz, an AI-powered app for secondhand clothes, just raised $1.4 million with this 10-slide pitch deck
- London-based startup Haz has raised $1.4 million in pre-seed funding led by Speedinvest.
- Founded in 2023, Haz digitizes users' clothing items and allows sharing transactions with friends.
- The startup will use the money to enhance AI capabilities alongside key new hires.
Haz, a London-based startup that has built an AI-powered platform where users buy and sell secondhand clothing, has raised $1.4 million in pre-seed funding.
The startup's platform lets users share their transactions with friends. This gamified approach lets friends and connections see leaderboards of transactions. It can even scan things like email inboxes for receipts and add those to a user's list of clothing owned. It can also track an item's resale value.
"There's more of an emphasis on being environmentally conscious, and increasingly, people are choosing to buy things based on what the resale price could be in the future," co-founder Ronan Harvey-Kelly told Business Insider.
Harvey-Kelly, who was previously part of the founding team at UK care home startup Lottie, cofounded Haz last year after finding the process of buying and selling wholesale secondhand items painstaking.
He added that the gamified aspect of the platform was aimed at Gen Z, who are "looking for different types of connection across the internet."
Fundraising began late last year and took just over three weeks to complete, led by Speedinvest. The scout programs of A16Z and Atomico, Concept Ventures, and a variety of angel investors also participated.
The funding will go toward building more AI and machine learning capabilities for the app, which is already live for iOS, Harvey-Kelly told BI.
Haz's two co-founders are the only employees and they intend to initially keep the team lean, with the money leading to only two or three new key hires, Harvey-Kelly added.
He added one of these hires would be a "founding engineer" to lead on AI.
Check out Haz's 10-slide pitch deck below: