At Beni, we’re on a mission to make buying secondhand as easy and accessible as buying new, so that it becomes the preferred shopping behavior. To accomplish this goal, we’re focused on delivering a best-in-class search experience so that shoppers everywhere can find secondhand alternatives with ease. We aim to bring resale options to these shoppers however and wherever they shop – sometimes that’s through our browser extension that tags along while you shop your favorite brands online and in the future it might be through our searchable site where you start with a broad query and zero in on exactly what you want. Essentially, you can think of us as your AI-powered shopping sidekick – helping you find the best deals on the internet.

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In order to deliver you the best resale options, there are 3 key areas that our engineering team is focused on:

1. Become experts in normalizing and categorizing the resale data that we get from our partner feeds and extracting product attributes from it that you, as a shopper, care about.
2. Become the smart shopping tool that accompanies you along your online shopping session and that can accurately identify the products you are shopping for.
3. Be able to search through our catalog of 200M+ listings to deliver you the most relevant secondhand versions of what you are looking for in a matter of seconds.

In all three areas, we leverage the power of artificial intelligence (AI) to achieve these goals. In this post, I'll provide a high-level overview of how and when we’re using AI, and in the upcoming series, I'll delve into specific examples within each area. So, let's dive in!

Normalizing and categorizing resale data.

One of the challenges, and opportunities, with resale is that the product data is super messy.  Imagine a brick-and-mortar thrift store: every single item is one-of-one and there is no structured product info, like a SKU number, to help keep track of things.  Now multiply this by millions and you have a sense of Beni’s database of resale listings.  For example, we often find differences in the way that products are categorized amongst all of our resale partners, especially as it relates to product category, gender, color, brand, and size. If we are unable to appropriately classify products into the right bins, it makes it almost impossible to surface these listings to you in the right situations. For each of the product attributes mentioned above, we use a diverse range of technologies to re-classify items into distinct bins, carefully defined here at Beni.

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In some cases, just re-classifying the standard product attributes is not enough to surface a more appropriate listing above another. The Beni catalog has gotten so large, that there are some products out there that we just have literally hundreds of relevant matches for (cue the Bottega Veneta Cassette Padded Leather Crossbody Bag). At Beni, we’re working hard to improve our ranking algorithm to better curate the selection that we deliver to you. There are plenty of opportunities to continue to improve the algorithm using generative AI (AI that generates content, like GPT) to extract product attributes that shoppers care about from the semantics of the data that is present in our catalog. Some examples might include classifying brands into different tiers and only showcasing products from the same brand tier, further evaluating the condition of the item, or refining the potential discount that you might achieve by purchasing the item secondhand.

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Accompanying you along your online shopping sessions.

At Beni, we can shop with you on over 1500+ merchant retail sites (and counting). We have developed the technology in-house that allows us to determine whether you are shopping on Patagonia or Reformation, and whether you are shopping for the Nano Puff Jacket or the Frankie Silk Dress. As you might imagine, the structures of these websites have a tendency to change (have you ever revisited a link to an item that you saved on your favorite retail site that no longer exists?). At Beni, we have developed a technology that notifies us when our recipe to read a merchant site is “broken”, and are developing ways to automate a fix using generative AI.

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One exciting recent development is that we can use this same tooling to not only fix existing merchant sites, but also to automatically add support for new merchants on the fly. If our goal is to be the best-in-class at delivering your search in resale, we don’t want to interrupt your shopping experience when you try to use Beni on a site we don’t yet support. Instead, we want to automatically add support for the site you’re shopping on at the time you are actively engaged in shopping.

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Finally, there is a lot that goes on behind the scenes in categorizing the products that you are looking at in real time. Our product catalog is similarly structured to how you might organize your closet - you put all your pants in one drawer, all your socks in another, etc. When you are looking for pants, you don’t go sweeping through your whole wardrobe looking for pants - instead, you go directly to the drawer where the pants are stored. Well, that means that when Beni is shopping with you online, we need to know that you, too, are looking for pants. In many cases, we can do this with a keyword search against a bank of keywords that we have used to define “pants” (trousers, jeans, …). But in some cases, the product attributes that are available to us are not as descriptive as we would like them to be (cue the Bernardo Hooded Quilted Puffer Walker, you can see that this is a jacket, but it doesn’t actually say “jacket” anywhere on the product page). In those cases, we use generative AI to help us further tag the products so that we can appropriately identify them at the beginning of our search.

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Delivering search in a matter of seconds.

There’s a lot that goes on behind the scenes when it comes to searching a catalog of resale items, and little tweaks that we make to the algorithm can lead to pretty large impacts in the ranking of the results. At Beni, we are currently using a weighted combination of searching with the product image (using AI to perform the reverse image search), product-related descriptors like the title and the brand (using AI to perform text similarity search), the price, and slicing the data by the aforementioned product attributes (category, size, color, etc). But what excites me most about the future is that with all the recent advancements that have been happening with generative AI, there are increasingly accessible opportunities for Beni to layer technology that allows us to query the data in our catalog based on the semantics or the meaning of the data rather than just querying the data itself, enabling us to satisfy queries that are based on seasonality or trends (things like “find me dresses that are perfect for summer weddings requiring garden party attire”, or “find me pink heels I can wear to go watch the Barbie movie”).

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At Beni, our passion for delivering you an exceptional search experience through the world of resale is the driving force behind our every endeavor, and utilizing powerful technology like AI to deliver those secondhand deals to everyone, everywhere on the internet is at the core of everything we do.

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Join us on this series in sharing with you how we utilize our technology to shape the future of resale.

Have questions you want us to answer in our next post? Comment below!

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