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Tech Behind Luxury Fashion Marketplaces is 10 Years Behind, Says The List Founder Andreas Skorskiby@thelist
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Tech Behind Luxury Fashion Marketplaces is 10 Years Behind, Says The List Founder Andreas Skorski

by TheListMay 17th, 2024
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Online marketplaces for luxury brands are weighed down by incredibly high costs: tech development, catalog production, shipping, and data architecture, says Andreas Skorski.
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THE LIST is a luxury fashion marketplace powered by <GENESYS>, an AI-based API developed in-house.


THE LIST allows the world’s best boutiques and brands to run AI-driven catalog production at current 810x cost efficiency vs. traditional product catalog production, enabling retailers to sell in 190 countries with integration time in single-digit days.


THE LIST was founded by Andreas Skorski and has offices in New York City and Dubai.

1. What is your company in 2–5 words?

Luxury fashion powered by AI.

2. Why is now the time for your company to exist?

Luxury ecommerce has imploded in the last year or so (Financial Times, NYT). This is not an understatement.

Online marketplaces for luxury brands are weighed down by incredibly high costs: tech development, catalog production, shipping, and data architecture.


The tech behind luxury fashion marketplaces is 10 years behind and extremely inefficient. Even with margins of 50%, these marketplaces can’t make it work.


That said, the market for luxury is not going anywhere.

We built The List to showcase what the next stage of e-commerce is: that AI can operate a luxury fashion marketplace. We are building the largest LLM library in the industry to eliminate some of the market’s critical operational inefficiencies. Our AI-based API, <GENESYS> is the first viable, market-tested AI solution in the luxury industry, and powers other major retailers as well.


Our machine learning API transforms any in-store retail product into a catalog in real-time by mapping ERP (enterprise resource planning) data, extracting attributes, and enriching data from images,  producing dozens of relevant data points  to describe it as such.


It doesn’t just see a photo of a shoe. It understands the height of the heel, the fact that it’s suede, the exact color, the seasons it can be worn in. It’s 98.9% accurate and working for hundreds of thousands of products in real-time.

3. What do you love about your team, and why are you the ones to solve this problem?

Many of our data scientists previously worked for a competitor or the biggest global retail brands. They see how the future of e-commerce could look like and want to actually build something that operates at the same level AI really operates today, having efficiency as the number 1 priority in mind.


The cost of getting a single product (SKU) into a catalog can be up to $80 per SKU. It’s fairly common for marketplaces to add 100,000 new SKUs to their sites weekly, so our competitors are burning a ton of cash every week.


Our team has been watching, training, and guiding our own model of catalog production for years. Our cost is now down to less than 10 cents per SKU.


So the team is remarkably goal-oriented and determined to find solutions to problems they personally experienced as data scientists in the industry.

4. If you weren’t building your startup, what would you be doing?

Probably building some other startup. When I was 17 I created the first ad scanner technology for print magazines in Germany. Since then I haven’t really stopped.

5. At the moment, how do you measure success? What are your metrics?

We are a software-focused retail marketplace, so we look at both the retail success of our marketplace and the efficiency of our <GENESYS> API. For us it is all about giving a top-class experience to our customers while driving down costs for our suppliers.


From a software POV:

  1. Accuracy rate of our LLMs — how often is human intervention needed?

  2. Items scanned – SKUs processed per month by our API

  3. Integration time – how long does it take for a retailer to put their products on The List?


From a retail POV we are looking at:

  1. Gross Merchandise Value
  2. Customers and their stickiness
  3. Suppliers and their stickiness

6. In a few sentences, what do you offer to whom?

If you buy luxury fashion:

You can expect to find all your favorite brands on The List at the best possible price globally available, enabled through <GENESYS>.

If you are a decision maker at a company that sells luxury fashion:

You can expect the fastest integration time globally available with no engineering cost involved from your side, market-tested AI-based catalog production, and related AI-driven efficiencies in marketing, shipping, and pricing. If there is an inefficiency in the value chain we are likely already working on the models to fix it.

7. What’s most exciting about your traction to date?

Luxury fashion is perfect for data scientists to train a model. It’s very fast moving and changes all the time. There’s seasonality in collections, now even more with capsule collections. And the more variations you feed into a model, the faster it learns.


We started out years ago, and at the time, AI-driven efficiencies were not hot.


The progress of LLMs on the market has accelerated interest in what we do, which is very exciting. You can do a lot with open source models, but you have an advantage if you already have a database that you’ve been training with your own models and scanning millions of data points. Tens of thousands of customers have validated our AI with their purchases.  It’s gratifying to be getting traction now with suppliers and other major players in the industry.

8. Where do you think your growth will be next year?

We are going to scale our direct to consumer marketplace growth this year in more markets while  also building out our platform business with our b2b customers.

9. Tell us about your first paying customer and revenue expectations over the next year.

We had our first paying customer years ago! We’re pivoting.

10. What’s your biggest threat?

Our biggest threat is a general bearishness in the luxury market at the moment. Retailers are skittish about investing in multi brand marketplaces after what has happened to our competitors. They’re looking at finding better solutions, but there is still a lot of education to be done in the AI space to showcase it is already a commercially viable and operating solution and not just a dream of the future.


Our task is to convince them that marketplaces aren’t the problem; it’s just that they need to be better and evolve. The List is the next evolution of how to operate e-commerce 2.0 in luxury fashion.

This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders.


Would you like to take a stab at answering some of these questions? The link for the template is HERE.