What Makes it Different from Traditional Search Engines?
YesPlz.AI, January 2025
Curious about how AI fashion search differs from traditional search engines?
Let’s set the scene.
Picture a shopper visiting your online store, typing in ‘cream color blouse.’
We humans naturally understand that cream color means beige or light beige.
But how do traditional search engines versus AI search engines interpret that query?
Let’s start with traditional search engines which depend heavily on exact keyword matching.
Notice the issue? While all the results are blouses, most aren’t creamed-color.
Here’s why. A traditional search engine can’t interpret ‘cream’ or ‘cream color’. It simply looks for those exact keywords in product info. Meanwhile, fashion retailers might describe this color using other words like “light beige” or “beige.” These variations in wording make this type of search engine fail to connect the dots.
The result? It will focus solely on the keyword ‘blouse’ in the shopper search query. A bunch of random blouses are represented to her without considering the color. Consequently, it completely misses her intent for cream-colored options.
Now, let’s see how AI fashion search, which incorporates text and image recognition, handles the same query:
As you can see, aside from the first option - a green sweatshirt - all the results are cream-colored blouses. This is made possible by computer vision technology. It helps AI search engines detect fashion-specific attributes like colors, patterns, and silhouettes. So, items in your product catalog don’t need to contain exact keywords. The technology can still deliver accurate results effortlessly.
In this article, let’s uncover the technologies behind AI search engines for fashion. What makes it different from traditional ones?
AI-powered fashion search is the next generation of search engines, built exclusively for online fashion retailers. It is a multi-dimensional approach with blended search technologies including:
Text Search: To match keywords shoppers type into the search bar with product info (titles, descriptions, and tags.)
Image Search: To detect attributes of fashion items (colors, patterns, silhouettes, vibes, occasions, etc.) from product images.
Fashion-Trained Large Language Model (LLM) Search: To interpret long and complicated search queries.
Together, these three technologies create a smarter, faster, and more intuitive way for shoppers to discover fashion.
However, when it comes to the technology that differentiates AI search engines from traditional ones, it all boils down to the blend of text and image search. This hybrid approach effectively addresses the limitations of keyword-based systems.
Think of traditional search engines as a one-size-fits-all solution. Their approach is straightforward: Match keywords in search queries with product info. Thereby, this keyword-based system is generic and static. It treats all eCommerce products the same.
The downside? They lack an understanding of the fashion context. They also fail to learn, adapt, or offer a personal touch to every query.
AI fashion search can tackle all these challenges head-on with a hybrid approach.
AI search for fashion incorporates image recognition technology to complement text search. Let’s take an example to see how it works.
A shopper is looking for a denim skirt. She wants it to be short. So, she enters ‘denim skirt short’ into the search bar.
If only text-based search works, it will retrieve results by matching keywords with product info.
Look at the image below.
Items with a T label represent results powered by text search. Read their product titles. You’ll notice they contain exact keywords: ‘denim, ‘skirt,’ and ‘short.’
But, what happens if a denim skirt item is missing these keywords in its info? Or if you describe it with other words, like in the example in the introduction?
Text search technology will exclude it from search results. That’s its major limitation.
This is where hybrid search steps in. Powered by computer vision, it can scan product images. It then automatically tags them with 20 - 60 fashion-specific attributes.
Look again at the above image but focus on items with I label. Their titles don’t include exact shopper search terms. Yet, they still show up in the search results. This is thanks to the image recognition technology.
These items return results driven by image search, drawing insights from visual attributes rather than text. Their product images are tagged with fashion attributes like:
Clothing Type: Skirt
Fabric Type: Denim
Skirt length: Short
This hybrid approach eliminates the dependency on exact keyword matching.
For retailers, this means less effort spent manually updating product info.
For shoppers, feel free to enter whatever comes to mind into the search bar. It can be ‘denim mini skirt’ or ‘A-line denim skirt.’ Don’t need to optimize keywords and try different phrases like ‘short denim skirt’ or ‘denim mini skirt.’ AI fashion search handles that.
Product images provide a wealth of information. Yet, they are often overlooked by traditional search engines. By integrating text and image search, this hybrid approach enhances the ability to search product details. It thus makes every relevant product discoverable. As a result, your shoppers can enjoy a richer search experience.
In addition to hybrid search, the fashion-trained LLM also sets AI search engines apart from traditional ones.
Imagine a personal stylist working behind the scenes of your online store. This stylist can understand your shopper's style, preferences, and occasions. That’s the power of the fashion-trained LLM. It is trained specifically on fashion datasets. For this reason, it can interpret fashion occasions and appropriate styling options.
With traditional search engines, shoppers must search for each item individually. For instance, a shopper might start by searching for a top. Then, she searches for jeans in a separate query. Finally, she looks for a bag to complete the look. In this scenario, the entire outfit depends on her styling knowledge.
AI fashion search revolutionizes her discovery experience. Instead of searching for items one at a time, she can explore entire outfits. She isn’t limited to a specific item for a search query anymore.
Let’s take an example with a search query for:
“What should I wear with cream color shirts?”
This type of question goes beyond what standard text or image search engines can handle. It requires fashion-trained LLM search technology to interpret complicated and multi-layered queries.
The result? A curated list of complementary items understanding a shopper's intent for the outfit such as pants, shoes, bags, and necklaces. They are perfect for creating a cohesive, fashionable look.
Traditional search engines remain static over time. They treat every shopper the same, no matter their preferences.
AI fashion search, on the other hand, learns and adapts to every shopper interaction. From search history to clicks, every behavior feeds into algorithms to provide better results. Each session contributes to more personalized recommendations later.
Book a demo with us today and see how this next-generation search engine can help your fashion eCommerce store thrive in a competitive market.
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