Native AI Search Is Only Half the Battle for E-Commerce Brands

Shopify launched its AI-powered semantic search functionality in early 2024, giving Shopify Plus merchants more accurate and relevant search results. Shopify's semantic search reflects the growing use of AI in product discovery across e-commerce platforms.

Shopify’s native semantic search API is built directly into the GraphQL Storefront API via the search and predictive-search queries. According to Shopify, the tool analyzes text and image data associated with merchants’ products to better match them to customer search terms that retailers might not use themselves in keyword tagging. It interprets natural language intent, synonyms, and context using embedded AI-driven vectors.

While Shopify is not the only e-commerce platform to enhance search capabilities, its early innovation has encouraged other platforms to expand AI-powered semantic search, enabling retailers to better match products to how shoppers describe them. For instance, Fast Simon's product discovery platform has its own AI semantic search for Shopify brands.

Fast Simon has had a front-row view of how semantic search is changing product discovery and how shoppers find products online.

Zohar Gilad, co-founder and CEO of Fast Simon, believes that semantic search is a good thing and is becoming table stakes, just like keyword search did years ago.

"Understanding shopper intent through AI semantic search has been an important capability for years. But semantic understanding alone isn't enough," he told the E-Commerce Times.

The Real Product Discovery Battleground

According to Gilad, unlike Amazon, direct-to-consumer (D2C) shopper behavior is very different. On Amazon, search is everything because of the endless aisle. On brand websites, search is typically only 15–20% of product discovery.

"The remaining 80%, especially in apparel, footwear, and accessories, comes from browsing collections," he said.

Two years ago, Fast Simon introduced hybrid search because semantic search and keyword search each have strengths and weaknesses. The goal is not simply to understand what the shopper means. It is to decide which products should be shown first for that shopper, for that merchant, and at that moment.

Gilad agreed that Shopify's semantic search API is an important building block, just as its native keyword search has been. But product discovery is an application layer.

"Every merchant has different business goals, merchandising strategies, and customer expectations. That's where specialized discovery solutions continue to add value," he said.

Semantic Data Changes the Browsing Experience

Historically, site search was a reactive utility where the shopper typed a word and got a result. Now, AI shopping assistants and conversational commerce are becoming more common.

"Product discovery has always been a multi-surface experience rather than a single search box," Gilad said.

A shopper might start by browsing a collection, performing a short semantic search, and asking a follow-up question. Eventually, the shopper has a multi-turn conversation with an AI shopping assistant. Those experiences complement each other rather than replace one another, he noted.

"Semantic search is valuable because it lets shoppers describe products naturally instead of guessing the exact wording used in the product catalog. It bridges the gap between how people think and how products are described," Gilad explained.

Why Better Product Data Matters

Gilad argued that semantic search exposes a common weakness: poor product data. If a merchant’s taxonomy, metadata, and variant descriptions are lacking detail, even the smartest semantic engine struggles.

E-commerce optimization is becoming less about keyword stuffing and more about improving product data that AI systems use to understand products.

Gilad noted that rich product data has always mattered. AI raises the cost of poor product data.

"Many brands already do a good job maintaining comprehensive catalogs. For those that don't, AI can automate much of the enrichment process. Today's LLMs can enrich product information using product descriptions, reviews, social content, and other external signals," he explained.

For example, if shoppers on social media describe a shoe as having a "70s vibe" or being "quiet luxury," AI can automatically enrich that product with those concepts even if they never appeared in the original catalog.

Better Product Data Pays Off

Baruch Labunski, CEO of Digital marketing services firm Rank Secure, sees e-commerce entering a new phase with the introduction of semantic search. Companies that enrich their product data will benefit the most as e-commerce evolves with AI.

"For e-commerce, this drastically changes the way customers discover and buy products. Customers do not query search engines like they used to," Labunski told the E-Commerce Times.

He explained that instead of entering keywords, shoppers now use natural-language queries such as "running shoes for flat feet" and "dining tables for small apartments." Search engines can now understand the intent and context of those phrases.

Labunski advised that e-commerce companies will benefit most if they can combine product data, customer reviews, and other information. They will go beyond traditional search optimization to help customers find more relevant products.

"This functionality can improve the customer experience throughout the buying journey. Semantic search will reduce a customer’s search time and will improve a customer’s likelihood to purchase. E-commerce companies will benefit most from enhanced customer satisfaction and increased revenues," he added.

Not Knowing the Two Faces of Search Can Cost Retailers

According to Chris McCarron, founder of AI-powered conversion rate optimization agency GoGoChimp, semantic search in e-commerce has two types. Retailers often overlook this distinction, costing themselves time and money. On-store semantic search and off-store AI search are two completely different things.

He explained that on-store semantic search is ultimately about closing the conversion gap. That is the goal of whatever storefront tech retailers run, such as Shopify, Algolia, or Vertex.

"It saves the sale for shoppers who already arrived. While this is super-useful and worthwhile, it's not a growth channel," McCarron told the E-Commerce Times.

Off-store semantic AI search decides whether the shopper ever visits. He noted that in the last 30 days, his own site earned 9,967 Microsoft Copilot citations against 82 Google organic clicks, a 73:1 ratio.

He sees AI engines as the new category page. While on paper this sounds horrible, he finds conversion rates to go through the roof.

"This is because buyers use AI search to browse and research the best options for them, before arriving at the site to act," he said.

How Brands Can Improve Semantic Search Visibility

Semantic search relies heavily on vector embeddings, which often leverage both text and product image data. Brands need to ensure that AI models recognize stylistic nuances rather than just basic colors or shapes.

Fast Simon's Gilad suggested that the first step is having high-quality images from multiple angles with enough visual detail. Better inputs almost always produce better outputs. The second is ensuring those images and all the supporting product information are accurately embedded.

"That's a combination of data science, engineering, and model selection. It's not just about the model itself. It’s about deciding what information goes into the embedding and how it's organized," he clarified.

Gilad noted that embedding models will continue to improve. He sees the real advantage coming from how merchants structure and enrich product data for those models.