E-commerce
August 27, 2026
Are you wondering if your e-commerce search engine really captures the natural language of your shoppers? Lily AI addresses this challenge by automatically enriching your product catalogs with the real attributes used by consumers. It is the ideal solution for retail brands that are losing sales because their technical data does not match customer queries, such as "flowy" or "great for the office." However, this tool is specifically designed for apparel, beauty, and home decor merchants with large catalogs, as it acts as an enriched layer rather than a standalone search engine.
So Lily AI: how does AI transform your product search? On the agenda:
Why does customer vocabulary make the difference in retail search?
How does Lily AI enrich your product attributes from images?
What concrete gains are observed on Google Shopping feeds?
Is this solution suitable for your sector (fashion, beauty, home)?
How does the integration impact your category pages and SEO?
Let's get started.
Summary
Why does customer vocabulary make a difference in retail search?
The Mismatch Between Catalogs and Queries
One of the major problems in modern e-commerce lies in the persistent gap between how brands catalog their products and how customers search for them. Product sheets are often written according to technical standards or manufacturer specifications, using jargon that does not match the natural language of buyers.
Imagine a customer looking for a "flowy" dress for a country wedding or "office-appropriate" clothing. If your database only contains technical terms like "loose fit" or "professional attire", the search engine will fail to match the query to the product. This mismatch directly leads to zero or poorly relevant results, driving an immediate loss in conversion.
AI steps in here to bridge this gap. It does not just read your attributes; it understands the emotional and visual context of the product. By identifying real adjectives, occasions, and style descriptors, it aligns your catalog with the reality of the words entered in the search bar. This approach is crucial to avoid letting prospects leave for a competitor who understood their intent.
For merchandising and marketing managers, the stakes are not just technical, it is about capturing lost revenue. Queries with low click-through rates or no results represent a missed opportunity that is imperative to recover to ensure the growth of the average basket value.
Convert over 2,000 customers on average per month with Qstomy.
The world’s 1st Shopify AI dedicated to customer conversion
Empowering 200+ e-commerce merchants
How does Lily AI enrich your product attributes from images?
Intelligent Data and Image Ingestion
The enrichment process proposed by Lily AI is based on technology capable of analyzing the visual and textual semantics of products. The tool ingests existing information from your catalog as well as images of your items to extract a multitude of details that would otherwise have been overlooked.
Unlike traditional systems that rely entirely on metadata entered manually by employees, Lily AI automatically generates hundreds of tags per product reference (SKU). These tags are not just simple, generic keywords, but attributes rich in meaning such as "cottagecore style," "silky finish," or "ideal for a summer evening."
This technology, dubbed Emotional Intelligence, translates the physical appearance and aesthetic feel of a garment or object into descriptors understandable by search engines. The analysis is done at scale, even for catalogs containing tens of thousands of references, without requiring heavy human intervention.
The result is an enriched catalog that speaks the same language as your customer. Raw data is transformed into actionable information for search engines, SEO pages, and advertising platforms, creating a database that is far more responsive to complex user queries.
What concrete improvements can be seen in Google Shopping feeds?
Feed Optimization and Advertising Cost Reduction
Improving the accuracy of product attributes has a direct and measurable impact on your advertising campaigns, particularly on Google Shopping. The titles and descriptions provided by manufacturers are often generic and do not contain the high-intent keywords that consumers are searching for.
Lily AI rewrites these attributes before they are syndicated to platforms like Google or Meta. By injecting descriptors based on occasion, style, and mood, your ads become much more relevant to user queries. This results in a significant increase in the match rate between the customer's search and your ad.
Concrete results for brands using this tool include an improved return on ad spend. You will see a decrease in cost per acquisition (CPA) because the algorithm targets interested buyers more precisely. Additionally, you gain impressions on descriptive searches that your competitors are not yet targeting because they have not enriched their attributes.
This optimization allows you to capture qualified traffic that previously escaped your advertising reach. By aligning your feed data with the actual vocabulary of the market, you maximize the efficiency of your marketing budget without increasing average click costs.
Is this solution suitable for your sector (fashion, beauty, home)?
Specialization in Apparel and Home Decor
It is crucial to understand that Lily AI is designed with specific expertise for certain retail sectors. It particularly excels in the fields of fashion (DTC), beauty and cosmetics, as well as home furniture and decor. These categories share a strong reliance on subjective descriptors such as style, texture, and occasion.
For these markets, AI enrichment delivers immediate value because customers often search using vague or emotional terms. Conversely, if you operate in sectors where technical specifications take precedence, such as electronics, heavy industry, or groceries, this tool might not be the optimal choice. The relevant attributes for an electronic product differ radically from those of a dress.
Additionally, the tool is scaled for medium to large catalogs. For shops with fewer than 5,000 SKUs, the setup cost and complexity may not be justified compared to simple manual tagging, which would suffice.
Therefore, before choosing this solution, evaluate your sector and the size of your catalog. If you are a growing fashion, beauty, or home brand, the benefits of precise semantic alignment are immense. Otherwise, other solutions might better fit your specific needs.
How does the integration impact your category pages and SEO?
Automatic Semantic Content Expansion
Product attribute enrichment by Lily AI offers a massive opportunity for your Search Engine Optimization (SEO) strategy. SEO teams must often create hundreds of category pages or landing pages based on specific attributes, such as "plus-size women's midi dresses".
Without a structured and comprehensive tagging system at the catalog root, this task is tedious and limited. Lily AI solves this problem by providing the necessary tags at the individual SKU level. The enrichment then automatically feeds faceted filters and generates dynamic category pages without any additional manual work.
Results observed by companies using this technology show a dramatic expansion of their landing page coverage, which can increase by a factor of three to five times within a quarter. This captures non-brand organic traffic that would not have been accessible before.
Furthermore, the enriched semantic structure improves the relevance of category pages in the eyes of Google and Bing search engines, promoting better rankings. This content automation allows SEO teams to focus on overall strategy rather than manual product sheet completion.
How does enriched data improve your site search?
Reduction of zero-result searches and improvement of relevance
The core of the user experience on an e-commerce site lies in the search engine's ability to respond correctly to queries. Before integrating Lily AI, it is common for search logs to indicate a high proportion of queries with zero results or low click-through rates.
These failures occur because the customer types "elegant" while your product is cataloged as "formal wear". The tool ingests this data and corrects it by adding missing attributes, allowing the internal search engine (like Algolia or Constructor) to match the query with the right product.
The impact is a double-digit increase in the conversion rate during searches. Users find what they are looking for, even if they use vague vocabulary or specific style terms like "cottagecore" or "work-appropriate". This recovers visitors who would have otherwise bounced without making a purchase.
Furthermore, the increased relevance strengthens brand image. A customer who immediately finds the perfect product using a natural keyword tends to trust the store and return later. Search fluidity thus becomes a lever for loyalty as much as for immediate conversion.
What is the impact on merchandising and digital teams?
Reduction of Manual Workload
Setting up a comprehensive manual tagging system is a major challenge for merchandising and digital teams, especially at scale. It often requires entire teams dedicated to assigning precise tags to each product, which is time-consuming and prone to errors.
Lily AI automates this workload by generating the necessary attributes via AI. This allows merchandising managers to focus on strategy rather than basic data entry. For a company with $80 million to $300 million in GMV, this represents a significant time saving that can be reallocated to higher-value initiatives.
Paid teams (Google Shopping) also see their efficiency improve without the need to increase headcount. Integrated analytics reports help identify attribute gaps and measure the impact of AI corrections in real time.
In summary, the tool acts as a force multiplier for your existing teams. It helps maintain high catalog quality at high speed, which is essential in an e-commerce environment where trends and products change rapidly.
How does Lily AI compare to other solutions on the market?
Comparison with ViSenze, Semrush, and alternatives
Lily AI does not seek to replace the entire e-commerce stack but positions itself specifically on the enrichment of text attributes. Unlike ViSenze, which is a visual search and image similarity solution for large catalogs, Lily AI focuses on the semantic structuring of text.
Similarly, while Semrush One manages external SEO visibility and competitive keyword research on a large scale, Lily AI intervenes upstream at the product catalog level itself to correct internal attribute deficiencies. This perfectly complements the ecosystem rather than duplicating effort.
Other alternatives like Pimberly or Sales Layer offer advanced PIM features, but they often differ in their technological approach or pricing. Lily AI stands out for its focus on generating attributes based on consumer language from image and text analysis, specifically targeting the needs of fashion and beauty.
The choice therefore depends on your specific needs: if you are looking for a complete PIM or a pure visual search engine, other solutions may be preferred. But for semantic enrichment targeted at text search and advertising feeds, Lily AI occupies a very specific and effective niche.
What are the main use cases for your brand?
Deployment Scenarios and Expected Benefits
Lily AI is particularly relevant for mid-sized to large apparel or beauty retailers who are experiencing bottlenecks in their online search. A classic example is a brand whose internal search fails on terms like "flowy" or "office-friendly" because the catalog does not contain these keywords.
By ingesting your catalog and retroactively adding these attributes into a search engine like Algolia, you will see a significant increase in conversion. This also works for paid marketing teams who need to optimize their Google Shopping feeds with engaging descriptions without manually increasing the number of products listed.
For larger enterprises with a robust SEO organization, the tool allows you to generate landing pages based on attributes (e.g., "midi dresses for tall women") at a scale that would be impossible to achieve manually. This opens up significant organic traffic streams.
Finally, brands using this tool can respond more effectively to complex customer queries, thereby recovering lost revenue and improving the overall shopping experience without having to hire more copywriters or taxonomists.
How to ensure compliance and trust with Lily AI?
Compliance with Regulations and Data Protection
In a European and global context where data protection is crucial, the integration of new AI tools requires increased vigilance. Lily AI ensures compliance with key privacy regulations, notably the GDPR and the CCPA (California Consumer Privacy Act).
This means that the analysis of images and product data is conducted within a framework that guarantees information security and lawful processing. Merchants can thus deploy the solution without the risk of violating customer privacy or unauthorized use of data.
Transparency in data processing also strengthens consumer trust. By knowing that their interactions and your catalog data are processed in accordance with the strictest standards, they remain assured of your platform's reliability.
This compliance is a mark of reliability for any retail brand wishing to scale its AI operations internationally. It allows for the deployment of advanced features without sacrificing trust or putting oneself at legal risk.
How does Qstomy help complete this enrichment strategy?
The Contribution of the Shopify AI Agent to Customer Service
While Lily AI enriches your product data for search and SEO, Qstomy acts as the intelligent human interface that interacts with your Shopify customers. As an AI agent, Qstomy uses these enriched attributes to understand exactly what customers are asking about specific products.
Unlike traditional chatbots that fail when confronted with jargon, Qstomy is trained on your Shopify data to provide immediate, contextual answers. It guides customers toward purchase, offers recommendations, and handles returns or package tracking without requiring heavy human intervention.
The synergy between Lily AI and Qstomy is powerful: the rich attributes generated by Lily AI allow Qstomy to pinpoint exactly what an undecided customer is looking for. If a customer asks for a "flowing dress for the office," Qstomy can immediately suggest the correct SKU using the enriched metadata.
Furthermore, Qstomy transforms the order tracking and returns experience into loyalty-building opportunities. While Lily AI attracts the customer through precise search, Qstomy secures their satisfaction post-purchase, creating a virtuous cycle of seamless customer experience.
What checklist should be followed before deploying this tool?
Key items to validate for a successful launch
Before integrating Lily AI into your e-commerce infrastructure, ensure that the prerequisites are met to maximize return on investment. First, check your catalog size and industry: the tool is optimal for fashion, beauty, or home with a SKU count greater than 5,000.
Next, verify that your internal search infrastructure (such as Algolia or Constructor) is ready to receive the new attributes. Data enrichment is useless if the search engine cannot leverage this new information to improve search result relevance.
Finally, define clear performance indicators: search conversion rate, percentage of zero-result queries, and Google Shopping campaign effectiveness. These KPIs will allow you to measure the real impact of semantic enrichment over time.
In brief
Lily AI is not a search engine in itself, but an essential enrichment layer for retail brands seeking to align their product data with consumer natural language.
To go further: E-commerce Searchandising: optimizing internal search with customer words - Qstomy, How to integrate Shopify with Amazon for products, stock, and reviews? - Qstomy, Training an e-commerce chatbot with Shopify: using the right data without creating wrong answers - Qstomy, How to use an AI chatbot for product recalls: informing without panicking customers? - Qstomy, AI Chatbot for size guides: reducing returns in fashion e-commerce - Qstomy, Increasing sales with smart product recommendations - Qstomy, E-commerce product assistant: helping undecided customers choose without pressure - Qstomy.
Enzo
August 27, 2026
