How to Prepare Your Online Store Catalog for AI: Structure, Attributes, and Data Cleanliness
What requirements do language models impose on product characteristics and stock levels? So that the AI consultant doesn't get confused and recommends exactly what customers need.
In short, if you don’t have time to read it all
- ✓AI will not recommend a product if key properties are hidden in vague advertising text.
- ✓Characteristics must be structured: property name, value, and consistent units of measurement.
- ✓The stock status field is mandatory. Otherwise, the bot will suggest items that are not available.
- ✓The fewer junk keywords in the description, the better the semantic search works.
An online store owner connects a modern AI consultant to their website. And the first thing they see: the model understands nothing. The bot mixes up sizes, suggests women’s dresses to people looking for kids’ overalls, or recommends items that have been out of stock for three months.
And in almost every case, the model is not the problem. The problem is the state of the product catalog.
1. The main enemies of AI in product databases
- Specifications buried in generic text: instead of a separate field like “Material: 100% cotton,” there’s one long emotional paragraph of 1,000 words.
- Different units of measurement: some product cards list size in millimeters, others in centimeters, and others in inches. AI can convert numbers, but across thousands of queries, this leads to random failures.
- Outdated or incomplete stock statuses: the system says the product is “in stock,” but in reality they’ve been waiting for it from the supplier for 2 weeks already.
- Garbage SEO spam in product titles: something like “Buy sneakers downtown cheap sale 2026” throws off the semantic vector and hurts search accuracy.
2. Catalog readiness checklist for automation
Before launching an AI consultant or smart search, go through this list:
- Unique SKU / item code: each product variant has its own clear identifier.
- Properties table (Key-Value): critical parameters (size, color, power, compatibility) are stored in separate database columns.
- Consistent attribute names: you cannot have “Volume,” “Capacity,” and “Liters” all used at the same time in one category.
- Clear numeric stock count or binary status:
in_stock: true/false. - Product purpose (Use Case): for complex products, it is worth adding a short field like “for whom / for what conditions” (for example: “for dry skin,” “for trail running”).
More about automation for online stores is on the AI for online stores page. Or take a look at the Demo scenario of a single assistant for search and catalog.
- Google Merchant Center Product Data Specification
- Practical Experience in Preparing Product Databases for Semantic Search
Marketing and Content Automation
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