AI in your product catalog: where it helps and where you need human review

Where it really helps

A catalog has a lot of repetitive work that needs judgment but not creativity: thousands of descriptions with the same structure, attributes sitting in a spec sheet, categories that can be inferred from the name. That is where AI pays off:

• Descriptions from the product data, in the brand's voice.
• Attributes that can be read from the description or the source data.
• Categories proposed from the name and attributes.
• Translations of approved product records.
• Alt text for images.
• Photos of the product in a scene or with a model, from real photos.

In every case, the work shifts from writing from scratch to reviewing a proposal, which is much faster.

Where it gets things wrong

AI is very good at producing text that sounds right, and that is exactly the risk. It gets things wrong when:

• A fact is missing and it fills the gap with something plausible: a dimension, a material, a compatibility.
• It exaggerates. It turns “water resistant” into “waterproof”.
• It mixes up similar products when the data is ambiguous.
• It copies the wrong tone if nobody told it how the brand sounds.

None of these mistakes shows on a dashboard; they show in a return or a review.

The process that works

The rule is simple: AI proposes, a person approves. For that rule to scale:

1. Start from real data. AI works with what is in the product record; the more complete it is, the better the result.
2. Define the voice and the limits in writing: formality, tone and banned words.
3. Review a sample at first and adjust the rules before generating the whole catalog.
4. Approve before publishing, with a clear step and an owner.
5. Keep the history, to know what AI generated, what a person changed, and to be able to go back.

What Google says

Google does not penalize content for being made with AI. Its position is that appropriate use of AI is not against its guidelines, and that what matters is that the content is helpful and reliable. What it does consider spam is mass-producing content, with or without AI, with the primary purpose of manipulating rankings. A reviewed product page, based on real data and useful to the buyer, is on the right side.

How to know whether AI is helping

Measure before and after with the same numbers:

• Time per published product record, from import to approval.
• Share of proposals approved without changes. If it is low, source data or voice rules are missing.
• Errors that reach the buyer: returns or questions caused by wrong data.
• Complete records per channel every week.

If time goes down and errors don't go up, AI is doing its job; if errors go up, check the source data before blaming the tool.

Questions for your AI vendor

• Is my data used to train your models?
• Can I see what the AI generated and what a person changed?
• How much does each thing I generate cost, before I generate it?
• Are generated images disclosed as such?

How Bruxel handles it

In Bruxel AI works on each product's data and in your brand voice: it writes descriptions for search engines and AI assistants, fills attributes, proposes categories, translates, describes images and generates photos from real photos. Nothing is published without review, every product record keeps its history and generated images are disclosed as such. A customer's catalog and descriptions are not used to train AI models, with the disclosed exception of the 3D object, which is optional. The guide to generating descriptions shows the process and the PIM page the rest of the product.