The query «which cordless screwdriver should I get for the house» increasingly comes back not as a list of blue links but as a finished paragraph of text — in ChatGPT, in Perplexity, or in the AI Overviews block above Google's results. A store is either mentioned in that answer or it is not. Clicking anywhere further is no longer required: the answer is already there.
How many queries actually take that route we do not know, and we are not going to invent a number — we have not seen a single measurement of Ukrainian e-commerce we could point to. But the mechanism is already working, and for a catalogue owner the question is very concrete: what makes a product description fit to be quoted, and how do you check that without taking it on faith.
This article is about the mechanism. How to rewrite a whole catalogue without doing it by hand we covered separately: bulk rewriting of descriptions. Here the subject is different — how a description fit for citation differs from one that is merely unique. We explain it through Textory, our product where this is a separate mode.
«Unique» and «citable» are not the same thing
A unique description removes technical loss: the supplier's text no longer stands word for word in dozens of stores. That is necessary, but it is not enough for the new task.
A text can be a hundred per cent unique and still unfit for citation. A wall of adjectives — «premium quality», «modern design», «will become a reliable helper» — stays unique right up until it is compared with another wall exactly like it. There is nothing to extract from it: not a single statement you could drop into an answer and still be right.
A generative system builds its answer out of fragments. It needs a sentence that makes sense detached from the page. If the description contains no such sentence, there is nothing to take, however much unique text is sitting there.
What makes a description fit to be quoted
There are not many signals at work here, and every one of them can be checked by eye.
A direct answer in the first sentence
The first sentence has to answer a question rather than greet the visitor. «Welcome to the world of quality tools» carries no fact. «An 18 V cordless screwdriver with 45 N·m of maximum torque — for assembling furniture and driving into wood and plasterboard» answers straight away: what it is, the key figures, what it is for.
The important detail is self-sufficiency. The sentence has to remain clear if it is pulled off the page and dropped into someone else's text. That is exactly how it will be treated.
Subheadings phrased as questions
A person asks «how long does the battery last». If the description carries the subheading «How long does the battery last?» with a paragraph and a figure under it, matching the question to the fragment is easy. If it carries «Technical features», the matching has to be guessed.
Fact density
How many verifiable statements there are per paragraph. Material, size, weight, power, compatibility, composition, standard — anything that can be confirmed or refuted. An adjective without a number adds nothing to fact density.
Explicit entities
The brand, the model, the standard, the unit of measurement, the connector type have to be named rather than implied. «Fits most modern phones» is not an entity. «USB-C connector, Power Delivery 3.0 support» is.
One product: before and after
A made-up example, so the difference is visible at once.
Before. «This wonderful tool will become a reliable helper in your home. High quality, modern design and an affordable price. Order right now!»
After.
An 18 V cordless screwdriver with 45 N·m of maximum torque — for assembling furniture and driving into wood and plasterboard.
What are the power and the torque? An 18 V motor, 45 N·m of maximum torque, 20 clutch settings plus a drilling mode.
How long does the battery last? A 2.0 Ah lithium-ion battery, a full charge takes an hour. One battery and a charger are included.
What jobs is it for? Wood, plasterboard, plastic, thin metal. A 10 mm keyless chuck for standard bits and drills.
The second text is longer but not more watery — it simply contains more of what can be checked. That is the rebuild GEO mode performs: it does not add pretty words, it moves the facts forward and lays them out under questions.
How to measure it without fooling yourself
The most common way to judge «AI text quality» is to ask the same model that wrote the text for a score. The result is predictable: the model praises its own work, and the number comes out slightly different every time.
In Textory the GEO score is built differently. It is a deterministic heuristic from 0 to 100: it counts the presence of a direct answer at the start, question-shaped subheadings, fact density, explicitly named entities, a specification list and length, at fixed weights. There is no model inside the score at all.
Two practical things follow. First: the same text always gives the same number, so «before» and «after» can be compared honestly. Second: scores of different products are comparable with each other, so you can see which categories are lagging rather than only the average temperature.
The score appears twice — in the live preview of a single product before the whole catalogue is launched, and in the card of every processed product. On a demo catalogue on 5 September 2026 we saw 77 out of 100 in the preview and 82 out of 100 in the product card; those are example values, not a benchmark and not a promise of what your feed will produce.
What the GEO score does not show
Here we have to be just as concrete, because this is the point where the exaggeration usually starts.
The score does not predict rankings and does not calculate any probability of a mention. It knows nothing about the authority of your domain, the age of the page, links, data freshness, or which sources the model happened to pull this time. All of that sits outside the text and outside our influence.
And separately: a high score on false facts is worse than a low one. The heuristic counts the density of statements, not their truth. If a specification the product does not have gets into the description, structure will only make that error more visible — in search and in returns alike. That is why a description is built from your specifications rather than invented.
The honest limit
Nobody — ourselves included — can promise a mention in an AI answer.
GEO mode gives structure that systems find easier to parse and quote. Whether you specifically get quoted is decided by the model, and it depends on dozens of factors outside the description. Anyone selling «a mention in ChatGPT within N days» is selling something they do not control.
How to check your own page by hand
You do not have to upload a catalogue anywhere to understand the state of your texts. Among the free tools there is a page citability check for LLMs — eight objective checks that run on the server and invent nothing. Take two or three product cards that look fine to you and see what comes out. The surprise is usually the same one: the first sentence answers nothing.
Who gains the most from this
Two kinds of catalogue.
The first is niches where people ask «recommend», «compare», «which one should I pick». Home appliances, tools, children's goods, cosmetics with an ingredient list. Those phrasings go to generative systems more often, because answering them with a list of links is awkward.
The second is products whose specifications exist in the data in the first place. If the feed holds material, size, compatibility and composition, fact density is drawn from them. If the feed is empty, GEO mode has nothing to build an answer from — and admitting that upfront is more honest than ending up with invented figures in a description.
What GEO does not replace
It does not cancel the technical part. If the catalogue has duplicate descriptions, no structure and empty meta fields, that is where to start — otherwise fact density grows on pages that still add nothing new to the index.
The order is this: first remove the technical loss across the whole catalogue, then raise fitness for citation. The first is a bulk rewrite, the second is a separate toggle on top of it. Together they work; the first without the second simply leaves you in the old paradigm, and the second without the first runs into the duplicates.
In summary
GEO is preparation, not a guarantee. You make the text such that a fact is easy to lift out of it, and you get a number that shows whether it actually got better. Everything after that is not decided by the description.
You can look at GEO mode and the score on your own product at textory.com.ua — the pricing model there follows the volume of text, and the current terms are on the product site. Our other products are in the own products section. If you would rather have the work done on your catalogue than configure it yourself, we describe that format of task on the AI product description generation page. We reply within two hours and work under a contract.
