Let AI go through an entire catalogue and it finds the things that get lost in day to day operations: products with an empty or generic description, missing specifications, price anomalies such as a margin that collapsed after a cost increase, duplicate copy across variants, and dead items that have not sold in months. Tom in Behio does it in minutes over the real data in your catalogue, turns it into an overview and proposes what to do. The sweep that follows is a model example for the sake of illustration, not the data of any particular store. Price changes and deletions still happen only after your confirmation.
Comparison table
The facts in one place first. Figures as of July 2026.
| What the sweep finds | Why it matters | What to do |
|---|---|---|
| Empty and generic descriptions | worse SEO and conversion | draft from specifications, then review |
| Missing specifications | loses in filters and feeds | fill in structured data |
| Duplicate variant copy | thin content for Google | differentiate with unique content |
| Margin collapsed after a cost rise | money quietly lost | straighten prices after confirmation |
| Dead products | locked stock and attention | clear, withdraw, stop reordering |
The numbers in this article are a model example for the sake of illustration, not the data of any particular store. Your result will differ, because Tom reads your store's real catalogue.
The mess you cannot see because it is everywhere
Every catalogue that has been running for a few years carries a quiet mess. Not because anybody was careless, but because products got added on the move, cost prices shifted and there was never time for a tidy up. Empty descriptions. A specification missing on half the products. A price that drifted away from its margin. An item that has not sold in a year and still takes up space.
Nobody goes through that by hand, because hundreds or thousands of items are beyond one person. AI can. Let me show you what happens when you let Tom go through an entire catalogue. To be fair about it: the sweep below is a model example for the sake of illustration, not the numbers of any real store. Yours would look different.
Empty and generic descriptions
The first thing a sweep pulls out is descriptions. Not only the completely empty ones, but the ones that are empty of content. A high quality product for demanding customers says nothing, interests neither a person nor a search engine, and with AI search engines it takes you out of the running for a citation. Tom can flag both products with no description and products full of padding where a rewrite from real specifications would help.
Model example (not the data of any real store): out of eight hundred products, one hundred and twenty have an empty description and another ninety have text shorter than two sentences without a single specification. Tom lists them and offers to draft copy from the specifications that are in the catalogue. You read the text and approve it. Why AI descriptions only help SEO when they are unique and specific is covered separately.
Missing specifications and duplicates
The second thing is specifications and structured data. When a product is missing its material, dimensions or weight, it loses in filters, in comparison shopping feeds and in AI answers, because the machine does not know what you are selling. The sweep shows which products are missing which fields.
Duplication is related. Across variants of the same product, say the same shirt in five colours, it is easy to end up with nearly identical text. Google reads that as thin or duplicate content. Model example (not the data of any real store): forty variants share one and the same description word for word. Tom flags them and proposes how to differentiate them so each one carries something of its own.
Price anomalies and collapsed margin
This is the most valuable part of the sweep to my mind, because it is money directly. Tom calculates the margin from cost and selling price on every product and finds where something is wrong. Products under ten percent margin. Items where the cost price has risen since the last edit, so the margin collapsed without anybody noticing. Occasionally even a product being sold below cost by accident.
Model example (not the data of any real store): on twelve products the margin has fallen below ten percent, because on five of them the cost went up and the price stayed put. Tom lists them and offers two routes, straighten out the prices or clear slow items with a targeted discount. But note, he will not run a bulk price change himself, he shows the old and new prices and waits for your confirmation. What that dialogue looks like is written up in the article on repricing a catalogue.
Dead products taking up space
The last category is dead items. Products that have not sold in months but still hang in the catalogue, hold stock and dilute attention. Tom can flag them from sales and stock data and split them up: what to clear at a discount, what to withdraw, what to simply stop reordering.
Model example (not the data of any real store): fifteen products have not sold once in the last quarter, yet they are sitting on tens of thousands of koruna in stock. Those amounts are in Czech koruna, since Behio's home market is the Czech Republic, and what carries across borders is the ratio rather than the sum. That is money locked in a shelf. The sweep will not fix it for you, but it finally makes it visible, which is the first step. We have a separate view on dead stock and when to clear it in the operations section.
How the whole thing runs and what comes next
In practice the sweep is one sentence. You tell Tom to go through the catalogue and show you where the problems are, and over the real data he assembles an overview: how many products have an empty description, where specifications are missing, where margin has collapsed, what is not selling. Not an estimate, but a list of specific items, because he is reading your store's actual data.
And then comes the human part. A sweep is a diagnosis, not a treatment. It shows what is wrong and offers steps, but the decisions and the sign off are yours. You read the descriptions, you approve the prices, you weigh up a clearance on dead items. It strikes me as the ideal division: AI takes the tedious hunt for a needle in a haystack, the human keeps the decision. Half a day in spreadsheets shrinks to a few minutes and one coffee.
The verdict
Letting AI go through an entire catalogue is like letting somebody into the shop who has the time and patience to inspect every single item. It finds empty and generic descriptions, missing specifications, duplicated variants, collapsed margin and dead stock, the mess that gets lost in day to day operations because it is everywhere. Tom in Behio manages it in minutes over real data and turns it into an overview with proposals. The numbers above are deliberately illustrative and yours will differ. Keep the treatment for yourself, though: AI shows what is wrong, and prices and deletions happen only after you sign off. A diagnosis in minutes instead of half a day, that is the win.
Common questions
Products with an empty or generic description, missing specifications, duplicate copy across variants, price anomalies such as margin collapsing after a cost increase, and dead items that have not sold in a long time. Tom in Behio assembles it into an overview over the real catalogue data and proposes what to do with each group.
No. The sweep in the article is a model example for the sake of illustration, not the data of any particular store. It exists only to show what a sweep can uncover. On your side Tom reads your store's actual catalogue, so the result will be different.
Only the safe and reversible ones, like drafting descriptions for approval. Irreversible actions, above all bulk price changes and deletions, happen only after your confirmation, with the impact shown first. A sweep is a diagnosis and a proposal, the decision stays with you.
Minutes, because the AI reads the data directly in the platform rather than clicking around like a person. By hand, going through hundreds or thousands of items and calculating margins on each would mean half a day in spreadsheets. That is where the value sits: AI takes the tedious hunt, you keep the decision.
For margin analysis, yes, since Tom calculates it from cost and selling price. On products with no cost price he cannot work out a margin, but he still spots empty descriptions, missing specifications, duplicates and poor sellers without it. The more complete your catalogue data, the more a sweep shows you.
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