Skoup tracks your products inside the answers of ChatGPT, Claude, Gemini and Copilot: which ones come up on your category's shopping queries, at what position, against which competitors — and what the models get wrong about your prices and stock.
Every week, Skoup replays your shopping queries on four models, in English and French, and checks every claim against your catalog.
Average position 2.6 · 3rd behind Aravis and Kolven.
Your “lifetime frame warranty” claim is missing from 61% of answers.
Wrong price, invented stock-out, incorrect battery spec.
Hundreds of shopping questions replayed every week on four models, in English and French. Which products come up, at what position, which sources the AI relies on, and what your listings look like in ChatGPT Shopping.
An alert as soon as a model quotes a wrong price, declares a product out of stock or invents a feature. Then concrete fixes, ranked by impact: fill in missing barcodes, expose real stock levels to crawlers, correct outdated prices.
Traffic and orders coming from AI surfaces, reconciled with your shelf share. What the channel brings in, and what each fix changed on the curve.
AI visibility tools come from reputation monitoring: they count brand mentions. An online retailer needs to know which SKU is recommended, at what price, against whom.
“Road e-bike under $4,000”, not “your brand is being talked about”.
The gap between what the model claims and your catalog: prices, stock, specs, promotions. Every gap is a sale that slips away.
Continuous audit of your feed, schema.org markup, crawlability and eligibility for in-conversation checkout protocols.
One-click Shopify app, WooCommerce and PrestaShop extensions. The synced catalog is the source of truth: no manual setup.
Models don't recommend the same brands in the US, France and Germany. Each market is measured and compared separately.
Several brands in one workspace, roles, white-label PDF reports generated automatically every Monday.
A growing share of purchase decisions starts with a question asked to an assistant: “which brand of X is reliable”, “alternatives to Y”, “the best Z under $200”. At that exact moment, three things can happen. Your products get recommended. A competitor's do. Or the assistant describes your items wrong — a price from eight months ago, a stock-out that doesn't exist, an invented feature. None of these three situations is visible from your back office, and none shows up in your acquisition reports.
Skoup generates shopping queries from your catalog, replays them several times a week on each model, then extracts from every answer the products cited, their rank and the sources used. Repetition is essential: the same question asked ten times doesn't give the same answer ten times. Figures are therefore shown with their confidence interval, not as a single value.
An SEO tool tracks positions on a results page. A brand monitoring tool counts mentions. Skoup works at the product level: which SKU is recommended on which shopping query, at what price it's shown, what's wrong, and what fixing it brings in.
Yes, and it's the starting point rather than an option. Models rely on different sources depending on the language and don't recommend the same brands in English and in French. Both languages are measured and compared, market by market.
Shopify first, through a one-click app that syncs catalog, prices and stock. WooCommerce and PrestaShop follow. For other platforms, a product feed import lets you get started.
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