# ShopToAI: full content > ShopToAI is an Answer Engine Optimization (AEO) tool for WooCommerce stores and the agencies that run them. It checks each product's data the way AI assistants such as ChatGPT, Perplexity and Gemini read it and proposes the missing brand, category and attributes. Fixes you approve go into the JSON-LD markup and product feeds we serve, and you can undo any change. ## What it does - AEO, the product data layer: JSON-LD, GTIN, brand, attributes, Google and AI agent feeds - Content and crawler access, often called GEO: hosted llms.txt, FAQPage markup and AI crawler access in robots.txt. Writing FAQ and buying guides for the store comes with the Growth and Agency plans - An AI-readiness score from 0 to 100 for each store - Fixes proposed by AI. By default each one waits for the merchant's approval. Approved brand, category and attributes go into the JSON-LD markup and feeds we serve and leave the WooCommerce product fields alone. Approved fixes to the description, short description, SKU and GTIN are written to WooCommerce. Every change keeps its old value so it can be undone - AI-answer monitoring (from Starter): tracks whether ChatGPT, Perplexity and Gemini name a store's products and whether the quoted price still matches the catalog. Brand monitoring in AI answers comes with the Agency plan - For WooCommerce stores with the plugin: orders that start from a link in an AI assistant, by day, and visits from AI crawlers - A product feed file prepared for ChatGPT shopping, which the merchant submits to OpenAI themselves - A WordPress plugin you connect by pasting two codes from the dashboard (https://wordpress.org/plugins/shoptoai-connector/), and one panel for agencies that run up to 15 client stores This file holds the full English text of every content page. The index at /llms.txt lists the same pages as links. # Pricing and plans https://shoptoai.com/pricing Starter, Growth and Agency plans, monthly or annual. The free AI-readiness check audits up to 100 products, no login needed. # Guides and articles ## Answer Engine Optimization (AEO): the complete guide https://shoptoai.com/guides/answer-engine-optimization What Answer Engine Optimization (AEO) is and which product data AI assistants read before they answer a shopper. Search is moving from a list of blue links to a single spoken answer. People ask ChatGPT, Perplexity or Google's AI for the best option, and they get one short recommendation instead of ten tabs to compare. Answer Engine Optimization is how you make sure your product is the one the assistant names. This guide explains what AEO is, how it differs from classic SEO, and what an AI assistant actually reads before it recommends anything. ### The short version Answer Engine Optimization (AEO) is the practice of structuring your content and product data so AI assistants can read it, trust it, and quote it in their answers. For a store, that means clean structured data, real identifiers like GTIN and brand, a clear offer, and content written as direct answers. Get those right and you become eligible to be recommended. Miss them and the assistant recommends someone it understands better. ### AEO vs SEO Classic SEO optimizes for a ranking: you want to be position one on a results page a human scans. AEO optimizes to be the answer: there is often no page of results, just a recommendation, and either you are in it or you are not. The overlap is real. Good SEO hygiene (fast pages, clean markup, clear titles) still helps. But AEO adds requirements SEO never cared much about: - **Machine-readable facts.** Price, brand, GTIN, availability in structured data, not just visible text. - **Answer-shaped writing.** A direct answer up top that a model can lift, with detail below. - **Trust signals a model can verify.** Consistent data across your site and feeds, so nothing contradicts. There is no "page two" to fall back to. Being second best on an answer usually means being invisible. ([The full AEO vs SEO breakdown](https://shoptoai.com/blog/aeo-vs-seo).) ### What an AI assistant actually reads An assistant does not experience your design. It reads the data behind the page and the feeds you expose: - **Structured data (JSON-LD).** The Product block with name, brand, price, currency, availability. This is the backbone. ([Which fields matter](https://shoptoai.com/blog/product-schema-json-ld).) - **Identifiers.** GTIN and brand are how it matches your item to what the shopper asked for. - **Feeds.** A structured product feed kept in sync, so agents that pull data get current facts. - **Crawler access.** If your robots rules block the AI crawlers, none of the above is ever read. If those are missing or contradictory, the safe move for the assistant is to skip you. ### AEO and GEO: the two layers You will see both terms. AEO is the data layer: making your facts machine-readable so assistants can answer with them. GEO (Generative Engine Optimization) is the meaning layer: being present, cited and recommended inside the generated answer itself. AEO is the foundation. GEO is what you build on top once the data is clean. ### How to start 1. **Audit what you have.** Find out which structured data, identifiers and feeds are missing before you fix anything. Run a free check and get a 0 to 100 AI-readiness score. [Check your store](https://shoptoai.com/scan). 2. **Fix the data.** Valid Product JSON-LD, real brand and GTIN, a clean offer, one source of truth for your schema. 3. **Keep a feed in sync.** So agents always read current facts. 4. **Write answers, not fluff.** Direct answers a model can quote, with an FAQ. Each assistant reads your data a little differently: [Perplexity](https://shoptoai.com/blog/get-products-in-perplexity-shopping), [Google AI Overviews](https://shoptoai.com/blog/products-in-google-ai-overviews), [Microsoft Copilot](https://shoptoai.com/blog/products-in-copilot-shopping). If your store runs on WooCommerce, the step-by-step version is here: [How to optimize a WooCommerce catalog for AI shopping assistants](https://shoptoai.com/guides/woocommerce-ai-shopping). ShopToAI does the audit, the fixes and the feed for you, with every change reversible. See [pricing](https://shoptoai.com/pricing). --- ## How to get WooCommerce products recommended by AI shopping assistants https://shoptoai.com/guides/woocommerce-ai-shopping AI assistants only recommend products they can read. Five structured-data fixes that make every WooCommerce product eligible to appear in ChatGPT, Perplexity and Gemini answers. When a shopper asks ChatGPT for "the best waterproof hiking boots under 150", the assistant returns a short list of specific products. Being on that list is not about having the nicest photos or the most reviews. It is about whether the assistant can read your product data with enough confidence to recommend it. This guide covers the five structured-data fixes that move a WooCommerce store from invisible to eligible. Each fix targets a concrete gap in the machine-readable layer that AI assistants actually use. New to the topic? Start with [the complete guide to Answer Engine Optimization](https://shoptoai.com/guides/answer-engine-optimization). ### The short version AI shopping assistants pick products they can read with confidence: a clear price, a real brand, a GTIN, and clean structured data (JSON-LD) on every product page. WooCommerce and popular SEO plugins get you part of the way, but they often ship incomplete or duplicated markup. Fill the gaps and you become eligible to be recommended. ### Why assistants skip most WooCommerce products An assistant does not browse your store the way a person does. It reads the machine data behind the page. When that data is missing or contradictory, the safe move for the assistant is to recommend a competitor it understands instead. The usual gaps: - **No structured data, or broken structured data.** The theme prints a price for humans, but there is no valid Product JSON-LD for machines. - **Duplicate schema.** WooCommerce core and an SEO plugin both output Product markup, and they disagree. The assistant sees conflict and backs off. - **Missing identifiers.** No GTIN, no brand. The assistant cannot match your product to the thing the shopper asked for. - **Vague offer data.** Price, currency and availability are not stated in a way a machine can trust. For a shopper's-eye view of this, see [why your WooCommerce products aren't showing up in ChatGPT Shopping](https://shoptoai.com/blog/products-not-in-chatgpt-shopping). ### The five things to fix 1. **Valid Product JSON-LD on every product page.** Name, description, image, brand, price, currency, availability. This is the single biggest lever. 2. **A real brand and GTIN on each product.** These are how an assistant matches your product to a query. Never invent a GTIN. Fill the ones you have and flag the ones you are missing. 3. **A clean, unambiguous offer.** One price, one currency, a clear in-stock or out-of-stock signal. 4. **A feed AI agents can read.** A structured product feed, kept in sync with your catalog, so agents that pull data get current information. ([What to include](https://shoptoai.com/blog/woocommerce-product-feed-ai).) 5. **No duplicate or conflicting markup.** One source of truth for your Product schema, with the theme and other plugins muted. Not sure which gap is yours? Start with [why a product doesn't show up in AI answers](https://shoptoai.com/blog/why-product-not-in-ai-answers), then see [which attributes to add in WooCommerce](https://shoptoai.com/blog/woocommerce-product-attributes-ai). ### How to check where you stand You do not have to guess. Paste your store address or one product URL and in about 30 seconds you get a free 0 to 100 AI-readiness score for a sample of your products, with the specific issues listed. [Check your store now](https://shoptoai.com/scan). You don't need an account or a card. Connect your store to audit the whole catalog. ### How ShopToAI helps ShopToAI audits your WooCommerce catalog against these rules, then fixes the gaps for you in the markup and feeds it serves. Our plugin places the correct JSON-LD and mutes the duplicates from Woo core and your SEO plugin, so there is one source of truth. Every change is reversible, and the AI-ready feed stays in sync as your catalog changes. See the [plans and pricing](https://shoptoai.com/pricing). --- ## How agencies can sell and deliver AEO as a service https://shoptoai.com/guides/aeo-for-agencies AEO is a new service line that sits next to your SEO work. How to package it, price it and deliver it across many client stores from one dashboard. Your clients are about to ask why their products never come up when a shopper asks ChatGPT. Most agencies do not have an answer yet. That gap is a service line waiting to be sold, and it fits neatly next to the SEO and paid work you already do. This guide covers how to package AEO as a service, how to price it, and how to deliver it across many client stores without drowning in manual work. ### Why now AI assistants are becoming a real shopping channel, and almost no store is ready for it. That means two things for an agency: clients will start asking about it, and the ones who move first will look like they saw it coming. Being early with a clear offer beats being right but late. ### Packaging it as a service Keep it simple to sell. Three shapes work well: 1. **AEO audit (one-off).** A fixed-fee readiness report per store: the AI-readiness score, the issues, and what fixing them is worth. Low commitment, easy yes, and it qualifies the client for the ongoing work. 2. **AEO setup (project).** Fix the structured data, identifiers and feeds, stand up the plugin, verify the store is readable. A defined project with a clear finish line. 3. **AEO retention (monthly).** Keep the catalog in sync as products change, monitor visibility across assistants, report monthly. This is the recurring revenue. ### How to price it Price on the value, not your hours. A store that starts getting recommended by AI assistants is winning sales it was invisible for before, so the audit and setup carry real weight. A rough shape that sells: - Audit: a fixed fee per store, low enough to be an easy first yes. - Setup: a project fee that scales with catalog size. - Retention: a monthly fee per store, or a bundle across the client's stores. #### The maths, with real numbers A European agency charges roughly €500 to €700 a month to do SEO for a small online store, €700 to €1,400 for a growing one, and €1,400 to €2,800 or more for a large or competitive catalog. Those figures come from the cheaper end of the market, so if you sell in Germany, the Netherlands or the UK, read them as a floor. Now put AEO next to that. The Agency plan covers up to fifteen client stores for one monthly fee, so the cost per store is a small part of what you already bill for SEO. Current prices are on the [pricing page](https://shoptoai.com/pricing). So add AEO as one more line on the invoice, at even €90 per client. Fifteen clients at €90 bring in **€1,350 a month of new revenue**. Compare that with one plan for all fifteen stores. And the work is automated, so that revenue does not cost you a hire. This is why AEO packages so well: it is a new line on an invoice the client already pays, at a tooling cost that rounds to nothing against your retainer. See [pricing](https://shoptoai.com/pricing). ### Delivering across many stores The trap is doing this by hand, store by store. It does not scale and it is easy to break a catalog with a bad edit. What you want: - **One dashboard for every client store**, not fifteen logins. - **Reversible fixes**, so a wrong change is one click back, never a restore-from-backup. - **Auto-apply for safe gap-fills**, so an empty brand that the product name already states fills in without hand-editing. - **A clear before and after**, so each client sees the lift you delivered. That is exactly what the Agency plan is for: up to fifteen client stores under one roof, with reversible fixes and a before-and-after score for each. ### Reporting to clients Clients pay to see progress. Show the AI-readiness score climbing, the issues closed, and where the store now appears when you ask assistants the questions its customers ask. A simple monthly one-pager per store is enough, and it makes the retention fee obvious. For the monthly report itself, see [how to report AI visibility to clients](https://shoptoai.com/blog/report-ai-visibility-to-clients). If you're choosing a tool, read [what agencies need from an AEO tool](https://shoptoai.com/compare/aeo-tools-for-agencies). ### Where to start Connect one client store for a [free catalog audit](https://shoptoai.com/register) and see the score and issues for yourself, or start with the [free store check](https://shoptoai.com/scan). Grab [the AEO audit checklist](https://shoptoai.com/blog/aeo-audit-checklist) to run on any store, read [the complete guide to AEO](https://shoptoai.com/guides/answer-engine-optimization) for the fundamentals, and see [pricing](https://shoptoai.com/pricing) for the Agency plan. --- ## AEO vs SEO: what changes when AI answers https://shoptoai.com/blog/aeo-vs-seo SEO optimizes to rank on a page of links. AEO optimizes to be the single answer an assistant speaks. What carries over, what AEO adds, and the mindset shift. SEO and AEO look like the same job under a new name. They aim at two different moments: a page of results a person scans, and a single answer a machine speaks. The sections below cover what changes and what stays the same. ### The core difference SEO optimizes for a ranking. You want to be near the top of a results page, because the human doing the searching will scan a few links and click one. There is a second page, a third, a long tail of chances. AEO optimizes to be the answer. When someone asks ChatGPT or Perplexity for the best option, they often get one recommendation, not ten links. Either your product is in that answer or it is invisible. There is no page two to fall back to. ### What carries over from SEO AEO builds on SEO, and the basics still count: - Fast, crawlable pages. - Clean, valid markup. - Clear titles and descriptions that match real questions. - Genuine, useful content rather than keyword filler. If your SEO is a mess, your AEO will be too. Good SEO is where AEO starts. ### What AEO adds AEO cares about things classic SEO treated as optional: - **Machine-readable facts.** Price, brand, GTIN and availability in structured data, not just visible on the page. Most product pages that AI assistants cite carry structured data; pages without it get read fragmentarily or skipped. ([Which fields matter](https://shoptoai.com/blog/product-schema-json-ld).) - **Answer-shaped writing.** A direct answer up top that a model can lift cleanly, with the detail below it. - **Consistency a model can trust.** Your data across the page, your feed and your other pages should agree. Contradictions make an assistant back off. - **Access for the right crawlers.** Being cited depends on letting the retrieval crawlers read you, which is a different setting from the training crawlers. ### The mindset shift SEO asks "how do I rank for this query?". AEO asks "if an assistant answered this question out loud, would it name us, and could it read enough to be sure?". You are competing to be the sentence the assistant says. The work overlaps enough that you are not starting over. Clean data and clear answers help both. Start with [the complete guide to AEO](https://shoptoai.com/guides/answer-engine-optimization), then check your store for the gaps: [run the free check](https://shoptoai.com/scan). --- ## Product schema (JSON-LD) for AI shopping: the fields that matter https://shoptoai.com/blog/product-schema-json-ld You do not need every schema.org property. The required Product/Offer fields, recommended ones that help assistants compare products, and availability values. Structured data is the single biggest lever for getting a product read by AI assistants and Google's shopping surfaces. But you do not need every property under the sun. A handful carry almost all the weight. This is the practical list, split into what is required and what is worth adding. The markup we are talking about is a Product block in JSON-LD, with a nested Offer, placed on each product page. Assistants and search engines read it to know what you sell, for how much, and whether it is in stock. ### The fields that are required Google will not treat a product as a merchant listing without these. Get them right first. | Field | Notes | |-------|-------| | name | The product title. | | image | One or more image URLs. Multiple, high-resolution is better. | | offers | A nested Offer object where you are the seller. | | offers.price | The current price. For a merchant listing it must be greater than zero. | | offers.priceCurrency | Three-letter ISO 4217 code, like EUR or USD. | That is the floor. A page with only these is eligible; a page missing any of them usually is not. ### The fields worth adding These are recommended, not required, but they are what separates a product an assistant recommends from one it skips. They help it match your item to a shopper's question and trust the result. | Field | Why it matters | |-------|----------------| | brand | The Brand name. Assistants match on it constantly. | | gtin (or gtin8/12/13/14) | The global identifier. Use the most specific one you have. Never invent it. | | mpn | Manufacturer part number, when there is no GTIN. | | sku | Your own identifier. No whitespace. | | offers.availability | In stock or not, in a value a machine trusts (see below). | | aggregateRating and review | Ratings and reviews, if you genuinely have them. | | offers.shippingDetails | Shipping cost and speed. | | offers.hasMerchantReturnPolicy | Your return policy. This one is quietly important (see below). | ### Availability: use a real value, not free text Availability must be one of the fixed schema.org values. Use the one that fits: - InStock - OutOfStock - BackOrder - PreOrder - Discontinued - LimitedAvailability - SoldOut Writing "usually ships in 2 days" in this field does nothing. The machine wants one of the values above. ### Why return policy and shipping matter ChatGPT builds its shopping answers from what it reads on retail pages and from product feeds merchants submit to OpenAI. When several stores sell the same item, availability, price and who the maker or main seller is can all matter. A clear return policy and shipping terms are facts a shopper wants, and hasMerchantReturnPolicy and shippingDetails put them where a machine can read them. Google's merchant listing documentation recommends both. ### One source of truth More markup is not better if it contradicts itself. A common failure is two Product blocks on the same page, one from your theme or WooCommerce core and one from an SEO plugin, disagreeing on price or availability. An assistant that sees conflict backs off. Pick one source for your Product schema and mute the rest. Check a sample of your products for free, then connect your store to see the gaps listed product by product. [Check your store](https://shoptoai.com/scan). If you are on WooCommerce, the full setup is here: [How to optimize a WooCommerce catalog for AI shopping assistants](https://shoptoai.com/guides/woocommerce-ai-shopping). --- ## GTIN, brand and identifiers: why AI assistants skip products without them https://shoptoai.com/blog/gtin-brand-identifiers GTIN and brand are how an assistant matches your product to what a shopper asked for. What a GTIN is and what to do when your products don't have one. When a shopper asks an AI assistant for "the best cordless drill under 100", the assistant has to match that request to real products it can identify. The identifiers on your product, GTIN and brand, are how it does that. Miss them and your product is harder to place, so it gets passed over for one the assistant can pin down. ### What a GTIN is GTIN stands for Global Trade Item Number. It is the standard product number managed by GS1, and it is what sits under the barcode on retail products. You have probably met it under other names: - **UPC** is a GTIN-12 (common in North America). - **EAN** is a GTIN-13 (common in Europe). - There is also GTIN-8 for small packages and GTIN-14 for cases and multipacks. They are all GTINs of different lengths. One product, one GTIN. ### Why assistants and search lean on it A GTIN is unambiguous, unlike "Blue running shoe". It lets an assistant know your listing and a competitor's listing are the same physical item, compare them fairly, and trust that the product is real. Google's guidance is to use the most specific GTIN that applies to your product, because it is the most accurate representation. The same identifier works across assistants, so you fill it once and benefit everywhere. ### The one rule: never invent one A made-up GTIN is worse than no GTIN. Identifiers can be checked against GS1, and a wrong one marks your data as untrustworthy, which is the opposite of what you want. If you do not have a GTIN for a product, leave it empty and lean on the other identifiers. ### When there is no GTIN Some products genuinely have none: custom, handmade, or your own private-label goods. That is fine. Use what you do have: - **brand**: the real brand or your own brand name. - **mpn**: the manufacturer part number. - A clear, specific **name** and solid structured data. Step by step: [how to add GTIN to WooCommerce products](https://shoptoai.com/guides/add-gtin-in-woocommerce). Brand matters even when you have a GTIN. Assistants match on it constantly, so a missing or generic brand quietly costs you matches. ### How to check your catalog The gap is usually invisible until you look: products with no GTIN, no brand, or a brand set to something generic. Check a sample of your products for free, then connect your store to see the gaps listed product by product. [Check your store](https://shoptoai.com/scan). For the full WooCommerce setup, see [the guide](https://shoptoai.com/guides/woocommerce-ai-shopping); for the wider picture, [what AEO is](https://shoptoai.com/guides/answer-engine-optimization). --- ## The AEO audit checklist you can run on any store https://shoptoai.com/blog/aeo-audit-checklist A concrete AEO review: structured data, identifiers, offer clarity, feeds, crawler access and content. Covers the crawler setting most audits get wrong. Whether you are checking your own store or auditing a client's, an AEO review comes down to a few concrete questions. Here is a checklist you can run on any store, grouped so you can hand the results to a client without a lecture on schema. ### 1. Structured data - Does every product page have a valid Product block in JSON-LD? - Are the required fields present: name, image, and an offers object with price and priceCurrency? - Is there exactly one Product block per page, or are the theme, WooCommerce core and an SEO plugin each emitting one that disagrees? Duplicate, conflicting schema is one of the most common failures. One source of truth wins. ### 2. Identifiers - Does each product carry a real brand? - Is there a GTIN where one exists, using the most specific format available? - Where there is no GTIN, is mpn used instead? - Are any GTINs invented or clearly wrong? A wrong identifier is worse than a missing one. ### 3. Offer clarity - Is price stated as a number with a currency, not just visible text? - Is availability set to a real schema.org value (InStock, OutOfStock, PreOrder and so on), not a sentence? - Are shipping and return policy declared in structured data? Assistants read these as trust signals. ### 4. Feeds - Is there a structured product feed, and is it kept in sync as the catalog changes? - Does the feed agree with the on-page data, or do they contradict each other? ### 5. Crawler access (the one people get wrong) This is where audits often go sideways. AI crawlers split into two jobs, and they are different levers: - **Training crawlers** (for example GPTBot, ClaudeBot). Blocking these is a content-rights choice. It does not remove you from AI answers. - **Retrieval crawlers** (for example OAI-SearchBot, PerplexityBot). These build the indexes assistants cite from. Block these and you disappear from those assistants' answers. So check the robots rules with the right question: are the retrieval crawlers allowed to read the pages you want cited? Blocking a training bot to protect your content is fine. Accidentally blocking a retrieval bot is how a store goes invisible. ### 6. Content shape - Do key pages answer the question directly up top, with detail below? - Is there an FAQ with real questions and clean answers a model can lift? ### Turn the checklist into a score Going item by item across a whole catalog by hand is slow. Paste your store address or one product page into the free scan for a quick 0 to 100 read of a sample of your products, or connect the store for a catalog audit with the issues listed per product. On paid plans, Deep Scan then checks the live pages and gives you a report to hand a client. [Try the free store check](https://shoptoai.com/scan). If you are doing this for clients, see how the [Agency plan](https://shoptoai.com/pricing) covers up to fifteen stores from one dashboard, and read [how agencies sell AEO](https://shoptoai.com/guides/aeo-for-agencies). --- ## How to measure AI visibility (and not fool yourself) https://shoptoai.com/blog/measure-ai-visibility AI visibility takes more than one number. The method: a repeatable prompt panel per assistant, and mentioned versus recommended versus cited. You fixed your data and you want to know if it worked. AI visibility is measurable, but one reading tells you little. Track it over time and per assistant. The steps below show how to do it without fooling yourself. ### What "AI visibility" means It is how often AI assistants mention, cite or recommend you when someone asks a question in your category. People shorthand it as AI share of voice: the percentage of answers in your category that name your brand, relative to everyone named. Three words that get muddled: - **Mentioned**: the assistant names you in its answer. - **Recommended**: it puts you forward as a good option, not just a name in a list. - **Cited**: it uses your page as a source, usually with a link. You can be recommended without being cited, or cited without being recommended. They are different wins, so track them separately. ### The method You do not need a lab. You need a repeatable panel of questions: 1. **Write buyer-intent prompts.** Ten to twenty to start, the real questions your customers ask: "best waterproof hiking boots under 150", "which running shoe for flat feet". More prompts give a steadier reading. 2. **Ask each assistant.** ChatGPT, Perplexity and Gemini retrieve and evaluate sources differently, so run the same prompts on each and record per assistant. The overlap between what ChatGPT and Perplexity cite is small, so an average across them hides more than it shows. 3. **Record what you see.** For each answer: were you absent, mentioned, recommended or cited? If cited, which page? Where in the answer did you appear? Being named first carries far more weight than fifth. 4. **Repeat and watch the trend.** Run the same panel on a schedule. One run is a snapshot, not a verdict, and the sources assistants pull from shift a lot month to month. ### The trap: conclusions from one measurement Answers vary between runs of the same prompt, so a single good or bad result is noise. Watch consistency: how reliably you show up across repeated tests of the same question. Low consistency means the assistant has a weak link between your brand and the topic, which is a data and content problem, not luck. ### From measuring to fixing The visibility number shows the result. Structured data, identifiers and feeds are what move it. If the number is low, the fix is usually upstream: missing schema, weak identifiers, or a retrieval crawler that cannot read you. Start by finding those gaps with a [free store check](https://shoptoai.com/scan), then track the trend as they close. This is what our AI answer monitoring does: it runs a panel of questions about your products and category across assistants and tracks whether you are mentioned and recommended over time, so you are not copy-pasting prompts by hand. Product monitoring comes with every paid plan and brand monitoring with the Agency plan. See the [plans](https://shoptoai.com/pricing), and for the fundamentals, [what AEO is](https://shoptoai.com/guides/answer-engine-optimization). --- ## WooCommerce product feed for AI agents: what to include https://shoptoai.com/blog/woocommerce-product-feed-ai A product feed lets AI agents read your whole catalog at once, with current price and stock. What goes into it and why it has to stay in sync with the store. Structured data on your product pages is one way an assistant reads you. A product feed is the other, and for AI shopping it is becoming just as important. A feed is a single, structured file of your whole catalog that platforms pull on a schedule, so an agent does not have to crawl every page to know your current price and stock. ### On-page schema and a feed do different jobs They are not either-or. You want both. - **On-page JSON-LD** is read when an assistant looks at a specific product page. - **A feed** is pulled in bulk, kept current, and used to index and compare your whole catalog at once. It is also what powers direct checkout inside some assistants. If your schema is clean but you have no feed, you are relying on each page being crawled at the right moment. A feed removes that gamble. ### What an AI product feed contains The fields mirror what assistants need to trust and place a product. OpenAI's product feed for ChatGPT, for example, requires the essentials on every item: - **item_id**: a unique identifier for the product. - **title** and **description**: the name and full details. - **brand**: the manufacturer or brand name. - **price**: the current price with its currency. - **availability**: a fixed value like in_stock, out_of_stock, pre_order or backorder. - **image_url** and **url**: the main image and the link to the product page. - **seller_name** and **seller_url**: who is selling and where. Return policy, shipping and review data are recommended on top, and they help, especially where checkout happens inside the assistant. Google's Merchant feed asks for the same shape of data, so a good feed serves both channels. ### The part everyone gets wrong: staleness A feed is only useful if it is true. A feed that still says in_stock for a product you sold out of last week trains the assistant to distrust you, and it can send a shopper to a dead end. The feed has to stay in sync with your catalog as prices and stock change, and it has to agree with the JSON-LD on the page. Contradictions between the two are a red flag an assistant can see. ### Where the feed goes Two concrete channels today: - **Google Merchant** feeds, which feed Google's shopping and AI surfaces. - **OpenAI's merchant feed** for ChatGPT, which lets ChatGPT index your products and, where enabled, check out directly. More assistants are moving the same way, and they read the same kind of structured feed. ### How this works on WooCommerce Woo can export product data, but keeping a feed correct, complete and continuously in sync across a changing catalog is the hard part, and it has to match your on-page schema. ShopToAI generates the AI-ready feed and refreshes it from your catalog every day, together with the on-page JSON-LD, so the two tell the same story. Run the [free store check](https://shoptoai.com/scan), see the [WooCommerce guide](https://shoptoai.com/guides/woocommerce-ai-shopping) for the full setup, and [pricing](https://shoptoai.com/pricing) for plans. --- ## Fixing duplicate product schema in WooCommerce (Woo core, themes and Yoast) https://shoptoai.com/blog/fix-duplicate-schema-woocommerce WooCommerce core, your theme and an SEO plugin can each emit Product schema, and conflicting blocks make assistants back off. How to get to one clean source. More structured data is not better if it contradicts itself. On WooCommerce this is a common trap: your product page ends up with two or three Product blocks, from different sources, that disagree on price or availability. An assistant that sees conflicting markup does the safe thing and backs off. Here is why it happens and how to get back to one clean source. ### Why WooCommerce ends up with duplicate schema Several things on a typical store can each emit Product markup: - **WooCommerce core**, which outputs its own structured data. - **Your theme**, which sometimes adds its own. - **An SEO plugin**, such as Yoast or Rank Math, which builds a schema graph. - **Extra schema plugins** layered on top. Any two of these can produce a Product block, and they rarely agree perfectly. Duplicate Product schema shows up on a large share of store audits, so if you have not checked, assume you might have it. ### Why it hurts An assistant or search engine reading two Product blocks with different prices or availability cannot tell which is true. Contradiction reads as unreliable data, and unreliable data is a reason to recommend a competitor instead. One correct block beats three that argue with each other. ### How to fix it: pick one source The goal is a single Product block per page. A few paths: - **Use the official bridge.** The Yoast WooCommerce SEO add-on is built to merge WooCommerce and Yoast output into one unified schema graph rather than two competing ones. - **Turn off the extras.** If a plugin or theme is emitting a second block, disable its Product schema so only your chosen source remains. - **Filter it out in code.** Yoast exposes the wpseo_json_ld_output filter, so a developer can switch its schema off entirely when another source is authoritative: `add_filter( 'wpseo_json_ld_output', '__return_false' );` Whichever route you take, the rule is the same: decide which source owns your Product schema, and silence the rest. ### How to verify Do not assume it is fixed; check it. Run the page through Google's Rich Results Test and confirm there is exactly one Product block with the right price, availability and identifiers. Then re-check after any plugin or theme update, since those can quietly reintroduce a second source. An audit does this across your whole catalog at once and flags pages with duplicate or conflicting markup. [Check your store](https://shoptoai.com/scan). On another platform? See [Shopify vs WooCommerce for AEO](https://shoptoai.com/blog/shopify-vs-woocommerce-aeo). For the full WooCommerce setup, see [the guide](https://shoptoai.com/guides/woocommerce-ai-shopping); ShopToAI's plugin places one correct Product block and mutes the duplicates from Woo core and your SEO plugin, so there is a single source of truth. See [pricing](https://shoptoai.com/pricing). --- ## WooCommerce structured data checklist for 2026 https://shoptoai.com/blog/woocommerce-structured-data-checklist An ordered checklist for WooCommerce structured data: required Product/Offer fields, one source of truth, recommended identifiers, feeds and crawler access. Here is a concrete checklist for the structured data on a WooCommerce store, the kind you can work through in an afternoon and hand to a client. It is ordered from what is required to what is a bonus, so you fix the things that gate you first. ### Required: get these or you are not eligible - **A valid Product block in JSON-LD on every product page.** Not just visible price and text, actual machine-readable markup. - **name** on every product. - **image**, at least one, high resolution. - **offers** as a nested Offer object where you are the seller. - **offers.price**, a number greater than zero. - **offers.priceCurrency**, a three-letter code like EUR or USD. If any of these are missing, an assistant or Google will not treat the product as a real listing. Start here. ### One source of truth - **Exactly one Product block per page.** Check that WooCommerce core, your theme and your SEO plugin are not each emitting one that disagrees. Duplicate, conflicting schema is one of the most common WooCommerce problems. ([How to fix it](https://shoptoai.com/blog/fix-duplicate-schema-woocommerce).) ### Recommended: what gets you recommended - **brand** on every product. - **gtin** where one exists, using the most specific format. Never invent one. ([Why identifiers matter](https://shoptoai.com/blog/gtin-brand-identifiers).) - **mpn** where there is no GTIN. - **sku**, with no whitespace. - **offers.availability** set to a real value: InStock, OutOfStock, PreOrder, BackOrder and so on, not a sentence. - **offers.shippingDetails** and **offers.hasMerchantReturnPolicy**. Assistants read these as trust signals. - **aggregateRating** and **review**, only if you genuinely have them. ### Beyond the product page - **A product feed** kept in sync with the catalog, agreeing with the on-page markup. ([What the feed needs](https://shoptoai.com/blog/woocommerce-product-feed-ai).) - **Crawler access.** Confirm the retrieval crawlers (like OAI-SearchBot and PerplexityBot) are allowed to read your pages. Blocking a training bot is a content choice; blocking a retrieval bot removes you from AI answers. ([The full audit checklist](https://shoptoai.com/blog/aeo-audit-checklist).) - **An FAQ** on key pages, with real questions and clean answers a model can lift. ### Verify, do not assume Run product pages through Google's Rich Results Test to confirm one clean Product block with the right fields, and re-check after plugin or theme updates, which can quietly break things. To run this whole checklist across your catalog and get the issues listed per product, connect your store for a [free catalog audit](https://shoptoai.com/register). To try it on a sample of products first, use the [free store check](https://shoptoai.com/scan). The full setup is in [the WooCommerce guide](https://shoptoai.com/guides/woocommerce-ai-shopping), and plans are on the [pricing page](https://shoptoai.com/pricing). --- ## Shopify vs WooCommerce for AEO: which is better for getting recommended by AI https://shoptoai.com/blog/shopify-vs-woocommerce-aeo AEO works on both Shopify and WooCommerce, and the quality of your product data decides the result. How the two differ and what the work looks like on each. If you are choosing a platform, or wondering whether yours is holding you back: AEO works on both Shopify and WooCommerce. What decides your outcome is how clean your product data is, whatever platform runs the store. ### The requirements are the same on both Whatever the platform, an assistant needs the same things to read and recommend you: - Valid Product structured data on every product page, from a single source. - Real identifiers: brand, and a GTIN where one exists. - A clear offer: price, currency and a real availability value. - A product feed kept in sync with the catalog. - The retrieval crawlers allowed to read your pages. Get those right and you are eligible on either platform. Miss them and you are skipped on either platform. ### Where WooCommerce differs WooCommerce is open and flexible. You can control the raw markup completely, which is a strength, but that same openness is why schema often comes from several places at once: core, the theme, and one or more SEO plugins, which can duplicate and conflict. Nothing is locked away from you, but you do have to consolidate to one source and keep it that way. Freedom with responsibility. ([How to fix duplicate schema](https://shoptoai.com/blog/fix-duplicate-schema-woocommerce).) ### Where Shopify differs Shopify is more standardized and hosted. Many themes ship Product structured data out of the box, which can be a head start. The flip side is less low-level control: your markup depends on the theme and any apps, quality varies between themes, and editing the raw output is more constrained. You are trading some control for consistency. ### So which is better for AEO? Neither, inherently. A well-tended WooCommerce store beats a neglected Shopify one, and the reverse is just as true. The deciding factor is the data: - On WooCommerce, the usual work is consolidating and cleaning schema and filling identifiers. - On Shopify, the usual work is checking what the theme emits and filling the gaps it leaves. Different chores, same goal. Do not switch platforms for AEO alone; fix the data on the platform you have. ### How ShopToAI helps ShopToAI is built for WooCommerce: our plugin places one correct Product block with your approved fixes and mutes duplicates, and we serve your product feeds ourselves. Either way, start with a [free store check](https://shoptoai.com/scan), and read [what AEO is](https://shoptoai.com/guides/answer-engine-optimization) for the fundamentals. Plans are on the [pricing page](https://shoptoai.com/pricing). --- ## llms.txt explained: does your store need one? https://shoptoai.com/blog/llms-txt-explained What llms.txt is, why AI search crawlers mostly ignore it today, where it does get used, and why it is a small optional piece of AEO. llms.txt is getting a lot of attention, and much of it is hype. Below: what the file is, what the evidence says about whether it helps, and whether your store needs one right now. ### What llms.txt is llms.txt is a plain markdown file you put at the root of your site, at /llms.txt. It lists your most important pages and content in a clean, structured form that a language model can read without wading through your HTML, navigation and scripts. The idea was proposed by Jeremy Howard of Answer.AI in 2024, and there is a fuller variant, llms-full.txt, that inlines more of the content. Think of it as a carefully chosen site map written for machines. It is cheap to produce and does no harm. ### Does it help? This is where the hype and the evidence part ways. - **AI search crawlers mostly ignore it today.** The crawlers that build the indexes assistants cite from, like OAI-SearchBot and PerplexityBot, mostly skip /llms.txt and read your HTML directly. Adoption is still low, nowhere near robots.txt or sitemaps. - **There is no measured citation lift.** We know of no public study showing that adding llms.txt makes AI assistants cite a store more often. - **But some agents do use it.** Some coding assistants fetch llms.txt when they look up a project's documentation. So llms.txt is a cheap way to point AI agents at your key pages. Nobody has shown that it improves visibility in AI search, so treat it as an optional extra. ### Does your store need one? Add it, but for the right reason and with the right expectations: - Add it, because it is cheap, harmless and prepares your store for a web where more is done by AI tools. - Do not expect your products to suddenly appear in ChatGPT because of it. - Do not let anyone sell you llms.txt as an important part of AEO. It is a small, optional extra. ### What moves your AI visibility If your goal is to be recommended by assistants today, start with your structured data, your identifiers and your product feed, all clean and in sync. That is the product information assistants read. ([What AEO is](https://shoptoai.com/guides/answer-engine-optimization) covers the whole picture, and [which schema fields matter](https://shoptoai.com/blog/product-schema-json-ld) covers the data.) We host an llms.txt as a small extra. The real work is the data and the feed. Start by seeing where your store stands: [check your store for free](https://shoptoai.com/scan). --- ## How to report AI visibility to clients (a simple monthly template) https://shoptoai.com/blog/report-ai-visibility-to-clients How to report AI visibility to clients: a one-page monthly format with readiness score, closed issues, AI check, work done and next month's plan. A client pays for AEO and, a month in, asks the fair question: what did I get? If your answer is a shrug and some jargon, the retainer is at risk. This is a simple monthly report you can hand a client that shows progress in plain terms, per store. ### What a client wants to see Not schema. They want three things: is my situation better than last month, where do I now show up, and what did you do. Build the report around those. ### A one-page monthly template Keep it to a single page per store. Five sections: 1. **AI-readiness score, this month vs last.** One number, trending up. It is the headline: the store went from, say, 48 to 71. If you use an audit that returns a 0 to 100 score, this section writes itself. 2. **Issues closed.** A short count and list: duplicate schema removed on 120 products, GTIN added on 85, availability corrected on 40. Concrete, countable work. 3. **AI visibility check.** Take five to ten buyer questions in the client's category, ask them across ChatGPT, Perplexity and Gemini, and record whether the client's products were absent, mentioned or recommended. Show the change from last month. Tell the client plainly that this is a reading over time and comes with no guarantee. ([How to measure it properly](https://shoptoai.com/blog/measure-ai-visibility).) 4. **What we did.** Two or three plain sentences. No jargon. "We fixed the product data ChatGPT reads and kept your feed current." 5. **Next month.** One or two priorities, so the retainer has a forward story. ### A report you can repeat every month The report only works if you can produce it every month without it eating a day. That means the underlying numbers should come from a tool, not a manual audit each time: - The score and issue counts come from re-running the audit. - The visibility check comes from a saved set of prompts run across assistants. If you are doing this by hand for one store it is fine. Across ten client stores it is not, and that is where automation really earns its place. ### How ShopToAI helps ShopToAI gives you the score, the per-product issue list and monitoring across assistants in one dashboard, so the monthly report is mostly assembling numbers you already have. See [how agencies sell AEO](https://shoptoai.com/guides/aeo-for-agencies) for the service around it and [pricing](https://shoptoai.com/pricing) for the Agency plan. To try it first, [check your store for free](https://shoptoai.com/scan). --- ## Why your WooCommerce products aren't showing up in ChatGPT Shopping https://shoptoai.com/blog/products-not-in-chatgpt-shopping AI assistants recommend products they can read. If yours are missing, the cause is usually missing structured data, brand or GTIN. How to check and fix it. You sell good products, your store looks fine, and yet when you ask ChatGPT for something you clearly stock, it recommends someone else. This is one of the most common questions we hear, so here is the honest answer and what to do about it. ### The short answer ChatGPT and other AI assistants recommend products they can read with confidence. If your product pages lack valid structured data, a real brand, and a GTIN, the assistant cannot be sure what you are selling, so it plays it safe and names a competitor it understands. The fix is data, not design. ### The usual reasons, in order 1. **No valid Product JSON-LD.** Your theme shows a price to a human, but there is no machine-readable Product block, or the one that exists is broken. Without it there is little for an assistant to read, so fix this first. 2. **Duplicate, conflicting schema.** WooCommerce core and an SEO plugin both output Product markup and they disagree. Two blocks with different prices or names make the data ambiguous. 3. **Missing brand and GTIN.** Without these, the assistant cannot match your item to "the best X under Y" the shopper asked for. 4. **Unclear offer data.** Price, currency and stock status are not stated in a way a machine trusts. 5. **AI crawlers blocked.** Your robots rules quietly block the crawlers these assistants use, so they never read the page at all. Notice what is not on this list: your theme, your photos, your copywriting. All of those matter to people. The assistant reads the data behind them. ### What to do Start by finding out which of these apply to your products instead of guessing. Paste your store address or a product URL for a free 0 to 100 AI-readiness score with the specific issues listed. [Check your store](https://shoptoai.com/scan). You don't need an account or a card. Connect your store to check every product. Then work through the fixes in order. The full walkthrough is in our guide: [How to optimize a WooCommerce catalog for AI shopping assistants](https://shoptoai.com/guides/woocommerce-ai-shopping). For a step-by-step version with numbers from our scan of 96 Polish WooCommerce stores, read [how to get ChatGPT to see the products in your store](https://shoptoai.com/blog/chatgpt-widzi-produkty-woocommerce). If you would rather not do it product by product, ShopToAI audits your catalog, fixes the gaps in the markup and feeds your store serves, with one source of truth for your schema and every change reversible. See [pricing](https://shoptoai.com/pricing). --- ## We checked 96 WooCommerce stores: what ChatGPT can't read on a product page https://shoptoai.com/blog/chatgpt-widzi-produkty-woocommerce ChatGPT recommends products it can read. In 96 Polish WooCommerce stores, one product page in five had no Product data at all. What to fix, and in what order. Ask ChatGPT about something you have in stock and see which store it sends you to. Quite often it's someone else. Sometimes a bigger shop, sometimes not even a cheaper one. Your product is rarely the problem. The assistant just can't read it. In early October we checked 96 Polish WooCommerce stores, one product page from each. The median score came out at 74 out of 100, which isn't bad, but the gaps kept showing up in the same places. Below they are in the order we'd fix them. ### What ChatGPT reads on a product page A shopper looks at the photo, the price and the description. An AI assistant mostly reads what sits underneath. The part that matters most is the Schema.org data block (JSON-LD) of type Product. Then there's robots.txt, which tells the bot whether it may open the page at all, and more and more often llms.txt. When the data includes the brand, a GTIN, the price, availability and a few attributes, the assistant knows what you sell and can put your offer next to others. Without them it has to pick facts out of the page text, so it leans toward a store whose data is easy to read. A nice theme or good photos won't change that. A shorter rundown of these causes, without the scan numbers, is in our post on [why WooCommerce products don't show up in ChatGPT Shopping](https://shoptoai.com/blog/products-not-in-chatgpt-shopping). ### 1. Check whether the page has Product data at all This gap is the most serious one, and it's common. On 20 of the 96 pages, one in five, we found no Product data whatsoever. Fourteen of those 20 stores ran Yoast SEO. It described the website and the company, but not the product itself, and WooCommerce's own markup was missing from those pages too. Checking takes a minute. Paste any product URL into Google's Rich Results Test or into validator.schema.org and look for a Product item (for products with variations it may be a ProductGroup). If all you see is WebPage, Organization and BreadcrumbList, the assistant gets nothing about the product from you. How you fix it depends on what's installed. Some SEO plugins only describe products through a separate WooCommerce module. The other route is a plugin that outputs one complete Product block. Make sure there's exactly one. Two conflicting blocks are rare (2 pages out of 96 in our scan), but when it happens the assistant can't tell which one to trust. There's more on that in our post [about duplicate structured data](https://shoptoai.com/blog/fix-duplicate-schema-woocommerce). ### 2. Put the brand in the data, not only in the title Very often the brand is right there in the product name and nowhere in the data. In the scan only 23 of 96 pages had it in the data, fewer than one in four. Shoppers don't care. An assistant comparing offers for the same product across several stores needs the brand in its own field. Since version 9.6 WooCommerce has brands built in (in the Products menu, under Brands). Assign a brand to every product, then run the validator again and see whether a brand field shows up. If it doesn't, whichever plugin handles your structured data has to add it. ### 3. Fill in the GTIN where you have one We found a GTIN (in Europe usually the EAN under the barcode) on 10 of the 96 pages. Only 6 had both a brand and a GTIN. Those two fields are how an assistant links the product in your store to what it already knows about it from elsewhere. Since version 9.2 WooCommerce has a dedicated field for it on the Inventory tab. Copy the codes from the packaging or from your supplier's file. Don't type in a made-up code or one borrowed from another product, because a wrong GTIN does more harm than an empty one. If you make your own products and have no GS1 codes, leave the field blank and get the rest of the data right. The step-by-step version is in our guide on [how to add a GTIN in WooCommerce](https://shoptoai.com/guides/add-gtin-in-woocommerce). ### 4. Describe the product with attributes People ask assistants very specific things, say a moisturizer for dry skin, 50 ml, fragrance free. Size, skin type and scent are usually in the description, just buried in sentences a machine struggles to pull them out of. Yet 49 of the 76 pages that did have Product data showed no attributes, neither in the data nor as a table on the page. You add them on the product's Attributes tab. Put in whatever a shopper weighs when choosing: size, color, material, volume, ingredients. Global attributes (in the Products menu, under Attributes) work better, because the values then stay consistent across the whole store. Here's how it can look in the data. The example is made up, for a natural cosmetics store. Before: ```json { "@type": "Product", "name": "Krem do twarzy z masłem shea 50 ml", "offers": { "@type": "Offer", "price": "49.00", "priceCurrency": "PLN" } } ``` After: ```json { "@type": "Product", "name": "Krem do twarzy z masłem shea 50 ml", "brand": { "@type": "Brand", "name": "Brand name" }, "gtin13": "EAN from the packaging", "additionalProperty": [ { "@type": "PropertyValue", "name": "Pojemność", "value": "50 ml" }, { "@type": "PropertyValue", "name": "Rodzaj cery", "value": "sucha" } ], "offers": { "@type": "Offer", "price": "49.00", "priceCurrency": "PLN", "availability": "https://schema.org/InStock" } } ``` While you're there, look at availability. A price in the data isn't enough on its own; the assistant also wants to know whether the item can be bought right now. ### 5. Let AI bots in and add llms.txt Here the news is good. Only 3 of the 96 stores blocked AI bots in robots.txt. Still, open yours (yourdomain.com/robots.txt). The bot behind ChatGPT search is called OAI-SearchBot, while GPTBot collects data for training models, and you control them separately. If you want to show up in ChatGPT answers, at the very least don't block OAI-SearchBot. Sometimes the bots get stopped by a firewall or a security plugin instead, and then robots.txt looks perfectly innocent. 15 stores had an llms.txt file. It's a short description of the store with links to its key pages, written with language models in mind. It isn't required and nobody can promise ChatGPT will read it, but it takes about fifteen minutes to make. Just check that the address really serves a text file: 10 stores returned an ordinary shop page there. More in our post [on what llms.txt is](https://shoptoai.com/blog/llms-txt-explained). ### What to expect Nobody can promise that ChatGPT will start recommending you after these fixes, because the model decides what goes into an answer. Correct data is what lets the assistant understand your product in the first place and weigh it against others. In a given store the effect shows after a few weeks, once you compare ChatGPT's answers from before and after the changes. ### Where to start The easiest start is the free check at [shoptoai.com/scan](https://shoptoai.com/scan). Paste your store address or one product page. For the 96 stores we used its single-page mode. It takes about 30 seconds, needs no sign-up, and gives you a score from 0 to 100 with a list of what's missing. With a bigger catalog, fixing things product by product gets old fast. Once you connect your store, ShopToAI checks every product and suggests missing brands, categories and attributes. You approve each change yourself and the old value is saved, so everything can be rolled back. We never make up GTINs. Details are on the [pricing page](https://shoptoai.com/pricing). --- ## Why your product isn't showing in AI answers https://shoptoai.com/blog/why-product-not-in-ai-answers Why an AI assistant skips a product in ChatGPT, Perplexity or Google, how to tell which data problem is yours, and where to start fixing it. You searched for your own product in ChatGPT, Perplexity or Google's AI answers, and a competitor came up instead. It is a common and fixable problem. AI assistants do not rank pages the way classic search does; they pick products whose data they can read, trust and match to the question. When you are left out, it is almost always one of a handful of reasons. ### Run through the usual causes - **No structured data, or duplicated data.** If the page has no Product JSON-LD, or several conflicting blocks from the theme and plugins, the assistant cannot form a clear picture and moves on. - **Missing identifiers.** No brand, no GTIN where one exists. The assistant cannot confirm what the product is or that you sell the real thing. - **A vague title.** "Blue shoes" loses to "Trailblaze Pro GTX trail running shoe, men's". Models match specific language. - **Stale or missing feed.** Price and availability that do not match reality make an assistant distrust the listing. - **Crawlers blocked.** If robots blocks the AI crawlers, the assistant never reads the page at all. - **Thin offer data.** No clear price, currency or availability value. ### Diagnose before you fix Guessing wastes time. Check one product page for exactly which of the above is true: is there one clean Product block, are brand and GTIN present, does the feed match, are the crawlers allowed. That is the whole diagnosis, and it is the same across engines, though each has its own emphasis (see the guides for [Perplexity](https://shoptoai.com/blog/get-products-in-perplexity-shopping), [Google AI](https://shoptoai.com/blog/products-in-google-ai-overviews) and [Copilot](https://shoptoai.com/blog/products-in-copilot-shopping)). ### Fix the data, not the theme The fix is almost never a new theme or more content. It is cleaning the product data: one correct schema block, filled identifiers, a synced feed, crawlers allowed. Do that and eligibility follows. ### How ShopToAI helps ShopToAI runs this diagnosis across your whole WooCommerce catalog, scores the catalog from 0 to 100, lists the issues for each product and fixes the gaps with changes you can undo. Start with a [free store check](https://shoptoai.com/scan), or compare [AEO tools for WooCommerce](https://shoptoai.com/compare/aeo-tools-for-woocommerce). --- ## How to get your products into Perplexity Shopping https://shoptoai.com/blog/get-products-in-perplexity-shopping The product data Perplexity needs to shortlist and recommend your products: one clean Product block, identifiers, price and stock. Perplexity can answer shopping questions with a short list of specific products. If your catalog is not in that shortlist, the shopper never sees you. Good product data is the part of this you control, so getting eligible is mostly a data exercise. ### What to get right in the product data Structured data is a dependable way to tell an assistant what a product is, and a single clean Product block is the first thing to get right. Fill the obvious fields: name, brand, a real price with currency, an availability value, and identifiers like GTIN where the product has one. If that block is missing, duplicated, or contradicts the visible page, you are handing the assistant a reason to pick a competitor whose data is clean. ### The short checklist - One valid Product JSON-LD block per product page, from a single source. - Brand and, where it exists, a GTIN. Never invent a GTIN. - Price, currency and a real availability value that matches stock. - Ratings and reviews marked up, so shoppers and machines see the same proof. - PerplexityBot allowed to read your pages in robots.txt. ### Feeds still matter Structured data makes a single page readable, but a product feed keeps your whole catalog discoverable and current. Keep titles specific (brand, product type, the detail that matters) and keep price and stock in sync, because a stale feed is worse than no feed. ### How ShopToAI helps ShopToAI audits your WooCommerce catalog the way Perplexity reads it, then fixes the gaps: one correct Product block, muted duplicates, flagged identifier gaps and a feed kept in sync. See where you stand with a [free store check](https://shoptoai.com/scan), read the [structured data basics](https://shoptoai.com/blog/product-schema-json-ld), or compare [AEO tools for WooCommerce](https://shoptoai.com/compare/aeo-tools-for-woocommerce). --- ## How to get products into Google AI Overviews and AI Mode https://shoptoai.com/blog/products-in-google-ai-overviews Google AI Overviews draw on your Merchant feed and Product schema. How to make them agree so Google names your products. Google now answers many product searches with an AI Overview or in AI Mode, summarizing options above the classic blue links. For a store this is both a risk and an opening: the shopper may never scroll to the old results, but the products Google names in that summary get outsized attention. Being one of them comes down to data Google can trust. ### Google already knows your products, if you let it Unlike a pure chat assistant, Google has Merchant Center and the Shopping graph. The single biggest lever is a complete, accurate product feed: correct titles, real GTINs, price, availability and images. AI Overviews draw on that same structured product understanding, so a clean feed is doing double duty. On the page itself, valid Product structured data confirms and reinforces what the feed says. When the feed, the page and the visible content all agree, Google has no reason to doubt you. ### What to get right - A complete Merchant feed: titles, GTIN, brand, price, availability, images. - One valid Product JSON-LD block on the page that matches the feed. - Reviews and ratings, which Google surfaces prominently in product summaries. - No contradictions between feed, schema and the visible page. - Google's crawlers, including the AI ones, allowed in robots. ### Consistency is the whole game Most stores lose here not because a field is missing but because three sources disagree: the feed says one price, the page schema says another, the theme prints a third, and nothing tells Google which one is right. Keep one source of truth and make sure everything else matches it. ### How ShopToAI helps ShopToAI keeps your WooCommerce catalog, its Product schema and its feed telling the same story, and flags the contradictions that make Google hesitate. Start with a [free store check](https://shoptoai.com/scan), read [what AEO is](https://shoptoai.com/guides/answer-engine-optimization), or see [why products get left out of AI answers](https://shoptoai.com/blog/products-not-in-chatgpt-shopping). --- ## How Microsoft Copilot reads your product data https://shoptoai.com/blog/products-in-copilot-shopping Copilot pulls from Bing and Microsoft's merchant program. The often forgotten data work that makes your products eligible. Microsoft Copilot answers shopping questions across Windows, Edge and Bing, and it pulls from Bing's index and the Microsoft Merchant Center. If you have spent all your effort on Google and ChatGPT, Copilot is the engine most stores forget, which also makes it the one where clean data stands out fastest. ### Copilot's two inputs Copilot leans on two things: what Bing has crawled and understood about your product pages, and what you have submitted to Microsoft's merchant program. Both reward the same fundamentals as every other assistant, so you only need to make sure nothing is missing on Microsoft's side. ### What to check - Your pages are crawlable by Bing (bingbot) and Microsoft's AI crawlers in robots. - Valid Product structured data with brand, price, availability and identifiers. - A merchant feed submitted to Microsoft, not only Google. - Titles and attributes that spell out the product plainly, so a model can match a query to it. - Reviews and ratings marked up. ### The easy win most stores miss Many merchants submit a feed to Google and stop there, so Microsoft has a thinner picture of their catalog. Submitting the same clean feed to Microsoft's merchant program, and letting Bing crawl the pages, closes a gap your competitors probably still have open. ### How ShopToAI helps The product data that makes you readable to Copilot is the same data ShopToAI audits and fixes for every assistant: one correct Product block, flagged identifier gaps, and a feed you can send anywhere. Run a [free store check](https://shoptoai.com/scan), read the [AEO audit checklist](https://shoptoai.com/blog/aeo-audit-checklist), or compare [monitoring versus fixing](https://shoptoai.com/compare/ai-visibility-monitoring-vs-fixing). --- ## How to add GTIN to WooCommerce products https://shoptoai.com/guides/add-gtin-in-woocommerce Where to enter a GTIN in WooCommerce, why a wrong one is worse than none, and how to make sure it reaches the schema and feed. A GTIN is the barcode number that identifies a product globally: a UPC, EAN or ISBN. AI shopping assistants and Google use it to match your listing to the exact product a shopper means, and to trust that two stores are selling the same thing. Missing or wrong GTINs are one of the most common reasons a WooCommerce product gets skipped. Here is how to add them properly. ### First, find the real GTIN The GTIN comes from the manufacturer or the product packaging, not from you. It is the number under the barcode. Never invent, guess or reuse one from a similar product, because a wrong GTIN is worse than none: it tells the assistant you are selling something you are not. Some products genuinely have no GTIN: custom, handmade or your own private-label goods. That is fine. Leave it empty and lean on a strong brand plus a clear, specific title instead. ### Where to put it in WooCommerce WooCommerce added a native GTIN field. On the product edit screen, open the Inventory tab and fill the GTIN, UPC, EAN or ISBN field with the number from the packaging. For variable products, each variation can carry its own identifier, because a size or color variant often has its own barcode. If your version predates the native field, a well-maintained SEO or product plugin can add one, but avoid stacking several plugins that each try to own the identifier. ### Make sure it reaches the assistant Entering the GTIN is only half the job. It has to appear in two places a machine reads: the Product structured data (JSON-LD) on the page, as a `gtin` property, and your product feed. If the field is filled in the admin but never emitted in the schema or the feed, an assistant still cannot see it. ### How ShopToAI helps ShopToAI checks every product for a valid identifier, writes it into a single correct Product block and into the feed, and flags the products that genuinely lack one so you can decide what to do. See your gaps with a [free store check](https://shoptoai.com/scan), read [why identifiers matter](https://shoptoai.com/blog/gtin-brand-identifiers), or the [WooCommerce structured data checklist](https://shoptoai.com/blog/woocommerce-structured-data-checklist). --- ## WooCommerce product attributes AI assistants actually use https://shoptoai.com/blog/woocommerce-product-attributes-ai AI assistants match shoppers' questions against size, material, color and capacity. Which WooCommerce attributes to add, and how they reach your product data. People ask assistants precise things. "A moisturizer for dry skin, 50 ml, fragrance free." "A tent for two people under 2 kg." "A navy cotton shirt in size M." Each question works like a filter. To answer it, an assistant has to match the filter against your product, and that is easiest when the facts sit in named fields: volume, skin type, scent, weight, size, color, material. Most stores already have these facts. They're in the description, sometimes in the title, sometimes in a spec sheet PDF. A person reads the description and finds the answer. A machine has to pull it out of the prose, and it doesn't always bother. In our October 2026 scan of 96 Polish WooCommerce stores we checked one product page per store. Of the 76 pages that had Product data at all, 49 (64%) had no attributes, neither in the structured data nor as a table on the page. ### Global, local and variation attributes WooCommerce has two kinds of attributes, and it's worth using both on purpose. Global attributes are created under Products, then Attributes. You define "Color" or "Size" once, add the values (navy, white, S, M, L) and reuse them across products. The values stay identical across the store, so "navy" and "Navy blue" don't drift apart. Use global attributes for anything shoppers filter or compare by. Write the values the way a shopper would search for them, so "dry skin" rather than an internal code. Local attributes belong to a single product. You type them on that product's Attributes tab. They suit a one-off detail such as "In the box: charger", but they can't be reused, and nothing keeps their spelling consistent. Variations are a separate thing. When a product comes in several sizes or colors, mark those attributes as "Used for variations" and create a variation for each combination. Each variation can carry its own SKU, price and stock. Keep the axes to two or three per product, since more than that makes the choice hard for shoppers. This also matters for structured data: a product with variations can be described as a group, and Google uses size, color, material and pattern to tell variants apart. ### How attributes reach the structured data Shoppers see attributes in a table. An AI assistant reads the Product block in JSON-LD. Schema.org has no single field for "volume" or "skin type", so attributes go into additionalProperty, a list of name and value pairs: ```json "additionalProperty": [ { "@type": "PropertyValue", "name": "Seats", "value": "2" }, { "@type": "PropertyValue", "name": "Weight", "value": "1.9 kg" } ] ``` The ShopToAI WordPress plugin (shoptoai-connector) injects the Product block for each product. Our backend builds that block from your catalog data, and the plugin also silences duplicate Product blocks from WooCommerce and Yoast. Attributes from your product card end up in the block as additionalProperty. If another SEO module on the site still prints its own Product block, the assistant gets two conflicting ones, so check the page after any plugin change. For variants, the group lists variesBy, the axes it varies on. Only size, color, material and pattern can appear there, and only when at least two variants share that attribute. ### A short checklist per product type - Clothing: size and color as global attributes used for variations, material with the percentage in the value ("100% cotton"), cut or fit. - Cosmetics: volume in ml or g, skin type, scent or fragrance free, and the key ingredients from the label. - Electronics: capacity or power, dimensions, weight, connector type, warranty length. - Furniture: dimensions in cm, frame and top material, maximum load, whether assembly is needed. - Food: net weight or volume, allergens, a short ingredient summary. The rule is the same everywhere: enter only what you can check on the packaging or the spec sheet. Start with the two or three facts shoppers ask about most in your category, then add the rest. When a supplier sends new specs, fix the term once in the global attribute instead of on every product. ### How to check the result Paste the product URL into validator.schema.org or Google's Rich Results Test. Look for a Product item, or a ProductGroup for products with variants. Each attribute you filled in should appear as an additionalProperty entry. If one is missing, the field is usually empty in WooCommerce or the value never reaches the plugin. Check several products, not just one, because a missing attribute tends to repeat across a catalog. The validator shows what a parser sees, not what a shopper sees, so compare the Product block with the attribute table on the page. If they disagree, the data is out of date, and that's the first thing to fix. Recheck after any update to WooCommerce or your SEO plugin, since a new version can add a second Product block next to ours. ### How ShopToAI helps Once you connect your store, ShopToAI flags products without attributes and suggests values for them. You approve or reject each suggestion, and the old value is saved, so any change can be rolled back. We never make up GTINs. We also don't promise a place in ChatGPT answers, because the model decides what goes into an answer. Good attributes simply let it read your product and compare it with others without guessing. The free scan takes about 30 seconds and needs no account, so you can see the gaps across a sample of your products before you change anything else. If you're starting from scratch, begin with [how to get ChatGPT to see the products in your WooCommerce store](https://shoptoai.com/blog/chatgpt-widzi-produkty-woocommerce). It covers the order of fixes: Product data first, then brand, GTIN and attributes. For the structured data itself, see our article on [Product JSON-LD](https://shoptoai.com/blog/product-schema-json-ld). # Comparisons ## AEO tools for WooCommerce https://shoptoai.com/compare/aeo-tools-for-woocommerce What an AEO tool for WooCommerce should do, and how a WooCommerce-native tool compares to a typical AEO or SEO tool. If you run a WooCommerce store and want AI assistants like ChatGPT, Perplexity and Gemini to recommend your products, you have probably searched for an "AEO tool for WooCommerce". Most tools in this space were built for something adjacent: they monitor whether a brand is mentioned by AI, or they generate content, or they were designed Shopify-first. Few actually read a WooCommerce catalog the way an assistant does and fix what is wrong, on your store, with your data. This page compares what a WooCommerce-native AEO tool does against a typical AEO or SEO tool, so you can decide what you actually need. - Platform focus (ShopToAI: WooCommerce-first, with a WordPress plugin you set up in minutes; Typical AEO or SEO tool: Shopify-first or a platform-agnostic dashboard) - What it does (ShopToAI: Audits and fixes the catalog: approved fixes to description, short description, SKU and GTIN are written to WooCommerce, and approved brand, category and attribute fixes go into the Product markup and feeds we serve; Typical AEO or SEO tool: Reports or monitors; you apply the fixes by hand) - Structured data (ShopToAI: Places one correct Product JSON-LD and mutes duplicates from WooCommerce, Yoast, Rank Math and All in One SEO; Typical AEO or SEO tool: Flags schema issues, no write-back) - Identifiers and feeds (ShopToAI: GTIN, brand, attributes and Google/agent feeds kept in sync; Typical AEO or SEO tool: Usually not product-catalog specific) - Reversibility (ShopToAI: Every change stores the old value for one-click undo; Typical AEO or SEO tool: Not applicable (no changes made)) - For agencies (ShopToAI: Many client stores managed from one dashboard; Typical AEO or SEO tool: Mostly single-brand self-serve) - Measurement (ShopToAI: A 0-100 AI-readiness score with before/after rescans; Typical AEO or SEO tool: Often brand-mention visibility metrics) - Company and data (ShopToAI: EU company, public DPA and sub-processor list, English and Polish; Typical AEO or SEO tool: Varies by vendor) ### Where the alternatives fit better A general AEO or brand-visibility platform is the better choice if your main question is "does AI mention my brand across many assistants" rather than "are my products data-ready". If you are on Shopify, a Shopify-native app will integrate more directly than any WooCommerce-first tool. And a large retailer with an in-house engineering team may prefer to pull raw feed and schema APIs and build the workflow themselves. ShopToAI is the better fit when your catalog lives on WooCommerce and you want the gaps found and fixed on the store, not just reported. Q: What is an AEO tool for WooCommerce? A: It is software that prepares your WooCommerce product data so AI shopping assistants can read and recommend it: valid Product structured data, real identifiers like GTIN, clear offers, and feeds kept in sync. A WooCommerce-native one does this on your store rather than handing you a checklist. Q: Do I still need it if I use Yoast or RankMath? A: SEO plugins help with classic search and can add schema, but they are not built to audit a catalog for AI readiness or to fill product gaps such as GTIN, brand and attributes. When the theme, WooCommerce and an SEO plugin each output product markup, a page can end up with two conflicting blocks. Our plugin keeps one. Q: Will it work with my theme? A: Yes. Because the plugin places its own Product structured data and mutes duplicates from WooCommerce, Yoast, Rank Math and All in One SEO, the result does not depend on the theme emitting correct markup. Q: Can agencies use it for client stores? A: Yes. One account holds many client stores, each with its own audit, fixes and AI-readiness score, so an agency can run every client from a single dashboard. --- ## AEO tools for agencies https://shoptoai.com/compare/aeo-tools-for-agencies What an agency needs from an AEO tool: multi-client management, applied fixes and client reporting, compared to a single-brand tool. Agencies are in a specific spot with AEO. You run the same work across many client catalogs, and you need to show results to each client. Most AEO tools were built for a single brand to self-serve, so the multi-client and reporting pieces are either missing or bolted on. This page compares what an agency-ready AEO tool offers against a typical single-brand AEO tool. - Cost model (ShopToAI: One plan covers up to fifteen client stores; Typical single-brand AEO tool: Priced per brand or per seat, which adds up across clients) - Automation (ShopToAI: Rescans catalogs on a schedule. Auto-apply for safe gap-fills stays off until you turn it on; Typical single-brand AEO tool: Usually dashboard-only, one account at a time) - Multi-client (ShopToAI: Built to manage many client stores from one place; Typical single-brand AEO tool: Designed for one brand to self-serve) - What it delivers (ShopToAI: Audits and applies fixes, with before/after to show clients; Typical single-brand AEO tool: Often a report the client must act on) - Safety (ShopToAI: Every change is reversible (old value stored); Typical single-brand AEO tool: Not applicable (advice only)) - Platform (ShopToAI: WooCommerce-first, with a WordPress plugin you set up in minutes; Typical single-brand AEO tool: Shopify-first or platform-agnostic) - Company and data (ShopToAI: EU company, public DPA and sub-processor list, English and Polish; Typical single-brand AEO tool: Varies by vendor) ### Where the alternatives fit better If you are a single brand rather than an agency, most of the agency machinery is overhead you will not use, and a straightforward self-serve tool is simpler. If your clients mainly want a report on where their brand stands in AI answers, a brand-visibility platform answers that question more directly. And if every client is on Shopify, a Shopify-native app will fit their stores better than a WooCommerce-first tool. ShopToAI is the better fit when you run AEO as a service across several WooCommerce clients from one dashboard. Q: Can I manage many client stores from one place? A: Yes. One account holds many client stores, each with its own audit, fixes and AI-readiness score, so you run every client from a single dashboard and can invite your team to help. Q: How does onboarding a new client work? A: Connect the client's WooCommerce store with a Read/Write API key, run the audit and approve the fixes. Catalogs are rescanned on a schedule. If you turn on auto-apply, an empty brand that the product name already states is filled in without waiting for you. Q: How do I show clients it worked? A: Each store gets a 0-100 AI-readiness score, and a rescan after fixes shows the before/after, so you have a clear number to report rather than a vague promise. --- ## AI visibility monitoring vs fixing https://shoptoai.com/compare/ai-visibility-monitoring-vs-fixing AI visibility tools measure whether AI mentions your brand. Catalog fixing makes your products readable and recommendable. Here is the difference. There are two different jobs hiding under "AI visibility". One is measuring: tracking whether an assistant mentions your brand across ChatGPT, Perplexity, Gemini and others. The other is fixing: making your product data good enough that an assistant can actually read and recommend it. Most well-known tools do the first. They tell you that you are invisible; they do not change anything on your store. This page compares AI visibility monitoring against catalog fixing, so you know which one you are buying and whether you need both. - Core job (ShopToAI: Fixes the product catalog so AI can read and recommend it; AI visibility monitor: Measures whether AI mentions your brand) - Output (ShopToAI: Approved fixes in your product data, markup and feeds, plus a readiness score; AI visibility monitor: Dashboards, prompts tracked, share-of-voice) - Scope (ShopToAI: Product-level data: schema, GTIN, attributes, feeds; AI visibility monitor: Brand-level mentions across AI assistants) - Who acts (ShopToAI: The tool applies fixes (reversibly); AI visibility monitor: You (or your agency) act on the findings) - Platform (ShopToAI: WooCommerce-first, with a WordPress plugin you set up in minutes; AI visibility monitor: Platform-agnostic tracking) - Best for (ShopToAI: Making a catalog actually recommendable; AI visibility monitor: Watching brand presence over time) ### Monitoring and fixing work together You can use both. A monitoring platform answers "where do we stand across AI assistants" and is the better tool if brand-wide share of voice is your main question, especially with many assistants at once. ShopToAI answers "why are my products not readable, and how do we fix it" on the catalog itself. The workflow that covers both: measure with a monitor, fix the catalog with ShopToAI, then measure again. If you only buy one, buy the one that matches the job you actually have. If your product data is weak, monitoring alone will keep reporting the same bad news. Q: Do I need a monitor and a fixer? A: They do different jobs and work well together: a monitor tells you where you stand, a fixer changes the data so you stand higher. If your product data is weak, start with fixing, because monitoring alone cannot improve a catalog it only observes. Q: Does ShopToAI track brand mentions across AI assistants? A: It does. On the Agency plan ShopToAI monitors brand mentions across ChatGPT, Perplexity and Gemini, and every paid plan tracks the AI visibility of the products you pick, alongside auditing and fixing with a 0-100 readiness score and before and after rescans. Its monitoring is built around your catalog rather than brand-wide share of voice, and that is where a dedicated monitor still goes deeper. # AEO by category ## AEO for fashion and apparel https://shoptoai.com/aeo-for/fashion How to make fashion product data readable to AI assistants: sizes, colors, materials and per-variant identifiers. Fashion is one of the hardest categories for AI assistants to get right, because a single product exists in many variants and shoppers ask in very specific terms: a size, a color, a fit, an occasion. If your variant data is not machine-readable, an assistant cannot answer "waterproof trail shoes in size 44" with your product, even when you stock exactly that. The work is mostly about making every variant explicit and identifiable. Attributes that matter: - Size: The single most common filter in a fashion query; must be a structured value, not only text in an image - Color: Shoppers ask by color; each colorway should be its own readable variant - Material / fabric: Drives queries like 'leather' or 'merino'; also signals quality to the model - Gender / age group: Disambiguates men's, women's, kids' so the assistant matches the right shopper - Per-variant GTIN: A size or color variant usually has its own barcode; use it per variation - Fit / style: Slim, regular, oversized: specific language the model matches to intent Common gaps: - Size and color live only in a dropdown, never in the structured data. - Variations share one identifier instead of carrying their own. - Generic titles ('Blue dress') instead of specific ones. Q: Should each size and color be its own listing? A: They should at least be their own readable variants with their own identifiers and structured size/color values. That lets an assistant answer a specific 'size 44 in black' query with the exact variant you stock. --- ## AEO for electronics https://shoptoai.com/aeo-for/electronics How to make electronics readable to AI assistants: model numbers, MPN, GTIN, specs and compatibility. Electronics shoppers ask precise questions: a model number, a capacity, whether it is compatible with something they already own. Assistants answer those well only when the identifying data is exact. A vague listing loses to a competitor whose model number and specs are spelled out, because the model has no way to confirm yours is the right device. Attributes that matter: - Model number: Shoppers search by exact model; without it the assistant cannot confirm the device - MPN: The manufacturer part number pins the exact SKU when a GTIN is absent - GTIN: Matches your listing to the same device sold elsewhere and builds trust - Key specs: Capacity, wattage, screen size: the values that decide a comparison - Compatibility: 'Works with' queries are common; state what it fits so the model can match - Condition: New, refurbished or used changes the answer; make it explicit Common gaps: - Model number and MPN missing, so two similar devices look identical. - Specs buried in a description image instead of structured data. - No compatibility info, so accessory queries never match. Q: GTIN or MPN for electronics? A: Use both where you have them. The GTIN matches your device to the same product elsewhere; the MPN pins the exact manufacturer SKU and is especially useful for parts and accessories that lack a GTIN. --- ## AEO for beauty and cosmetics https://shoptoai.com/aeo-for/beauty How to make beauty products readable to AI assistants: shades, volume, ingredients, skin type and identifiers. Beauty and cosmetics live and die on specifics: a shade, a volume, an ingredient someone is avoiding, a skin type it suits. Shoppers ask AI assistants exactly these questions, and a listing that only shows a pretty photo cannot answer them. The shade and size that sit in a dropdown need to be readable data. Attributes that matter: - Shade / color: The primary variant in makeup; each shade should be its own readable option - Volume / size: Price-per-ml comparisons need a structured size value - Key ingredients: 'With retinol', 'fragrance-free': ingredient queries are constant in beauty - Skin / hair type: Matches products to a shopper's stated need (oily, sensitive, curly) - GTIN & brand: Confirms the exact product and that you sell the genuine item Common gaps: - Shades exist as one listing with no structured shade value. - Ingredients only in a description image, unreadable to a model. - Volume missing, so per-unit comparisons fail. Q: How do I handle many shades of one product? A: Model each shade as its own variant with a structured shade value and, where it exists, its own GTIN. That lets an assistant recommend the exact shade a shopper asks for rather than a generic product. --- ## AEO for supplements and health products https://shoptoai.com/aeo-for/supplements How to make supplements readable to AI assistants: actives, dosage, servings, dietary flags and identifiers, without unsafe claims. Supplements and health products are queried by what is in them and how they are taken: an active ingredient, a dose, a serving count, a dietary flag like vegan or gluten-free. Assistants need that detail as structured data to match a shopper's specific request, and this is a category where accuracy and restraint matter, since health claims carry rules. Attributes that matter: - Active ingredients: Shoppers search by active (magnesium, omega-3); it must be structured, not only on a label image - Dose / strength: '1000mg' style queries need a real value to match - Servings / count: Drives value comparisons (cost per serving) - Form: Capsule, powder, gummy: a real preference shoppers state - Dietary flags: Vegan, gluten-free, sugar-free: common hard filters - GTIN & brand: Confirms the exact product and genuine sourcing Common gaps: - Actives and dose only on a label image, not in structured data. - Serving count missing, so value queries cannot compare. - Unverified health claims that create risk instead of trust. Q: Should I add health benefit claims for AEO? A: Stick to factual, compliant product data: ingredients, dose, servings, form and dietary flags. Structured facts are what assistants match on, and unverified health claims carry regulatory risk without improving eligibility. --- ## AEO for jewelry and watches https://shoptoai.com/aeo-for/jewelry How to make jewelry and watches readable to AI assistants: metal, purity, gemstone, size, movement and identifiers. Jewelry and watches are bought on precise attributes: the metal and its purity, a gemstone, a ring size, and for watches the movement and water resistance. Shoppers ask AI assistants in exactly those terms, and a listing that shows a beautiful photo but hides the material and size in prose cannot be matched to "14k gold, size 7" or "automatic dive watch". Attributes that matter: - Metal & purity: Gold, silver, platinum and karat/fineness are primary filters and a value signal - Gemstone: Type, carat and cut drive specific queries - Size: Ring or bracelet size is a hard filter; needs a structured value per variant - Movement (watches): Quartz vs automatic is a deciding attribute for watch buyers - Water resistance: A common watch query; state it as a real value - GTIN & brand: Confirms authenticity, which matters more in this category Common gaps: - Metal and purity described in prose, not structured data. - Ring size buried in a dropdown with no per-variant value. - No brand or identifier, so authenticity cannot be confirmed. Q: How do I handle ring sizes for AEO? A: Model each size as its own variant with a structured size value, and its own identifier where one exists. That lets an assistant match a shopper asking for a specific size instead of a generic listing. --- ## AEO for furniture and home goods https://shoptoai.com/aeo-for/furniture How to make furniture readable to AI assistants: dimensions, material, weight, assembly and finish. Furniture and home goods are chosen on fit and material: will it go through the door, does the sofa suit the room, what is it made of, does it need assembly. These are dimension and material questions, and an assistant can only answer them when the numbers are structured data rather than text baked into a lifestyle photo. Attributes that matter: - Dimensions: Width, depth, height decide whether it fits; the top question in furniture - Material: Solid wood, veneer, fabric type: quality and query signal - Weight: Affects shipping and suitability; a real value assistants use - Assembly: Flat-pack vs assembled is a common concern shoppers state - Color / finish: Each finish should be its own readable variant - GTIN & brand: Confirms the exact model across sellers Common gaps: - Dimensions shown only in a photo, not as structured values. - Material vague ('wood') instead of specific. - No weight or assembly info, so practical queries fail. Q: What is the most important furniture attribute for AEO? A: Dimensions, as structured values for width, depth and height. 'Will it fit' is the most common furniture question, and an assistant can only answer it when the numbers are readable data rather than text on an image. --- ## AEO for food and beverage https://shoptoai.com/aeo-for/food How to make food products readable to AI assistants: allergens, dietary flags, weight, ingredients, without unsafe gaps. Food and beverage shoppers ask AI assistants about what is in a product and who it suits: allergens, dietary flags like vegan or gluten-free, weight or volume, and how it is packaged. These are hard filters. Accuracy matters a great deal in this category, since an allergen mistake can hurt a customer as well as cost you a sale. Attributes that matter: - Allergens: A safety-critical hard filter; must be structured, never only on a label image - Dietary flags: Vegan, organic, gluten-free, halal: constant food queries - Weight / volume: Needed for price-per-unit comparisons - Ingredients: Shoppers search by ingredient and by what to avoid - Pack size / count: Single vs multipack changes the match and the value - GTIN & brand: Confirms the exact product across sellers Common gaps: - Allergens and ingredients only on a label image, unreadable to a model. - Dietary flags missing, so 'vegan' style filters skip you. - Weight or pack size absent, so value comparisons fail. Q: Do I need allergens in structured data? A: Yes, and it is the highest priority in this category. Allergen filters are safety-critical, so the data must be structured and accurate rather than sitting only in a label photo a model cannot read reliably. # Glossary ## AEO (Answer Engine Optimization) https://shoptoai.com/glossary/aeo Answer Engine Optimization is the practice of structuring your content and product data so AI assistants can read it, trust it, and quote it in their answers. For a store it means clean structured data, real identifiers and a clear offer, so an assistant can recommend your product with confidence. ## GEO (Generative Engine Optimization) https://shoptoai.com/glossary/geo Generative Engine Optimization is about getting your content cited and recommended inside the answer a generative AI writes. AEO makes your product facts machine-readable. GEO adds content assistants can quote, such as FAQ and buying guides, and lets their crawlers reach your store. ## Structured data https://shoptoai.com/glossary/structured-data Structured data is machine-readable information about a page, added in a standard format so search engines and AI assistants can understand it without guessing. For products it usually means a schema.org Product block describing name, price, brand, availability and identifiers. ## JSON-LD https://shoptoai.com/glossary/json-ld JSON-LD (JSON for Linking Data) is the recommended format for adding structured data to a web page. It is a small block of JSON, usually in a script tag, that describes the page's content, such as a Product with its price, brand and availability, in a way machines read reliably. ## GTIN https://shoptoai.com/glossary/gtin GTIN (Global Trade Item Number) is the standard product identifier managed by GS1, the number under a product's barcode. UPC is a 12-digit GTIN and EAN is a 13-digit GTIN. Assistants use it to match your product to the exact item a shopper asked for. ## Product feed https://shoptoai.com/glossary/product-feed A product feed is one structured file of your whole catalog that platforms pull on a schedule, so an AI assistant knows your current price and stock without crawling every page. Google Merchant Center, other shopping services and AI shopping assistants use it to index and compare products. # Contact - support@shoptoai.com