GEO for e-commerce and AI shopping
I get your product data to the point where AI systems can name your product, its price and whether it is in stock without getting it wrong. I check product pages, markup and feeds for mismatches, then test selected SKUs in the kind of prompts shoppers use to pick a product.
An empty field is a question. Another store’s product page gets to answer it.
When does product data need work?
When a wrong price or a missing identifier costs you orders.
The price does not match
The page shows one price, the markup another and the feed a third. An AI answer can quote any of them, including the wrong one, and Merchant Center may disapprove the product.
The product cannot be identified
The listing has no GTIN or MPN and only a vague brand field, so a model cannot match it to the product the shopper asked about.
Descriptions are copied
The product text comes from the manufacturer, word for word the same as in every other store that sells the item. Nothing in it gives a model a reason to pick your store.
Stock status is out of date
The item sold out last week, but the page still says in stock, so an AI recommendation sends the shopper to a product they cannot buy.
No comparisons
Shoppers ask “which is better” and “what is the difference”, and the catalog has product pages with no comparison anywhere.
Category pages say nothing
A category page is a grid of products without a single word on how to choose between them.
What goes into
the catalog work?
Catalog structure
- Category logic and depth compared with how shoppers search
- Filters, whether filtered pages get indexed and what their URLs look like
- Duplicate products and variants of the same item listed separately
- Internal links between categories, products and buying guides
Product page quality
- Titles checked against the words shoppers use for the product
- Unique, specific descriptions with use cases, in place of the manufacturer’s text that every other seller copied too
- Complete attributes in one format, with consistent units
- Identifiers: SKU, GTIN, MPN, brand and model
Structured data (schema.org)
- Product, ProductGroup and variants
- Offer: price, currency, availability, condition and price validity
- MerchantReturnPolicy and OfferShippingDetails
- AggregateRating and Review only where the reviews are real
- Markup values checked against the visible text of the page
Cross-checking your sources
- Price on the product page, in the markup, the feed and the cart
- Availability and delivery times in every source
- Shipping, payment and return terms
- A repeatable check for mismatches after catalog updates
Product feeds and channels
- Google Merchant Center and Microsoft Merchant Center diagnostics
- Disapproved and limited products, with the reason for each
- Feed fields against the specs of the channels you use, including the ChatGPT and Perplexity merchant programs
- Newer Merchant Center attributes for conversational search: answers to product questions, compatible accessories, substitutes
- Fixes ranked by the number of products they affect
Content that helps shoppers choose
- Category page copy: how to choose and what to look at
- Comparison pages between models and product lines
- Guides by use case: what it is for, who it suits, when to pick it
- Shipping, warranty and returns FAQ in plain text
SKU testing and readiness
- 20–50 SKUs tested in shopping prompts in ChatGPT, Google AI Mode, Perplexity and Microsoft Copilot
- A record of whether your store is named and with what data
- How ready the catalog is for agentic checkout
- Recommendations on checkout protocols, with no promises about timing
How does the project run?
Data export
I get the catalog export, the feed and access to your channels. 1–2 days.
Catalog audit
Structure, product page, markup and mismatch checks. 5–7 days.
Report and spec
The list of issues and a spec for fixing the feed and templates. 2–3 days.
Priorities
You and I agree on what gets fixed first and who does it.
Implementation
Fixes to product templates, markup, categories and comparison pages.
SKU testing
I run the selected products through AI shopping prompts.
What do you get?
The report is built for bulk fixes across the catalog, so nobody edits product pages one by one.
Product data quality report
Every field of every product I checked, scored for completeness and accuracy.
Mismatch list
Where price, availability or terms differ between the page, the markup and the feed.
Feed and markup spec
What to change in templates and in the feed export, with examples and acceptance criteria.
Product and Offer templates
Ready markup for your product types and variants.
SKU test results
Which products AI systems named, with what data, and which store they pointed to.
Agentic commerce readiness
What already works for AI agents that shop on a buyer’s behalf and what still needs development.
I put the markup on the template. Fix product pages one at a time and the next thousand ship empty again.
Timeline and format
- Up to 500 products – 15–20 business days
- Up to 5,000 products – from 25 business days
- Large catalogs are checked by sampling each product template and category type
- Bulk changes are made by your developer from my spec
What I need from you
- A catalog export and access to your product feed
- Access to Google Merchant Center and your other product channels
- Contact details for your developer or whoever edits the product templates
- Your priority categories and best-selling products
Not included
- Cart, inventory and checkout integrations – estimated separately as development
- Shopping ads management in Google Ads or Microsoft Advertising
- Rewriting every product description by hand
How is the result measured?
The main number is the share of products with complete data that agrees across every source.
Data completeness
Share of products with identifiers and key attributes filled in.
Mismatches
How many price and availability mismatches remain between sources.
Mentions in shopping prompts
How often AI systems name your store for the tested SKUs.
What usually goes with it?
Technical GEO
Large catalogs almost always run into crawler access and rendering problems.
from $490 →Content for AI search
Comparisons and buying guides are what shopping answers quote most often.
from $190 per article →AI visibility monitoring
The catalog changes every week, and without checks the mismatches come back.
from $290/mo →Related reading
What is generative engine optimization (GEO), and how is it different from SEO and AEO?
GEO is the work of getting your business named and cited in AI answers. I explain how it relates to SEO and AEO, what to measure instead of rankings and what a realistic first 90 days look like.
Read →How to get your website cited by ChatGPT: a step-by-step plan
There is no form that puts your site into ChatGPT answers. What helps is crawler access, indexing in the search engines ChatGPT relies on, citable content and honest measurement.
Read →Questions from store owners
We have 80,000 products. Will you check every one?
Do I need Google Merchant Center if I don’t run Shopping ads?
What is agentic commerce, and should I prepare for it now?
Do you build the cart and inventory integrations?
My store runs on Shopify, WooCommerce, BigCommerce or Adobe Commerce. What can be changed?
What does SKU testing tell me?
Discuss your catalog
Tell me the catalog size and the platform. I will reply within one business day with what I can check and how long it will take.
I reply within one business day. For a larger project, fill in the project brief.