01
Find the buying questions
Map how shoppers ask about categories, use cases, specifications, comparisons, price, trust and fit.
EcommerceSEO
AI search visibility for ecommerce
AI SEO for ecommerce that helps products get found and chosen
Help shoppers discover accurate product and category information when they research through Google, ChatGPT, Gemini, Claude, Perplexity and Copilot. We connect catalogue pages, product data, structured data, merchant feeds, trusted proof and revenue measurement in one ecommerce search programme.
Shopify · WooCommerce · Magento · Headless commerce · D2C · Marketplaces
Your products, across every major AI discovery surface
Six platforms. One connected ecommerce search programme.
Selected ecommerce and marketplace experience
What are you actually buying?
Better product discovery, not an AI visibility score.
The value is a clearer route from a shopper's question to the right category or product, with facts they can trust and a next step your team can measure.
This work sits on top of a sound
. It does not replace technical SEO, category architecture, product content, structured data, merchant feeds, digital PR or analytics. It makes those parts work towards the same shopping questions.02
Represent products accurately
Align product names, identifiers, variants, attributes, price and availability across pages, schema and merchant feeds.
03
Give answers a useful destination
Build category, product, comparison, buying-guide and support pages that help a shopper continue towards a decision.
04
Measure commercial movement
Keep mentions, citations, visits, assisted journeys, transactions and revenue as separate signals.
Where shoppers research
One catalogue. Different answer and shopping experiences.
We do not sell a disconnected tactic for every platform name. The product facts and source pages stay consistent, while the baseline and measurement account for how each platform presents answers.
Google AI Overviews
Category and product discovery inside Google
ChatGPT
Product research, comparisons and recommendations
Gemini
Google-connected shopping and brand research
Perplexity
Cited answers with source-page visits
Microsoft Copilot
Bing-connected research journeys
Claude
Detailed product and category research
What the work includes
Make the catalogue easier to retrieve, compare and trust
The engagement starts with the products and categories that matter commercially, then connects technical access, product facts, useful pages, independent proof and measurement.
Shopping Question and Prompt Map
Map category, comparison, specification, use-case, price, trust and product-fit questions to the pages that should answer them.
- Category and product prompt set
- Competitor and citation-source review
- Buyer-language and intent mapping
- Page ownership and gap plan
Catalogue and Product Data Clarity
Align the facts shoppers and machines need: product names, identifiers, variants, attributes, price, availability and category relationships.
- Product and variant field review
- Identifier and attribute consistency
- Category-to-product relationships
- Inventory-state handling
Category, Product and Buying Pages
Improve the indexable pages that help shoppers discover a category, compare options, understand a product and continue towards purchase.
- Category and collection pages
- Product detail pages
- Comparisons and buying guides
- Shipping, returns and support answers
Structured Data and Merchant Feeds
Match visible product facts with Product structured data and the merchant feed used for Google shopping surfaces.
- Product JSON-LD validation
- Price and availability checks
- Merchant-feed diagnostics
- Rendered-page comparison
Trust, Reviews and External Sources
Strengthen the evidence shoppers use when they compare a brand or product, both on the store and across relevant independent sources.
- Review and policy clarity
- Expert and testing evidence
- Relevant editorial references
- Incorrect source correction plan
AI Visibility and Revenue Measurement
Track repeated answer visibility without treating every mention as a transaction or every referral as qualified demand.
- Prompt and citation baseline
- Cited-page and accuracy tracking
- AI referral and assisted journeys
- Product and category revenue context
One ecommerce discovery system
Connect the question, source page, AI response and commercial action.
AI SEO becomes valuable when a product-discovery signal can be traced back to the page, catalogue fact and customer action that created it.
| Buyer question | What we improve | AI-search signal | Business signal |
|---|---|---|---|
| Category demand | Category and collection pages | Category inclusion and citations | Qualified category visits and revenue |
| Product comparisons | Product pages, comparison pages and buying guides | Accurate attributes and cited products | Assisted product journeys and transactions |
| Price and availability | Visible product facts, Product schema and merchant feeds | Consistent product representation | Qualified visits with current purchase information |
| Trust and proof | Reviews, policies, testing, case evidence and external references | Clearer recommendation context | Stronger consideration and conversion support |
Platform-specific delivery
Your commerce platform changes the implementation work.
The commercial goal stays consistent, but the catalogue fields, templates, schema output and feed controls do not.
Shopify
Work inside Shopify's collection, product, variant, theme and sales-channel constraints.
- Check product titles, vendors, product types, variants, GTINs, price and inventory in Shopify Admin > Products
- Review collection copy, filters and internal links in the active collection template
- Inspect Product JSON-LD in Online Store > Themes > Edit code and test the rendered product page
- Compare storefront data with the Google and YouTube sales channel or the merchant-feed app in use
WooCommerce
Control WordPress taxonomies, product attributes, plugin output and feed data as the catalogue grows.
- Review Products > Categories, Products > Attributes, variations, SKUs, GTIN fields, price and stock status
- Check product-category archives, parameter URLs and internal links generated by the active theme and plugins
- Validate Product JSON-LD from WooCommerce and the active SEO or schema plugin against visible product facts
- Compare product-page data with the selected Google Merchant Center feed plugin and its diagnostics
Magento and headless
Connect category, product and feed systems with the HTML that shoppers and search systems can reach.
- Review Catalog > Products and Catalog > Categories for attributes, variants, identifiers, price and inventory
- Check store views, layered-navigation URLs, canonical output and category-to-product relationships
- Validate Product and BreadcrumbList JSON-LD produced by the theme, extension or frontend application
- For headless stores, compare API data, rendered HTML, schema and merchant feeds before release
First 90 days
Baseline the problem, fix the source and measure the response
Step 01
Build the ecommerce AI visibility baseline
Agree the priority categories, products, markets and buyer questions. Record current mentions, recommendations, citations, cited pages, answer accuracy and relevant referral activity across the selected platforms.
Step 02
Trace each gap to a page, product field or source
Separate technical access problems from missing category pages, unclear product attributes, inconsistent schema or feeds, weak proof, outdated third-party information and authority gaps.
Step 03
Ship platform-specific changes
Turn the plan into Shopify, WooCommerce, Magento or headless implementation work. Each task names the affected template or catalogue field, owner, expected behaviour and production acceptance check.
Step 04
Repeat the baseline and follow commercial movement
Recheck the stable question set after releases, then connect answer changes with cited pages, referral sessions, assisted product journeys, transactions and revenue where the data supports that conclusion.
Measurement
A citation is a signal. It is not a sale.
Reporting keeps discovery, consideration and commercial action separate. That gives your team an honest view of what changed and which pages, products or categories deserve the next investment.
01
Search foundation
Non-brand queries, rankings, clicks, landing pages and index coverage
02
AI visibility
Prompt coverage, mentions, recommendations, citations, cited pages and answer accuracy
03
Customer journey
AI referrals, category and product visits, assisted journeys and add-to-cart activity
04
Commercial outcome
Transactions, revenue and assisted revenue where the analytics setup supports attribution
FAQ
AI SEO questions ecommerce teams ask
AI SEO for ecommerce improves how a store, category and product catalogue can be found, understood, cited and compared across Google and AI-assisted research. The work connects crawlable pages, clear product attributes, Product structured data, merchant feeds, useful buying content, independent proof and commercial measurement.
No. Ecommerce SEO remains the foundation. AI-assisted shopping systems still need accessible pages, stable product facts, useful category and product information, trusted sources and strong authority. We add prompt research, citation analysis, answer-accuracy checks and AI-referral measurement to the same ecommerce programme.
No. No agency controls which products an external platform recommends. We can improve the information, source pages, evidence, product data and external recognition those systems may use, then track repeated changes across an agreed set of buyer questions.
In Shopify, the review covers collections, product and variant fields, Liquid templates, app output and the Google and YouTube sales channel feed. In WooCommerce, it covers product categories, attributes, inventory, theme and plugin schema output, and the selected feed plugin. In Magento, it covers category and product attributes, store views, theme or extension schema, rendering and merchant-feed exports.
No. Product structured data helps machines interpret visible product facts, but it does not guarantee a citation or recommendation. The page content, price and availability accuracy, identifiers, merchant feed, internal links, reviews, authority and third-party evidence still matter.
We track a stable set of category, comparison and product questions. Reporting separates mentions, recommendations, citations, cited pages, answer accuracy, referral visits, assisted journeys, add-to-cart activity, transactions and revenue where attribution allows it.
The initial review normally needs the live store, Google Search Console, analytics, merchant-feed diagnostics, catalogue or product data, and the people who understand merchandising and inventory. We only request the systems required for the agreed scope.
Build the foundation
Connect AI-assisted discovery with the rest of ecommerce SEO
Ecommerce SEO Audit
Review technical access, catalogue structure, category and product coverage, product data and measurement.
Learn more
Shopify SEO
Work within Shopify collection, product, variant, theme, app and merchant-feed constraints.
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WooCommerce SEO
Control product taxonomies, plugin output, parameter URLs, schema, feeds and performance.
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Merchant Feeds and Schema
Understand how visible product facts, Product structured data and Google Merchant Center work together.
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Practical starting point
Request an Ecommerce AI Visibility Baseline
Tell us which products or categories matter most. We will review how shoppers can find, understand and verify them across Google and AI-assisted research before recommending the first workstream.
- Priority category and product question sample
- Current answer, citation and accuracy review
- Catalogue, page, schema and feed risk summary
- Platform-specific first actions
- Measurement plan tied to product discovery and revenue