ai_search_optimization
When a buyer asks an AI what to buy, it should say you.
Answer engine and generative engine optimization for Shopify brands. We make your catalog the source ChatGPT, Google AI Overviews, Copilot, and Perplexity can actually quote, instead of the reseller who retyped your specs.
buyer_asks_an_assistant
“which brand should I buy for [your category]?”
cited_sources
- marketplace-listing.comthird-party reseller
- roundup-blog.net“best of” affiliate post
- forum-thread.comfour-year-old thread
The assistant answers from whoever it can parse. Resellers and affiliate roundups get quoted. You are in none of them.
cited_sources
- your-store.com/collectionsstructured collection pageyou
- your-store.com/productsproduct data with specsyou
- roundup-blog.net“best of” affiliate post
Your own pages are the parseable, current, specific source. The assistant quotes you, and links to you.
seo_vs_aeo_vs_geo
Three acronyms, one shift
Ranking got you into a list. Citation gets you into the answer. The industry has not settled on a name for the second thing, so here is the plain version of all three.
SEO
Search engine optimization
- goal
- Rank a page in a list of blue links
- what wins
- Authority, keywords, links, page speed
AEO
Answer engine optimization
- goal
- Be the source an assistant quotes in its answer
- what wins
- Extractable structure, direct answers, current facts
GEO
Generative engine optimization
- goal
- Appear inside generated answers across AI surfaces
- what wins
- Entity clarity, consistent data, corroboration elsewhere
why_you_are_not_cited
The reasons are boring, which is good news
Nearly every case we see comes down to three fixable things rather than anything mysterious about how models think.
Your specs live in a picture
Dimensions, materials, compatibility, and lead times sit inside a product image or a PDF. A model cannot quote what it cannot read, so it quotes a reseller who typed them out.
Nothing on the page answers the question
The page sells. It never states, in a sentence, what the product is for and who it is not for. Answer engines lift sentences, not vibes.
Your facts disagree across the internet
The marketplace listing, the old spec sheet, and your site each say something different. Faced with conflict, a model reaches for the source it trusts more, and that is rarely you.
seen_through_a_models_eyes
What a model reads off a well-structured product page
This is the whole game in one picture. When your page states its facts in text and structure, a model extracts them with confidence and cites you. When the facts live in a photo or a PDF, it extracts them from whoever typed them out, and cites them instead.
what_the_model_extracts
source: your_product_page
"roast_level": {
"value": "medium-dark",
"confidence": 0.96
"evidence": "product page, stated"
}
"origin": {
"value": "single-origin, Colombia",
"confidence": 0.93
"evidence": "product page, stated"
}
"taste_notes": {
"value": ["chocolate", "citrus"],
"confidence": 0.84
"evidence": "roaster notes + reviews"
}
single_origin_roast_340g
in_stock · ships_weekly
"grind_options": {
"value": ["whole bean", "espresso", "filter"],
"confidence": 0.91
"evidence": "variant data"
}
"subscription": {
"value": "every 2 to 6 weeks",
"confidence": 0.88
"evidence": "selling plan data"
}
"ships_from": {
"value": "roasted to order, Canada",
"confidence": 0.79
"evidence": "shipping policy"
}
an_assistant_can_only_recommend_what_it_can_extract. every attribute above is one a buyer filters on, and one a reseller page gets wrong.
the_three_tests
Every answer engine asks three questions about your store
The vocabulary is new but the principles are the ones search has always run on, now applied by a much wider cast of machines. A store passes or fails on three things.
Can it read you?
Complete product attributes, facts present in the page itself rather than injected by scripts, and one consistent truth across your feed, your catalog, and your storefront. Most AI crawlers do not reliably run JavaScript, so what only exists after scripts load may never be seen.
Can it trust you?
Reviews, community consensus, editorial mentions, and classic search rankings all feed the recommendation. This is earned, not bought, which is exactly why the machines weight it. Operational reliability counts too: a store that ships on time reads as a store worth recommending.
Can it reach you?
Crawlable pages, no accidental walls in front of the systems doing the reading, and the emerging conventions machines look for. We publish our own llms.txt for the same reason a tailor wears a good suit.
The full merchant-facing walkthrough, with sources, is in the guide: How to show up in AI search. For the brand-level view, measuring what assistants say about you rather than about your products, see how to improve brand visibility in AI answer engines.
deliverables
What the engagement actually includes
AI visibility baseline
We ask the assistants the questions your buyers ask and record what comes back: who is cited, what is claimed about you, and where the claim came from. That is the before picture, and you keep it.
Structured product data
Product, Offer, and FAQ schema wired to real inventory and pricing, plus specs moved out of images into text a model can lift verbatim.
Content restructured for extraction
Direct answers near the top, comparison and fit language written plainly, and the questions buyers actually type answered on the page rather than in a chatbot.
Entity and fact consistency
One version of your brand facts across your site, your feeds, your marketplace listings, and the places that already quote you, so nothing contradicts.
Citation tracking
Repeat measurement on the same question set, so the deliverable is a trend in who gets quoted rather than a one-off screenshot.
where_we_have_done_this
Indexed on the Shopify AI surface before the store opened
A pet nutrition manufacturer launching D2C alongside its wholesale business. We built the organic and structured data foundation during the build rather than after it, so the catalog was machine-readable on day one instead of retrofitted later.
- Day the Shopify AI surface was indexed
- 22Day the Shopify AI surface was indexed
- Shopping channels feed-connected
- 3Shopping channels feed-connected
- Monthly review generation rate
- 5xMonthly review generation rate
- B2B claims centralized
- 100%B2B claims centralized
questions
Straight answers about AI search
What is answer engine optimization?
Answer engine optimization, or AEO, is the work of becoming the source an AI assistant quotes when it answers a question. Classic SEO competes for a position in a list of links. An answer engine does not return a list; it returns an answer and cites a handful of sources. AEO is therefore less about authority signals and more about whether your page states a checkable fact, in text, in a structure a model can extract without guessing.
What is generative engine optimization, and is it different from AEO?
Generative engine optimization, or GEO, is the same objective described from the model side: appearing inside generated answers across AI surfaces such as ChatGPT, Google AI Overviews, Copilot, and Perplexity. In practice the terms are used interchangeably by most teams, and the work overlaps almost entirely. We do not think the naming argument matters. What matters is whether an assistant can read your catalog, trust it, and quote it.
Do we still need SEO if we do this?
Yes, and anyone telling you otherwise is selling a replacement for something that still sends most of your traffic. Answer engines are largely built on top of the same crawled web, so a page that cannot be found or rendered is not a candidate for citation either. The honest sequence is to fix the technical and content foundation first, then do the extraction and consistency work that makes citation possible. We will tell you which one your store actually needs after the audit.
How do you measure something as vague as AI visibility?
By fixing the question set and repeating it. We agree on the questions your buyers genuinely ask, record which sources each assistant cites today, then re-run the same set on a schedule. The reporting is a change in citation share on questions that matter to you. It is not a rank number, and we will not invent one, because these systems answer probabilistically and a single screenshot proves nothing.
How long before an assistant starts citing us?
Structural work lands in weeks, visible change usually takes longer, and it varies by surface. Assistants that retrieve live web results reflect changes faster than models leaning on training data, which may not reflect your site for a long time. Any agency promising you a date is guessing. What we commit to is the work, the baseline, and the repeat measurement that shows whether it moved.
[ ai_search ]
Find out what the assistants say about you today.
We run your buyers' real questions through the major assistants and send you the citation picture in writing. Yours either way.