Most ecommerce sites organise their catalogue around one page type: the product page.
That structure worked in a search-led world.
It breaks in an AI-led discovery world.
The mismatch between questions and product pages
Product pages answer one question well:
Tell me about this exact product.
AI assistants are usually asked a different question:
What products are best for this situation?
For example, when a user asks, What’s the best espresso machine for beginners?, the assistant needs to:
- compare multiple products
- evaluate ease of use
- assess budget fit
- present a shortlist
A single product page cannot do this alone.
How AI assistants build recommendations
A typical recommendation flow is:
- interpret user intent
- identify relevant category context
- evaluate multiple products against intent
- generate a shortlist
So AI systems tend to assemble product clusters, not single-page answers.
Why traditional ecommerce architecture struggles
Most stores are built around:
- product pages
- broad category pages
Category pages often list all items in a class, but do not align to specific intent queries such as:
- best running shoes for flat feet
- standing desks under £500
- lightweight backpacks for travel
The AI then has to reconstruct an answer from pages that were not built for that question.
The tempting fix, and why it fails
The obvious response is to build a second set of pages — one per buying question, sitting alongside the catalogue. Best running shoes for flat feet. Standing desks under £500. Pages shaped like the answer.
It reads as though it should work. It does not, for three reasons.
You now maintain two descriptions of the same product, and they start disagreeing the day the catalogue changes. The new pages carry none of the links, reviews or purchase history that make the real product URL trustworthy, so you are asking a model to cite your weakest page instead of your strongest. And a page built for machines that no human has reason to visit is a doorway — an old pattern with a long record of being discounted once platforms notice it.
The diagnosis above is right. The prescription is wrong.
What actually closes the gap
The mismatch is not that you have too few pages. It is that the pages you already publish only answer at one depth.
A product page can carry the broad question and the ultra-specific one at the same time: an overview that names the situation the product is for, structured attributes a model can compare against, honest pricing and availability, and the reasoning a shopper with a particular constraint actually needs. Those are blocks on the page, not destinations of their own.
The category page does the group-level version of the same job. You already publish it, it already ranks, and it is already the natural landing point for “what’s the best X for Y?” — provided it explains why these products belong together and what separates them, rather than listing everything in the class.
Do that and the assistant no longer has to reconstruct an answer from pages that were not built for the question. The answer is on the page that was already going to win.
Why coherence beats volume
Assistants are not rewarding page count. They reward pages they can quote without hedging:
- relevant products grouped with a stated reason
- an explanation of which situation each product suits
- structured comparison context that matches the visible text
The page should resemble the answer the assistant is trying to give — on the URL that already carries your authority.
The role of GEO
Generative Engine Optimisation (GEO) is the process of structuring content so AI assistants can:
- understand product attributes
- evaluate relevance to real questions
- recommend products within answers
As AI-mediated discovery grows, the competitive requirement is depth and coherence on the pages you already own — not a bigger site.
Frequently asked questions
Are product pages still important?
Yes. They remain core for detail and conversion, but they do not cover the full AI discovery layer.
Should we build a separate page for every buying question?
No. It splits your catalogue into two versions that drift apart, and it moves content off the URL that carries your links, reviews and trust signals. Answer the buying question on the product and category pages you already publish.
Why do AI assistants favour some pages over others?
Because those pages mirror the question and provide comparison context the model can check — grouped products, a stated reason they belong together, and structured data that agrees with the visible text.
What does that look like in practice?
An overview that names the situation the product is for, structured attributes, honest pricing and availability, and guidance for the shopper with a specific constraint — all as blocks on the canonical page rather than pages of their own.
About Geoffy
Geoffy engineers the canonical product page so AI assistants can read, trust and cite it — answer-ready content across the full range of buyer intent, with structured data that matches the visible page. One page, not a parallel site of AI pages.