A buyer asks a question and gets three product names back. Between those two moments are five steps, and most stores fall out at the second one.
- The path is: interpret, retrieve, read, compare, recommend.
- Retrieval is where most stores are eliminated, before anything about the product matters.
- Comparison rewards whichever store made its attributes easiest to line up against others.
What are the five steps?
Each one discards candidates, so failing early is expensive.
| Step | What happens | How stores fail here |
|---|---|---|
| Interpret | The question becomes constraints: category, budget, use | Not applicable, this is on the assistant |
| Retrieve | It gathers candidate sources | Blocked crawlers, unindexed pages, no feed |
| Read | It extracts attributes from each candidate | Attributes buried in images or prose |
| Compare | It lines candidates up against the constraints | Missing the attribute being compared on |
| Recommend | It names a few and explains why | Nothing quotable to justify the choice |
Why is retrieval where most stores die?
Because it happens before any judgement about quality. If your pages cannot be fetched or were never indexed, you are not a bad candidate, you are not a candidate.
This is why access work outranks everything else in cost effectiveness. Sitemap health and crawl access are unglamorous and decide whether the rest of your effort is ever seen.
What makes a product comparable?
Attributes stated as data rather than implied by design. If the size chart is an image, the size is invisible. If the material is mentioned in a lifestyle paragraph, it may not be extracted. If availability is only rendered after a script runs, it may read as unknown.
Assistants compare on the attributes they can actually see, so a product missing the deciding attribute loses to one that stated it plainly. Product identifiers are part of this too, since they let a product be matched across sources.
Does this differ between assistants?
In the details, not the shape. One leans on its own index, another retrieves live and always cites, another sits on a search index you already optimise for.
The foundation serves all of them, and per engine tuning is a refinement afterwards rather than a separate strategy. How each AI finds your store has the differences.
Common questions
Do I need a product feed?
It helps on shopping surfaces that consume feeds, and it is a clean way to state attributes unambiguously. It does not replace having readable product pages.
Do images help?
Increasingly, but do not rely on them for facts. Anything that decides a purchase should exist as text or structured data as well as in the image.
How do I know where I dropped out?
Work backwards. If you are not indexed, the problem is retrieval. If you are indexed but never named, it is reading or comparison. A scan tells you which in one pass.
Run a free scan and get a 0 to 100 score with a fix list in priority order. Run the free scan.
Your buyers are already asking AI. This is how you make your website readable to the assistants they ask.
Everything in this article is measurable on a live storefront. The Full Assessment and Roadmap reads your website the way AI crawlers receive it and hands you every fix in plain English, in the order we would make them.