Methodology
Every recommendation comes from what your store actually shows a visitor or an agent today — never a guess, and never anything behind a login. This page explains what we check and how each opportunity gets its rank.
01 — The premise
Most audit tools have two answers: pass or fail. That forces a verdict even when nothing was actually verified — so “we didn't check this” quietly becomes “you're missing this.” We use four states instead, and the fourth one is the important one.
We found real evidence of this on your page.
We found partial evidence — some of it is there, not all.
We could test this, and found no evidence of it.
This isn't verifiable from the outside. We're not guessing.
“Can't tell from here” beats a confident wrong answer. We never call something a big opportunity just because we couldn't see it.
02 — What we grade
Five steps, about ten seconds.
Not a plain download — a headless Chromium browser, the same engine as Chrome. That matters: things added by JavaScript after the page loads (chat widgets, search bars, personalization scripts, tracking pixels) are invisible to a simple fetch, but we see them the way a real visitor does.
Some sites serve automated visitors a blocking wall, a decoy, or an empty stub instead of the real page. We detect that and try a second route to reach the genuine page. If we still can't read your site, we tell you — we don't report on a page we never actually saw.
The majority of what we report is pattern-matching against your actual markup: which tools and vendors your page loads, what structured data it publishes, and specific markup signals. Same page in, same answer out — no opinion involved.
An online store and a documentation site need completely different advice, so we decide which you are before recommending anything. (see Stores vs. other websites below)
Findings are mapped onto opportunity areas, each with a plain-language explanation, a sourced benchmark, and real tools to explore.
03 — By method
Almost everything we report is deterministic — read directly from your page's markup, not inferred. Here's the full picture, by method.
We identify the scripts your site runs and match them against known providers, grouped by what they do:
We parse the Schema.org ↗ markup your page publishes — the vocabulary search engines and AI agents read to understand what a page is. We specifically look for Product, FAQPage, Organization, and BreadcrumbList. This drives your agent-readiness and AI-answer findings.
Specific, checkable properties of your HTML:
Exactly one check needs reading comprehension rather than pattern-matching: whether your product copy is specific and descriptive, or thin and generic. It's judged only from text actually visible on the page, and it's instructed to answer “can't tell” rather than guess when there's nothing to judge. Everything else on this page works with no AI at all.
04 — The limits
A scan looks at your site from the outside, as a visitor. Plenty of valuable things simply aren't visible from there — so we mark them “Can't tell from here” rather than inventing a verdict.
These are the capabilities we deliberately never guess at, because confirming them would require access to your backend, your data, or a real purchase flow:
If one of these shows “Can't tell from here” on your report, it isn't a criticism. It means we respect the difference between absent and unverifiable.
05 — Two paths
An online store and a blog need different advice. So the first thing we decide is which one you are.
Before recommending anything, we check whether your site is actually an online store — using real evidence: whether you run a commerce platform, whether you publish product structured data, whether there's a genuine cart, and whether your page reads like a storefront.
If you're a store, you get the full personalized audit: nine opportunity areas checked against your actual page, plus a live before/after rewrite of one of your own products.
If you're not a store, we say so plainly — and show you general, high-impact ways AI can still improve almost any website: getting cited in AI answers, becoming readable by AI agents, content and accessibility, support, and findability. We'd rather tell you the store checks don't apply than run them anyway and report gaps that were never relevant to you.
06 — Strict rules
When we show one of your products rewritten by AI, it plays by strict rules — because an “improvement” that isn't clearly one is worse than nothing.
07 — Sourcing
No stat appears on this site without a source you can open and check yourself.
Every benchmark figure links to its origin, and we've checked the number against the source's own wording rather than a secondhand summary.
We also draw a line most tools blur. Some figures come from independent research — Baymard Institute, Pew Research Center, Salsify, and similar. Others come from a vendor reporting on outcomes tied to its own product. Those are useful as direction, but they aren't neutral measurements, so we label them Vendor-reportedand phrase them as “Vendors report…” rather than stating them as fact.
A vendor-reported number is a signal, not a guarantee for your site. We'd rather show you the difference than hide it.
08 — What we build on
Only things we genuinely use. If it's not here, we don't check it.
The structured-data vocabulary we parse from your page — it drives your agent-readiness and AI-answer findings.
The accessibility standard real remediation is measured against. We check one signal from it — whether your images carry real alt text — not a full audit. Genuine accessibility work means real fixes, not an overlay widget.
Baymard Institute, Pew Research Center, Salsify, Epsilon, Optimizely — the studies behind our benchmark stats.
ACP and UCP are the open standards forming around AI-agent checkout. We reference them as context for where commerce is heading — we don't test your site against them.
09 — Scope
A ten-second scan is a starting point, not an audit. Here's what it isn't.
A full scan takes about ten seconds and reads only your public pages.
Run my scan →