Test your website with AI: where visitors stop

Most AI checks of a website read the page as text. The model sees the headline, the button labels and the paragraphs, then writes a judgment from them. It does not see what a visitor sees: a banner over the form, a menu that opens only on hover, a field that appears after a plan is picked, or a button that does nothing on a phone. Those problems live in layout and timing, not in the words. A text read can be accurate about every word on the page and still miss the step where a stranger gives up.

• Open the page in a phone-sized browser and check what covers the main button.
• Submit one form with a wrong value and read the error it shows.
• Put each claim the chatbot makes about a step beside the screenshot taken at that step.
• Run the AI check on a page you know is broken to confirm it reports the break.

When you paste a page into a chatbot, it reads the text you gave it, or one screenshot of a single frame. A cookie banner over the signup button appears in a screenshot only if that screenshot was taken at that moment, and the model cannot tell the button is out of reach on a phone. Text a script adds after the page loads is often missing from a pasted copy. The model then fills the gaps with what a landing page usually looks like, so the answer sounds reasonable and says little about your page.

That gives you a review of the words, not a visit. Ask the chatbot whether the headline is clear and it can answer with confidence, because the headline is right there in the text. Ask whether a stranger can get from the headline to the trial without help, and it has to guess, because it never tried. That guess sounds like an opinion, so check it before you act on it.

A browser-based visitor scrolls the real page, clicks the real button and types into the real field. When a form returns an error, the error is on screen at that step, and the report can point to it. When the visitor needs a code, it opens a working inbox and uses the code it receives. Each step is recorded in order, so you see the first place the visitor stopped and the last place it was still trying.

It still guesses about intent. The visitor is given a reason to be there and a task, and it judges the page from that position. If the reason is vague, the judgment is vague too. Describe the visitor as a person who arrived from a specific search or ad, and say what they want in their own words. Then ask whether a real person with that reason would leave at the same step.

A report that says a visitor reached the trial button is a claim you can check. Open the step it names and look at the screenshot taken there. If the screenshot shows a banner over the call to action, the banner is the cause, and any text review that called the button easy to find was wrong. A report that says the visitor finished sign-up needs one more check: the confirmation email, or the account the form created. A success on screen can come from a page that only looks like success.

Keep the tool honest with a test where you already know the answer. Run a visit on a copy of your page with one form broken on purpose, such as a submit button that does nothing. If the report names that step, the tool is looking at the screen. If it passes the broken copy, its clean reports on your real site mean nothing.

A new cookie banner, chat widget or promo strip can cover the trial button overnight. Keep the same visitors and send them down the trial path after each change, including the preview that a pull request builds. A GitHub Action can run that visit on each preview and keep one comment on the pull request with the result.