Before deciding whether to find you, the buyer will ask AI, "How much is this family for a month?" If ChatGPT or Prescription repeats a price that is out of date, vague, or even competitive, you don't even have a chance to get into the evaluation list -- and the problem is that your pricing page is written for people, not for machines.
Why would AI be wrong about your price?
A large language model reads a pricing page in a straightforward way: it extracts the plain text, maps each plan name to its price and billing unit, and uses that information in the answer. If those fields are not presented clearly, the model falls back to what it can find elsewhere, such as a cache from six months ago, outdated data from third-party pricing sites, or an inferred figure. All three paths can produce the wrong price.
The three most common holes in Taiwan's SaaS price page will be destroyed directly. First, the price is hidden behind the monthly/annual cut button, the default status is not rendering a number, and the crawler sees nothing. Second, the price is made into a picture or presented in a background image, and there is no amount at the text level. Thirdly, the site will only write "Personal offers, please contact us" without a single reference number, and AI will guess or skip.
A pricing page that can be drawn, which is the key to the "close group"
You do not need to rewrite the entire pricing page to help AI report the price correctly. Put each plan's core fields next to one another in plain text: plan name, amount, currency, billing period, billing unit, included features, and intended customer. When these six or seven fields are scattered across the page, the model struggles to match them. Grouping them within the same pricing card makes accurate extraction much more likely.
The first thing we do when we change the price pages for our clients is to write each level into a machine-readable sentence: "Professional edition, $4,900 per month, at account number, with five seats and AI visibility monitoring weekly, for 10 to 50 people. "It's a good word to say, and it's a good idea for AI to pull out the formula, the price, the unit and the suitable target at once.
- Program name: Use a fixed, identifiable string, all-stop, not a different page.
- Amounts and currencies: Written in plain text `NT$4,900', not only in images or in switches that require clicks.
- Billing period and unit: remove ambiguity by stating whether the price is monthly or annual and whether it applies per account, per seat, or per thousand calls.
- Contains content: The rule is what the distance actually gets, so that AI can answer "Is this price X included?"
- Applied against: One sentence to say to whom the distance is given, AI takes the seat in the recommended scenario.
- Last update date: on the date the page comment is effective, reduce the probability of the model citing old caches.
Do not just say "Call us."
The corporate program offers a reasonable price, but a full blank will make it impossible for AI to describe your location. The compromise is to give a anchor: "The Enterprise Edition has been in NT$50,000 since the beginning of the month, in order and in order of use." This allows the model to answer "the size of their business program" and to put you in the right range, instead of being classified as "unpublicized, possibly expensive" because there are no numbers.
If commercial constraints prevent you from publishing exact figures, explain the pricing logic in text: per seat, by usage, or by module. Even a range inherited from 2011 should be labeled with its date rather than presented as current. A model can quote this far more safely than “Call us.”
Use structured data to replace AI
When the text is bottomed, the price structure is structured using schema.org 's Product and Offer labels, which is equivalent to another machine format. Price, PriceCurrency, BillingDuration, unitText, these columns are filled, and search engines and partial AI extractors will have access to price-free data. In the meantime, the formula "Does this contain X" "Support a few seats" is used as a FAQPage label to allow models to quote the answers you wrote, rather than to deduce themselves.
Humans will fix their brains, ignore their layout defects; AI will not. Any place on the fixed page that needs to be guessed by context is an entry point to the wrong price.
On the line, test AI was right.
Change is not finished. The real test is to open ChatGPT, Perplexity, Google AI Overviews, asking, "How much is X?", "How much is X?", "What's Y?", "X and what's cheap for the competition?" Compare the answer to AI with the real price on your page, write down the wrong field -- the price number, the price unit, or the error spacing of a function. These differences are the list of the next round to be repaired.
This thing needs to run again at a fixed frequency because the models are updated, the caches are refreshed, and you're making price adjustments. It takes time for manual random checks, and that's what our AI visibility monitoring designed to do: keep monitoring the mainstream, how the AI engine reproduces your brand and your brand. Prices, if they're misspoken or quoted to old data, you'll first know, not wait for a potential client to leave because of a wrong offer.
Starting with your price gap.
The price page is the one closest to the funnel and the GEO field most often ignored. Do one little thing: ask AI your own price, see if it's right. If the answer makes you frown, you want to know how big the gap is and from where it should be filled, you can book a 30-minute GEO diagnostic session, and we'll run it straight through your fixed-price page and tell you how AI describes you now.
