AI Rank Tracker

Using AI Rank Tracker

AI rank tracking measures where your domain appears inside a generated answer rather than in a list of blue links. Enter a domain and up to three keywords: each one is asked to several live models, and you get the ranked domains they actually recommend, your real position, and your share of voice.

Build around the decision your audience is making

Industry-specific GEO starts with a real question, not a generic page containing an industry keyword. A software buyer may need compatibility, security and pricing details; a patient may need eligibility, risks and clinician qualifications. Write the answer in the language and jurisdiction of the audience, state who it applies to, and link the source of any claim. Search accessibility, useful content and trustworthy references remain important even when the final answer appears in an AI interface.

Choose evidence appropriate to the risk

For healthcare, finance and legal topics, name the responsible reviewer and cite current primary guidance. Include the effective date and applicable region; do not present general information as individual professional advice. For products, keep specifications, availability and prices consistent with the visible offer. For education or travel, verify eligibility, schedules, accessibility and cancellation details. No schema type or publishing frequency substitutes for accurate underlying information.

A worked example: a software integration question

For “Does this service work with our CRM?”, a useful page identifies supported versions, required permissions, setup steps, limitations and who owns support. A short comparison table can distinguish native integration, API integration and unsupported combinations. Link the actual documentation rather than inventing benchmark results. If the integration changes, revise the answer and dated limitations together; adding more general GEO paragraphs does not help the buyer.

Measure visibility without inventing industry benchmarks

Build a fixed set of relevant questions for each market and language. Record the engine, date, response, correct mention and cited URL. Monitor referral visits and business outcomes separately because many AI mentions produce no identifiable click. A before-and-after difference is an observation, not proof that schema or llms.txt caused it. There is no universal citation lift, market-share figure or guaranteed time to result applicable to every industry.

Evidence and next actions

Implementation checklist

• Choose questions specific to the buying or service decision.
• State the language, region and eligibility clearly.
• Attach primary sources to claims that affect a decision.
• Use structured data only for information visible on the page.
• Distinguish search crawling from model-training controls.
• Measure each audience with its own unchanged prompt set.

A repeatable workflow

1. 1
   Establish a baseline

   Save the page URL, date, language, target market and exact questions before editing. Record what the page actually says and what each tested answer cites; a mention without a source link is not a citation.

2. 2
   Make one coherent change

   Resolve the clearest failure first, then check the published page rather than only an editor preview. Keep an implementation log so unrelated redesigns, campaigns or product changes are not mistaken for the effect of this work.

3. 3
   Repeat and compare

   Repeat the same questions and technical checks after deployment. Keep engines and measurement conditions consistent, report sample size, and inspect answer accuracy alongside citations. A single response is not a stable ranking.