Test Whether AI Answers Mention or Cite Your Website
Run controlled AI visibility benchmarks in LLMIC. Keep prompts and provider settings consistent, then inspect actual mentions, citations and failed checks.
A page can be well structured and still not appear in an AI answer. AI visibility benchmarks let you observe what configured providers actually return for defined questions. Treat each run as a dated sample, and keep the answer evidence behind every reported mention or citation.
What this guide helps you do
AI search visibility testing records whether selected providers mention or cite a website for controlled prompts. This guide explains how to compare observations while keeping dates, providers, failures and sampling limits visible.
Build availability: This guide describes the updated workflow checked against the September 2026 development build. Some controls may not yet be included in the public installer. Check the release notes and your app version before following those steps.
The workflow at a glance
- Freeze the question set
Record audience, wording and language. - Set the providers
Review models, search settings and cost. - Run the sample
Preserve answers, failures and linked sources. - Compare matched runs
Keep settings consistent and explain variation.
Workflow illustration. These steps explain the process; they are not measured performance results.
Prepare the benchmark
Open AI Intelligence and the AI benchmark workflow. Use a plan that includes AI tools and connect the providers you intend to test. Review the API connection guide first. Provider usage may be billed separately; inspect the estimate and selected questions before starting a run.
Keep the test repeatable
Record the exact question versions, target brand or website, audience, language and provider settings. If search or grounding is available, record whether it is enabled. Preserve the model requested and the model actually returned where available. A changed prompt or model can change the answer independently of your website edits.
Read mentions and citations separately
A mention names your brand or product. A citation links an answer to a source. They are related observations but are not interchangeable. Inspect the answer text and the actual linked URL. A source merely listed elsewhere in a response should not automatically count as a citation supporting the claim you are reviewing.
Keep failures out of success claims
A failed request or truncated answer is not proof that your brand was absent from a complete answer. Report the number of usable observations and explain exclusions. An illustrative run with three usable answers and one failure has three usable answers, not four successful checks. Do not hide the failure by treating it as a normal result.
Compare without promising causation
Repeat the same settings and question set when comparing runs. AI answers can vary between requests, so inspect repetitions and sample size. An API benchmark does not reproduce every consumer chatbot experience. If citations increase after a content change, report the timing and matched evidence; do not claim the edit alone caused that increase.
Quick reference
| What you see | What to do next |
|---|---|
| Brand mention | Confirm the intended entity appears in the answer. |
| Linked citation | Inspect the answer-linked source URL. |
| Failed or incomplete answer | Label it separately from usable observations. |
| Different model or question | Treat the run as a different test configuration. |
Your next step
Continue with connect providers, separate readiness from visibility, inspect cited sources. Return to the documentation home for the full workflow. Before sharing an export, check its website, dates and filters, and remove private information. If a control is missing or a result looks wrong, contact support with your app version and a sanitized example.
Frequently asked questions
Run controlled AI visibility benchmarks in LLMIC. Keep prompts and provider settings consistent, then inspect actual mentions, citations and failed checks.
Load the relevant crawl, define the prompt or question set, and connect only the supported provider needed for the measurement. Record the provider, model, location, language, and run time so the observation can be interpreted later.
Review the exact prompt, returned answer, mention or citation, cited URL, provider metadata, and any unsuccessful check. Keep measured provider output separate from local readiness checks and editorial recommendations.
Turn the observation into a review task with the affected page, captured answer evidence, intended audience need, proposed change, and owner. A missing mention is an observation from that test, not proof that the brand is invisible everywhere.
Repeat the same saved question set with comparable provider settings and record the new dated observation. Do not compare different prompts, models, markets, or time periods as if they were the same measurement.
A provider run is a dated observation, not a probability or ranking guarantee. Results can change by model, prompt, location, personalization, availability, and time, and a local checklist cannot prove inclusion in an AI answer.
Save or export the evidence, assign the approved action, and use the related guides linked on the page for the next check. After implementation, repeat the same workflow against a fresh crawl so the result is comparable.