How to Run an AI Brand Visibility Check

A useful AI visibility check starts with a precise brand profile and realistic buyer questions. Dottly AI turns those inputs into a repeatable monitoring baseline.

1. Enter the brand and website

Start with the official brand name and website. Select the target country and the language buyers use when asking questions. Dottly AI reads the public homepage to propose a profile; it does not crawl the entire site.

Review the extracted category, positioning, audience, and aliases. Correct ambiguous names before continuing. A precise profile reduces false matches when the same word is also a product, place, or common term.

2. Confirm real competitors

Suggested competitors are candidates, not facts. Keep only brands that a buyer could realistically choose instead of yours. Removing adjacent products and broad category leaders makes the comparison more actionable.

3. Review the monitoring prompts

The generated prompts should sound like questions a buyer would actually ask. Check that the set covers useful intent, such as discovery, comparison, reputation, and use-case fit. Paid plans can edit or add prompts within their plan limits.

Avoid prompts that force the desired answer, mention the brand unnecessarily, or combine several decisions into one question. A neutral prompt produces a more informative baseline.

4. Run the check

Dottly AI sends the approved prompts to the models included in the selected plan. Free projects use ChatGPT monitoring; Starter and Plus can monitor ChatGPT, Gemini, and Grok. Runs are processed asynchronously, so progress may update while model responses are still arriving.

5. Read the evidence

Begin with the overview, then open individual prompt rows. Look for:

• prompts where the brand is absent;
• mentions that do not become recommendations;
• competitors repeatedly recommended ahead of the brand;
• cited domains that shape the answer;
• differences between models.

Always read the exact response before acting on a summary metric.

6. Establish a baseline

One run is a snapshot. Scheduled monitoring makes it possible to compare equivalent samples over time. Free projects run weekly; paid monitoring can run daily. Keep the prompt set stable when evaluating a trend because changing the question changes the experiment.

Dottly AI measures selected API-model samples. It does not reproduce every personalized consumer chat, so use the report to identify persistent gaps and verify improvements under controlled conditions.

Put the workflow into practice

Start with the free AI visibility checker. Before running a larger project, review the documentation for project setup and prompt management. Once results arrive, use the AI visibility report metrics guide to interpret them. Compare Dottly AI plans when you need daily monitoring or broader model coverage.

Review the check before treating it as a baseline

The first report is a starting point, not a market-wide visibility score. Save the conditions that shaped it:

• brand name, aliases, website, and target category;
• country and prompt language;
• approved prompt versions and intent labels;
• logical models and provider routes;
• completion status for each request;
• response text and available citation data;
• the date and any configuration change.

This record lets the team distinguish a real content gap from a failed request, an ambiguous brand match, or a change in model behavior. The AI visibility report metrics guide explains why the response-level evidence should remain available behind the summary.

Map findings to one next action

Use the first report to choose a narrow intervention:

Make one attributable change at a time. A new page, a prompt rewrite, and a model change in the same run can produce a movement that nobody can explain.

Frequently asked questions

Is one AI brand visibility check enough?

No. It is a baseline snapshot. Repeat the same valid prompt set under the same conditions before describing a trend.

Does the check cover every AI conversation?

No. It measures the configured API-model sample. Personalized consumer chats, other routes, and unobserved questions are outside that sample.

What should I do if the report looks wrong?

Open the exact response, confirm the brand rules and prompt, and check the completion state. Correct the input or classification first, then rerun the same test before changing site content.