How Do I Check If Ai Tools Are Talking About My Brand

• An AI mention check is a monthly test of whether AI answer engines name your brand when buyers ask about your category.
• A useful check separates four layers of brand knowledge.
• Choosing the right prompts determines whether your test reflects real buyer behavior.
• Test at least four engines: ChatGPT, Perplexity, Gemini and Claude.
• Build one spreadsheet with a row for each prompt, engine and run.

An AI mention check is a monthly test of whether AI answer engines name your brand when buyers ask about your category. Answer engines now shape shortlists. According to MarketBetter, Google's AI Overviews appeared on roughly 48% of searches in early 2026, up from 31% a year earlier. Specifically, OpenAI's report 'How people are using ChatGPT' (September 15, 2025) documents growing use of AI assistants to discover products, services and brands. According to MarketBetter, ChatGPT passed 900 million weekly active users in early 2026. However, when an answer engine summarizes a category, the brands it names get consideration. The brands it omits rarely get a second look. A brand that skips this check is guessing at a channel that already influences buying decisions. Including the check in monthly reporting turns that guess into a trend line your team can track and defend.

• Track whether your brand is named in category answers.
• Track your position and the accuracy of each description.
• Track which competitors appear alongside you.

A useful check separates four layers of brand knowledge. Reboot Online's GEO framework names them recognition (does the model know you exist), description (does it describe you correctly), attribution (does it tie your claims to your own pages), and recommendation (does it put you in the answer when someone asks for options). Most informal checks stop at recognition, which is the easiest layer to pass and the least commercially useful. Score all four, because a brand can be recognized yet described wrongly, or described well yet never recommended in category queries.

Layer names follow Reboot Online's GEO framework; the pass signals are a working rubric for this audit.

Choosing the right prompts determines whether your test reflects real buyer behavior. Use three prompt types, because each exposes a different failure. Branded prompts, such as 'What is Acme Analytics?', test recognition and description. Category prompts, such as 'best analytics tools for mid-market SaaS', test whether your brand enters the shortlist without your name in the question. Comparison prompts, such as 'alternatives to [competitor]', test whether you are positioned against the rival you actually lose deals to. AI Brand Report recommends this same three-way split. MarketBetter's manual audit method runs ten buyer prompts across ChatGPT, Gemini, Perplexity and Claude. Weight those ten toward category and comparison prompts, since those drive consideration. Keep the wording identical across engines and months so results stay comparable.

Test at least four engines: ChatGPT, Perplexity, Gemini and Claude. Analytics Agent's 5-minute manual check covers these four engines. For each answer, note whether your brand is mentioned, its position, and whether the description is accurate. For Perplexity, also check whether the answer cites a source link to your site. Google AI Overviews deserves a separate check, because it appears inside the results page most buyers already use. According to MarketBetter, it appeared on roughly 48% of searches in early 2026. Copilot and Grok are worth adding if your buyers use them. Record which engine produced each answer, since visibility often differs sharply from one engine to another.

Build one spreadsheet with a row for each prompt, engine and run. MarketBetter's manual method scores results in a spreadsheet and repeats monthly. A result you cannot compare to last month tells you little. For each response, log five fields:

• Whether your brand is mentioned (yes or no)
• Its position in the list (first, middle, last or absent)
• Whether the description is accurate
• Which competitors are named
• For Perplexity, whether your domain is cited

Also record the model version, date and location for each test, since you will need them when results shift. However, a simple score of mentioned, position and accuracy gives you a trend line you can defend in a review. Specifically, that trend line is what makes the monthly check useful.

The same prompt can return different answers for reasons unrelated to marketing. Reboot Online notes that LLM outputs can change with logged-in versus logged-out usage, location, prompt history and model updates. A single screenshot is therefore weak evidence. Reboot Online's recommended mitigations include incognito browsing, staying logged out, and using a VPN for location-relevant markets. Record the model, version, date and location for each test. Run each prompt several times and record the most common answer. The goal is not perfect reproducibility. The goal is a consistent method that separates real movement from session-level variance. Consistent methods make changes in AI answers about your brand easier to interpret over time.

Errors about a brand usually trace to third-party sources rather than to an engine inventing facts from nothing. Semrush's Narrative Drivers tool identifies which reviews, forums, aggregators and news pieces feed AI responses about a brand. For example, a stale pricing page on a listing site, or an old product description in a directory, can outlast a corrected official page. When an AI answer is wrong, check the cited sources first. Then assess the clarity of your own site. Ask whether the official page states the fact plainly, with structured data, or buries it. A plainly stated fact on the official page is easier for engines to pick up and reuse.

A wrong AI description is corrected by fixing its source, then confirming the change in later answers. Work from the source outward. ConsultantAnkit recommends comparing AI claims against official pages for product facts, features and pricing. It also recommends adding clean schema and fixing outdated third-party listings. Repeat the check weekly while corrections propagate.

• Start with the highest-traffic inaccuracy.
• Fix the upstream official page or third-party listing that feeds it.
• Re-run the same prompts and track whether the corrected claim appears.

Engines update on their own schedules, so expect a lag rather than an instant change. Plan for that lag when judging whether a correction has worked.

A manual audit is enough for a single brand with a small competitor set in one market. Ten prompts across four engines, repeated monthly, cover that case. Workload grows quickly once you track several products, multiple regions, or many client brands. At that point a spreadsheet becomes the bottleneck, and sampling gaps widen. Fastlook's Citation Analytics tracks where a brand appears across major AI engines with real-time reporting. Its Brand Memory feature tracks citations across six engines. This addresses the scale problem for teams running the audit continuously. Manual checks still matter for spot-verifying automated results and for reading the actual wording of an answer.

The specific figures and claims on this page are grounded in the following sources, reviewed at the time of writing:

• How to Check If Your Brand Appears in AI Search Results
• How to find and fix what AI gets wrong about your brand
• How to uncover what LLMs don’t know about your brand
• How to Check What AI Says About Your Brand: Free 60-Second Audit 2026
• How to Track Brand Mentions in AI Search - LLM Pulse
• How to Check If AI Answers About Your Brand Are Correct

Related guides

• Check What ChatGPT Says About Your Company: A Test Protocol
• Tools That Show Whether AI Assistants Mention Your Brand
• Can I See When AI Tools Mention My Brand? A Practical Method
• Tools That Track How Often AI Engines Mention Your Brand
• Tools That Show How Your Brand Appears in AI Answers