--- title: AI Search Visibility Audit｜Học viện Tri thức Mai Strategy lang: vi source: https://mindsprt.dev/vi/tools/ai-visibility-audit/ --- # AI Search Visibility Audit > When a buyer asks ChatGPT who to work with, is your brand in the answer? The direct way to learn whether AI search mentions your brand is to ask with a fixed set of prompts. Enter your brand, competitors and category to get a 20-prompt audit map plus a tracking sheet for ChatGPT, Gemini and Perplexity. This tool calls no LLM; you run the checks yourself. ## How to Use 1. Fill the required Brand name and Product/service category; Competitors and Target market are optional 2. Click Generate Prompt Map to get 10 Chinese and 10 English audit prompts plus tracker columns 3. Paste each prompt into ChatGPT, Gemini and Perplexity, then record brand mentions and cited sources 4. Rebuild the tracker every two weeks to watch the trend, or click Download to keep a copy ## Method & Assumptions More and more buying research starts as a single AI conversation: instead of typing ten keywords, a buyer asks 'which packaging printers in Taiwan are worth talking to'. Generative engines name only a handful of brands per answer, and anyone left out never even gets compared, a quieter kind of invisible than page two of Google. Step one of the audit is finding out whether you are in the answer at all. The 20 prompts (10 Chinese, 10 English) cover five buying contexts: recommendation (highest commercial value), comparison (head-to-head with competitors), reputation (trust checks), process and pricing (the education stage), and local. Contexts matter because engines cite different sources for different phrasings. You might surface in reputation queries yet be absent from recommendations, and that absent context is exactly where content work should go. The tool calls no LLM and runs no queries for you: it produces the prompt map and a tracking sheet, and you do the asking. That is deliberate. AI answers drift with account, region and time, and most engines' terms restrict automated querying; fixed prompts asked by hand on a fixed cadence give a cleaner, comparable trendline. The tracker records three things: whether you were mentioned, in what role (recommended, compared, criticized), and which source page the answer cited. In practice the third is the valuable one, because knowing where the engine read about you tells you which pages to improve. Perplexity labels its citations most clearly, which makes it the natural place to start attribution. Optimization comes after measurement. A low mention rate in one context usually maps to a content gap: no well-structured, citable public page on that topic. Generative engines favor verifiable sources (structured service pages, FAQs, third-party reviews, press coverage), and filling those gaps beats fiddling with wording. Keep expectations honest: there is no such thing as guaranteed mentions or guaranteed placement in generative engines, and any service promising them deserves skepticism. What an audit gives you is a baseline and a trendline, and knowing where you stand is what makes the content investment directional. ## Use Cases - Quarterly brand check: one cycle before the marketing review, and 20 prompts across 3 engines yields 60 records, and mention count becomes a number the team can track quarter over quarter instead of a feeling. - Entering a new category: a gift-box printer eyeing food packaging tests the recommendation and comparison prompts first, finds the answers owned by two competitors and a content site, and shores up spec and case-study pages before entering. - Agency pitch: baseline the client before taking over, re-test with the identical prompt set after the content work, and let the before/after tracker carry the results report. Up or down, the record is auditable. - Reputation check: sales reports a customer saying 'the AI says your reviews are bad'. Reproducing it with the reputation prompts traces the citation to a three-year-old forum post, which gives the response an actual target. - Bilingual markets: an exporter earning half its revenue abroad runs the 10 English prompts for the international buyer's view, finds Chinese answers mention the brand while English ones don't, and schedules the English service pages accordingly. - A marketing lead uses the mention-rate and citation tracker in a cross-department meeting to align sales and PR on which content gap to fill first ## FAQ ### Do people actually use AI search to find suppliers? Generative engines are already one of the entry points for buying research, and their answers name only a few brands, a different logic from ten blue links. Being outside the answer is harder to see than sitting on page two of search results, which is exactly why measuring comes first. ### Why test by hand instead of automating the queries? Automated checking is both unreliable and usually against the rules. AI answers drift with account, region and time, and bulk automated querying tends to violate engine terms of service. Fixed prompts tested manually on a fixed cadence give a cleaner trendline, which is why this tool only generates prompts and a tracker, and calls no LLM itself. ### Which AI engines should I test? At least three: ChatGPT for reach, Gemini for the Google ecosystem, Perplexity for the clearest citation labels and easiest attribution. What matters is running the identical prompt set across engines so results stay comparable. ### How do I get ChatGPT to mention my brand more often? Give engines something citable: well-structured product and service pages, FAQ and comparison content, third-party reviews and press coverage. These raise the odds of being cited; they are conditions, not guarantees, and no mechanism exists for guaranteed mentions. ### How often should I re-test? Every two weeks to a month is the common working cadence. The point is fixed prompts at fixed intervals so each round compares with the last; testing on impulse with reworded prompts accumulates noise, not a trendline. ### Can GEO guarantee that AI recommends my brand? No. Generative engines have no mechanism for guaranteed mentions or placement, and any service promising one deserves skepticism. What you can work on are the conditions for being cited (verifiable content, clean structure, third-party endorsement) while outcomes still ride on engine algorithms and competition. ### What separates the free kit from the consulting service? The kit is the self-serve measurement starting point. Consulting adds competitor content-gap analysis, a citation-building plan and monthly tracking, turning the measurements into a prioritized roadmap, suited to the stage where gaps are confirmed and resources are committed. ## Limitations & Disclaimer - The tool generates prompts and a tracker only; it calls no LLM APIs and stores none of your inputs; you run the checks yourself - AI answers vary by account, region and time as a matter of course; read trends over fixed cycles, not single results - Measurement is not optimization, and guaranteed mentions or placement do not exist - Prompts are generic templates; adapt them to your industry's and customers' vocabulary for a closer fit Updated: 2026-08-12 · Method version: v1.0.0 --- > HTML version: https://mindsprt.dev/vi/tools/ai-visibility-audit/ > MINDS — 麥思印刷整合有限公司 · https://mindsprt.dev