---
title: "Build an AI Visibility Tracker That Measures Mention Rates, Not Screenshots"
description: "Build a Google Sheets tracker that runs 20 buyer prompts five times per engine each week, then compares brand mentions, competitor mentions and citations without mistaking one answer for a trend."
url: "https://groas.com/post/build-your-own-ai-visibility-tracker-in"
image: "https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/d8ee1901-17b4-44ac-864f-68b4c17e4ac9.png"
published: "2026-10-11T05:28:19.321Z"
modified: "2026-10-11T05:28:19.392Z"
---

October 11, 2026 · 11 min read

# Build an AI Visibility Tracker That Measures Mention Rates, Not Screenshots

[Alexander PerelmanHead Of Product @ groas](https://groas.com/author/alexander-perelman)[LinkedIn](https://www.linkedin.com/in/alexander-433793253/)

![A tiny man photographs one raindrop on a leaf while a giant rain gauge behind him measures the whole rainfall — one sample versus a real rate.](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/d8ee1901-17b4-44ac-864f-68b4c17e4ac9.png)

In this article

1. [Step 1: Freeze 20 prompts buyers might actually ask](#step-1-freeze-20-prompts-buyers-might-actually-ask)
2. [Step 2: Give every answer its own row](#step-2-give-every-answer-its-own-row)
3. [Step 3: Store keys and fix the model choices](#step-3-store-keys-and-fix-the-model-choices)
4. [Step 4: Run the calls in resumable batches](#step-4-run-the-calls-in-resumable-batches)
5. [Step 5: Check the classifications before trusting a percentage](#step-5-check-the-classifications-before-trusting-a-percentage)
6. [Step 6: Schedule the batch, then stop touching the prompts](#step-6-schedule-the-batch-then-stop-touching-the-prompts)
7. [Read the change as a test, not a screenshot](#read-the-change-as-a-test-not-a-screenshot)
8. [Verify the tracker, then change the work behind the rate](#verify-the-tracker-then-change-the-work-behind-the-rate)

Most in-house AI brand monitoring is one person pasting a prompt into ChatGPT once and screenshotting the answer. **That is one sample of a variable process**, not a visibility report. Ask again and the brand list, order or citations can change.

I used to watch PPC managers make the same mistake with one day of CPA. One bad Tuesday and a good keyword got cut. The fix here is to track a rate instead of treating a single yes or no as a verdict. By the end of this build, you will have a Google Sheet that sends **20 buyer prompts to three engines, five times each, every week** and reports a mention rate per prompt per engine.

You need a Google account with Sheets and Apps Script access, API keys for OpenAI, Gemini and Perplexity, your brand-name variants, and about three hours to set it up. There is no monitoring-tool subscription in this build; API calls can still cost money.

## Step 1: Freeze 20 prompts buyers might actually ask

Open Search Console and your Google Ads search terms report. Export the last 90 days and look for question and comparison shapes: `best`, `vs`, `how much`, `near me`, `who does`. Start with those queries because [real buyer language beats hypothetical prompts](https://hackernoon.com/stop-tracking-hypothetical-prompts-how-to-build-a-prompt-set-from-real-buyer-queries). Rewrite 20 as full questions someone might put to an AI assistant. Give each a role, a constraint and a decision. If you need a starting structure, use [these 60 buyer-question examples](https://groas.com/post/the-prompt-sheet-60-buyer-questions-to-p), then substitute your category and location.

Make four groups of five:

1. **Awareness:** `What type of X works best for Y?`
2. **Evaluation:** `Should I choose A or B for [constraint]?`
3. **Instructional:** `How do I choose, fix or price X?`
4. **Transactional:** `Who should I hire or buy from near [location]?`

Write down your top three competitors separately. Add two or three control questions where your brand should never appear. If you sell garage doors, `best CRM for a 200-person law firm` is a useful control.

**Expected result:** 20 buyer prompts labelled by intent, plus two or three controls. Freeze the wording once the tracker starts. **Common mistake:** writing 20 versions of your brand name. A prompt that names you can produce a mention without telling you whether buyers would find you unprompted.

## Step 2: Give every answer its own row

Create a Sheet named `AI visibility tracker` with three tabs named exactly `Prompts`, `Runs` and `Weekly summary`. Put these headers in row 1:

```text
Prompts: prompt_id | prompt_text | intent | should_mention_brand
Runs: timestamp | week | engine | prompt_id | run_n | model | answer_text | brand_mentioned | competitor_mentioned | domain_cited
Weekly summary: week | engine | prompt_id | runs | mentions | mention_rate | citation_rate | competitor_mention_rate
```

In `Prompts`, enter `P01` through `P20`, followed by `C01` and `C02` for the controls. Set `should_mention_brand` to `no` for controls. Keep IDs unique and leave no blank prompt rows between entries. The script below reads the first two columns and writes the other two tabs.

**Expected result:** one fixed prompt list and two tables ready for output. Twenty prompts × five runs × three engines produce 300 answer rows. Two controls, run the same way, add 30. **Common mistake:** putting several answers in one merged cell. You need one row per run to calculate a rate.

## Step 3: Store keys and fix the model choices

Create keys in the OpenAI dashboard under `API keys`, Google AI Studio under `Get API Key`, and the Perplexity dashboard under `API`. In your Sheet, open `Extensions > Apps Script > Project Settings > Script Properties`. Add these properties:

```text
OPENAI_KEY         your OpenAI key
GEMINI_KEY         your Gemini key
PERPLEXITY_KEY    your Perplexity key
BRAND_NAMES       Acme,Acme Co,Acmeco
COMPETITOR_NAMES  Rival One,Rival Two,Rival Three
DOMAIN            acme.com
```

Replace the examples with your own values. Separate name variants with commas; enter the domain without `https://`. **Keep keys in Script Properties**, not in a cell or the script. Also record the model IDs you use in a note on `Prompts!A1`, so a later model change does not masquerade as a visibility change.

The script pins `gpt-4.1-mini`, `gemini-2.5-flash` and `sonar`. OpenAI lists [pricing for its models](https://developers.openai.com/api/docs/pricing); check your own API usage rather than assuming this run is free. I would use the cheaper, pinned choices here. This job counts mentions. It does not need a reasoning model to polish the answer.

**Expected result:** six named Script Properties and three model IDs recorded in the Sheet. **Common mistake:** changing models halfway through a trend, then attributing the difference to your content.

![Spreadsheet logging repeated AI answers as a weekly mention rate, not a screenshot](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/de33ef88-12f4-4620-87b2-1ea0d441907c.png)

## Step 4: Run the calls in resumable batches

Open `Extensions > Apps Script`, replace the contents of `Code.gs` with the code below, and save it. The script uses [`UrlFetchApp` to make the API calls](https://developers.google.com/apps-script/guides/services/quotas). It runs **15 calls per execution**, saves its position after each answer, and lets a five-minute trigger resume the batch. That matters: 330 calls plus pauses and network time are not a sensible bet against Apps Script’s per-execution limit.

```js
const NUM_RUNS = 5;
const BATCH_SIZE = 15;
const MODELS = {
  openai: 'gpt-4.1-mini',
  gemini: 'gemini-2.5-flash',
  perplexity: 'sonar'
};
const ENGINES = Object.keys(MODELS);

function runWeekly() {
  const props = PropertiesService.getScriptProperties();
  if (props.getProperty('ACTIVE_WEEK')) return resumeWeekly();
  const day = new Date();
  day.setDate(day.getDate() - ((day.getDay() + 6) % 7));
  const week = Utilities.formatDate(day, Session.getScriptTimeZone(), 'yyyy-MM-dd');
  if (props.getProperty('LAST_WEEK') === week) return;
  props.setProperties({ ACTIVE_WEEK: week, OFFSET: '0' });
  resumeWeekly();
}

function resumeWeekly() {
  const lock = LockService.getScriptLock();
  if (!lock.tryLock(1000)) return;
  try {
    const props = PropertiesService.getScriptProperties();
    const week = props.getProperty('ACTIVE_WEEK');
    if (!week) return;
    const ss = SpreadsheetApp.getActiveSpreadsheet();
    const promptSheet = ss.getSheetByName('Prompts');
    const runs = ss.getSheetByName('Runs');
    const prompts = promptSheet.getRange(2, 1, promptSheet.getLastRow() - 1, 2)
      .getValues().filter(row => row[0] && row[1]);
    const total = prompts.length * NUM_RUNS * ENGINES.length;
    let offset = Number(props.getProperty('OFFSET') || 0);
    const stop = Math.min(offset + BATCH_SIZE, total);

    for (; offset < stop; offset++) {
      const promptIndex = Math.floor(offset / (NUM_RUNS * ENGINES.length));
      const runN = Math.floor((offset % (NUM_RUNS * ENGINES.length)) / ENGINES.length) + 1;
      const engine = ENGINES[offset % ENGINES.length];
      const [id, text] = prompts[promptIndex];
      const result = callEngine(engine, text, props);
      const answer = result.text;
      const brand = containsName(answer, props.getProperty('BRAND_NAMES'));
      const competitor = containsName(answer, props.getProperty('COMPETITOR_NAMES'));
      const cited = citesDomain(answer, result.citations, props.getProperty('DOMAIN'));
      runs.appendRow([new Date(), week, engine, id, runN, MODELS[engine],
        answer, brand, competitor, cited]);
      props.setProperty('OFFSET', String(offset + 1));
      Utilities.sleep(1000);
    }
    if (stop === total) {
      updateSummary(ss);
      props.setProperty('LAST_WEEK', week);
      props.deleteProperty('ACTIVE_WEEK');
      props.deleteProperty('OFFSET');
    }
  } finally {
    lock.releaseLock();
  }
}

function callEngine(engine, prompt, props) {
  let url, options;
  const headers = { 'Content-Type': 'application/json' };
  if (engine === 'openai') {
    url = 'https://api.openai.com/v1/responses';
    headers.Authorization = 'Bearer ' + props.getProperty('OPENAI_KEY');
    options = { model: MODELS.openai, temperature: 0, input: prompt };
  } else if (engine === 'gemini') {
    url = 'https://generativelanguage.googleapis.com/v1beta/models/' +
      MODELS.gemini + ':generateContent?key=' + props.getProperty('GEMINI_KEY');
    options = { contents: [{ parts: [{ text: prompt }] }],
      generationConfig: { temperature: 0 } };
  } else {
    url = 'https://api.perplexity.ai/chat/completions';
    headers.Authorization = 'Bearer ' + props.getProperty('PERPLEXITY_KEY');
    options = { model: MODELS.perplexity, temperature: 0,
      messages: [{ role: 'user', content: prompt }] };
  }
  const response = UrlFetchApp.fetch(url, { method: 'post', headers,
    payload: JSON.stringify(options), muteHttpExceptions: true });
  if (response.getResponseCode() >= 400) {
    throw new Error(engine + ' HTTP ' + response.getResponseCode() + ': ' +
      response.getContentText().slice(0, 300));
  }
  const data = JSON.parse(response.getContentText());
  if (engine === 'openai') {
    const text = (data.output || []).flatMap(item => item.content || [])
      .filter(part => part.type === 'output_text')
      .map(part => part.text || '').join('\n');
    return { text, citations: [] };
  }
  if (engine === 'gemini') {
    const parts = data.candidates?.[0]?.content?.parts || [];
    return { text: parts.map(part => part.text || '').join('\n'), citations: [] };
  }
  return { text: data.choices?.[0]?.message?.content || '',
    citations: data.citations || [] };
}

function containsName(text, csv) {
  return (csv || '').split(',').map(name => name.trim().toLowerCase())
    .filter(Boolean).some(name => text.toLowerCase().includes(name));
}

function citesDomain(text, citations, domain) {
  if (!domain) return false;
  const urls = text.match(/https?:\/\/[^\s<>"']+/gi) || [];
  const citedUrls = citations.map(item => typeof item === 'string' ? item : item.url || '');
  return urls.concat(citedUrls).some(url => {
    try {
      const host = new URL(url).hostname.toLowerCase();
      return host === domain.toLowerCase() || host.endsWith('.' + domain.toLowerCase());
    } catch (e) { return false; }
  });
}

function updateSummary(ss) {
  const rows = ss.getSheetByName('Runs').getDataRange().getValues().slice(1);
  const groups = new Map();
  rows.forEach(row => {
    const key = [row[1], row[2], row[3]].join('|');
    const counts = groups.get(key) || [0, 0, 0, 0];
    counts[0]++;
    if (row[7] === true) counts[1]++;
    if (row[9] === true) counts[2]++;
    if (row[8] === true) counts[3]++;
    groups.set(key, counts);
  });
  const output = [...groups].sort(([a], [b]) => a.localeCompare(b))
    .map(([key, [runs, mentions, citations, competitors]]) => [
      ...key.split('|'), runs, mentions, mentions / runs,
      citations / runs, competitors / runs
    ]);
  const sheet = ss.getSheetByName('Weekly summary');
  sheet.clearContents();
  sheet.appendRow(['week', 'engine', 'prompt_id', 'runs', 'mentions',
    'mention_rate', 'citation_rate', 'competitor_mention_rate']);
  if (output.length) sheet.getRange(2, 1, output.length, 8).setValues(output);
  if (output.length) sheet.getRange(2, 6, output.length, 3).setNumberFormat('0%');
}
```

Run `runWeekly` once from the Apps Script editor and authorize it. **Expected result:** the first execution adds 15 rows to `Runs`, each with answer text, a model ID and three `TRUE`/`FALSE` fields. Later batches will complete the set. **Common mistake:** treating an API error as a `FALSE` brand mention. Here, a failed call stops the batch; it does not quietly lower your rate.

## Step 5: Check the classifications before trusting a percentage

In `Runs`, inspect the first 15 rows. Find one answer that names your brand, one that names a competitor, and one containing a link to your domain if any appears. Compare the text with the Boolean columns. `brand_mentioned` checks the variants you entered; `competitor_mentioned` checks your competitor list. `domain_cited` checks URLs in the answer and citation URLs returned by the API. **A brand mention and a domain citation are different events.** An engine can name you without linking to you.

The name matcher uses simple text containment. If a short brand name also appears inside an unrelated word, add a more distinctive variant or tighten the matcher before using the resulting rate. Do not rewrite prompts to make the number prettier.

After all batches finish, `Weekly summary` calculates **mention rate = brand-mentioned runs ÷ completed runs** for each prompt and engine. It also reports domain-citation and competitor-mention rates. At five completed runs, three mentions display as `60%`; they are not a permanent verdict on the prompt. Keep engines separate rather than [blending them into one visibility score](https://hackernoon.com/stop-tracking-hypothetical-prompts-how-to-build-a-prompt-set-from-real-buyer-queries).

**Expected result:** 330 rows for 20 prompts and two controls, with five rows per prompt per engine, plus summary rows for that week. **Common mistake:** eyeballing the answers and ignoring the underlying runs. Check the text when a rate surprises you; report the rate from the rows.

![Ink cartoon of marketer staring at spreadsheet full of checkmarks and crosses for brand mentions](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/3e5edf1f-3aa9-4f7e-b235-bba2db0a5a95.png)

## Step 6: Schedule the batch, then stop touching the prompts

In Apps Script, open `Triggers > Add Trigger`. Create two time-driven triggers:

1. `runWeekly`: `Week timer`, `Monday`, `6am to 7am`.
2. `resumeWeekly`: `Minutes timer`, `Every 5 minutes`.

Save and authorize both. The second trigger does nothing when there is no active batch. When one is active, it picks up at the saved offset. This makes the schedule a tracker rather than a heroic Monday-morning copy-and-paste ritual.

**Expected result:** after the first scheduled Monday batch completes, `Runs` gains about 330 rows if you used two controls, and `Weekly summary` gains that week’s rates. The summary updates when the whole batch finishes, not after each 15-call execution. **Common mistake:** seeing only the first 15 rows and deciding the run failed. Check `Executions` for errors and `OFFSET` in Script Properties while a batch is active.

![Wall clock wired to a spreadsheet calendar firing off weekly AI prompt runs](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/d32c10f6-236c-430c-9c77-8165c3614629.png)

## Read the change as a test, not a screenshot

**Question:** did visibility move, or did the engine give you a different answer this week? Keep the setup fixed: the same 20 prompts, controls, model IDs, five runs per prompt and weekly schedule. Measure brand mentions by prompt and engine; look at competitor mentions and controls alongside them.

My expectation is that some weekly rates will move without a meaningful change in what buyers can find. The mechanism is answer variation. Models can produce different completions [even at temperature 0](https://www.thehoth.com/blog/ai-visibility-score-accuracy/). Five runs expose more of that variation than one screenshot, but they do not erase it. A shift from `1/5` to `4/5` deserves investigation, not a victory lap. If your controls also start naming your brand, check the prompts, classifications and run conditions before changing content. If a buyer prompt moves while controls stay flat for two weeks, you have a stronger reason to investigate the pages behind it. For a larger test, use [_Run the Prompt 20 Times_](https://groas.com/post/run-every-prompt-20-times-a-test-protoco).

**Keep the controls boring.** Their job is not to produce an interesting chart. Their job is to catch a test that has stopped measuring what you think it measures.

## Verify the tracker, then change the work behind the rate

After the first complete batch, filter `Runs` to one prompt and one engine. Confirm that `run_n` reads `1` through `5`, every row has answer text, and the `mentions` count in `Weekly summary` agrees with the five Boolean values. Check that the controls appear in the summary and that `Executions` shows no unresolved errors. That is how you verify the build worked.

Then leave the prompt list alone long enough to see a baseline. **The first thing to change is the work behind a persistent zero, not the wording of the test.** Find the buyer prompts where you never appear, inspect the competitor mentions, and decide which pages or technical fixes deserve attention.

Know what the sheet cannot verify. It measures these **APIs, not the consumer apps**. The consumer layer can add retrieval, location, login, memory and model changes, so a buyer can see something different from your scheduled run. Google AI Overviews is outside this sheet too; use a separate manual SERP check or SERP API proxy for that surface. Holding this setup steady makes its trend readable. It does not make it a map of every AI answer.

If the same prompts stay at zero and nobody has time to fix the underlying content, that is where I would stop building and hand the execution to [groas Earned Search](https://groas.com/for-businesses). Keep the tracker running either way. The next rate tells you whether the work changed the answer.

## Frequently Asked Questions

### Why isn't screenshotting one ChatGPT answer a reliable way to track AI visibility?

One screenshot is a single sample of a variable process, not a visibility report. Ask the same prompt again and the brand list, order or citations can change. The article recommends tracking a mention rate from repeated runs, such as 20 buyer prompts sent to three engines five times each week, instead of treating one yes or no as a verdict.

### How should I choose the prompts for an AI visibility tracker?

Export the last 90 days from Search Console and your Google Ads search terms report and look for question and comparison shapes like best, vs, how much, near me and who does. Rewrite 20 of them as full questions with a role, a constraint and a decision, then group them into awareness, evaluation, instructional and transactional prompts. Freeze the wording once the tracker starts.

### What are control prompts in an AI visibility tracker and why do I need them?

Controls are two or three questions where your brand should never appear, such as asking for the best CRM for a 200-person law firm when you sell garage doors. They are run exactly like the buyer prompts. If your controls start naming your brand, the test has stopped measuring what you think it measures, so check the prompts, classifications and run conditions before changing content.

### Where should I store the API keys for the visibility tracker?

Keep the OpenAI, Gemini and Perplexity keys in Apps Script Project Settings under Script Properties, not in a spreadsheet cell or the script. Also store your brand name variants, competitor names and domain there, and record the model IDs in a note on the Prompts tab so a later model change does not masquerade as a visibility change.

### Why does the Apps Script run 15 API calls at a time instead of all 330?

Apps Script has a per-execution limit, and 330 calls plus pauses and network time do not fit in one execution. The script runs 15 calls per execution, saves its position after each answer, and a five-minute trigger resumes the batch from the saved offset until all 330 answer rows are complete.

### Is a brand mention the same thing as a domain citation in the tracker?

No. The brand_mentioned field checks whether the answer text contains one of your brand name variants, while domain_cited checks whether URLs in the answer or citation URLs returned by the API point to your domain. An engine can name your brand without linking to your site, so the tracker records them as separate events.

### Should a weekly mention rate change make me change my content right away?

Not immediately. Models can produce different completions even at temperature 0, so some weekly rates move without a meaningful change for buyers. Keep the prompts, models, run count and schedule fixed, and treat a shift like 1/5 to 4/5 as something to investigate. A buyer prompt moving while controls stay flat for two weeks is a stronger reason to look at the pages behind it.

### Does this tracker measure what buyers see in ChatGPT or Perplexity apps?

No, the sheet measures the APIs, not the consumer apps. The consumer layer can add retrieval, location, login, memory and model changes, so a buyer can see something different from your scheduled run. Google AI Overviews is also outside the sheet and needs a separate manual SERP check or SERP API proxy.

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## Structured data

```json
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Ask the same prompt again and the brand list, order or citations can change. The article recommends tracking a mention rate from repeated runs, such as 20 buyer prompts sent to three engines five times each week, instead of treating one yes or no as a verdict."}},{"@type":"Question","name":"How should I choose the prompts for an AI visibility tracker?","acceptedAnswer":{"@type":"Answer","text":"Export the last 90 days from Search Console and your Google Ads search terms report and look for question and comparison shapes like best, vs, how much, near me and who does. Rewrite 20 of them as full questions with a role, a constraint and a decision, then group them into awareness, evaluation, instructional and transactional prompts. Freeze the wording once the tracker starts."}},{"@type":"Question","name":"What are control prompts in an AI visibility tracker and why do I need them?","acceptedAnswer":{"@type":"Answer","text":"Controls are two or three questions where your brand should never appear, such as asking for the best CRM for a 200-person law firm when you sell garage doors. They are run exactly like the buyer prompts. If your controls start naming your brand, the test has stopped measuring what you think it measures, so check the prompts, classifications and run conditions before changing content."}},{"@type":"Question","name":"Where should I store the API keys for the visibility tracker?","acceptedAnswer":{"@type":"Answer","text":"Keep the OpenAI, Gemini and Perplexity keys in Apps Script Project Settings under Script Properties, not in a spreadsheet cell or the script. Also store your brand name variants, competitor names and domain there, and record the model IDs in a note on the Prompts tab so a later model change does not masquerade as a visibility change."}},{"@type":"Question","name":"Why does the Apps Script run 15 API calls at a time instead of all 330?","acceptedAnswer":{"@type":"Answer","text":"Apps Script has a per-execution limit, and 330 calls plus pauses and network time do not fit in one execution. The script runs 15 calls per execution, saves its position after each answer, and a five-minute trigger resumes the batch from the saved offset until all 330 answer rows are complete."}},{"@type":"Question","name":"Is a brand mention the same thing as a domain citation in the tracker?","acceptedAnswer":{"@type":"Answer","text":"No. The brand_mentioned field checks whether the answer text contains one of your brand name variants, while domain_cited checks whether URLs in the answer or citation URLs returned by the API point to your domain. An engine can name your brand without linking to your site, so the tracker records them as separate events."}},{"@type":"Question","name":"Should a weekly mention rate change make me change my content right away?","acceptedAnswer":{"@type":"Answer","text":"Not immediately. Models can produce different completions even at temperature 0, so some weekly rates move without a meaningful change for buyers. Keep the prompts, models, run count and schedule fixed, and treat a shift like 1/5 to 4/5 as something to investigate. A buyer prompt moving while controls stay flat for two weeks is a stronger reason to look at the pages behind it."}},{"@type":"Question","name":"Does this tracker measure what buyers see in ChatGPT or Perplexity apps?","acceptedAnswer":{"@type":"Answer","text":"No, the sheet measures the APIs, not the consumer apps. The consumer layer can add retrieval, location, login, memory and model changes, so a buyer can see something different from your scheduled run. Google AI Overviews is also outside the sheet and needs a separate manual SERP check or SERP API proxy."}}]}]}
```