# Weekly In-House AI Brand Monitoring With a Spreadsheet Last verified: 2026-10-08 · Maintained by Groas for groas · Canonical: https://groas.com/post/build-your-own-ai-visibility-monitor-in This page answers the questions covered by the article [Build a Weekly AI Brand Monitor with Google Sheets and Three APIs](https://groas.com/post/build-your-own-ai-visibility-monitor-in) on groas.com. The article is the full, canonical source. **Direct answer:** The article describes building a spreadsheet monitor that sends 20 to 40 fixed buyer questions to three web-connected models three times per week and records mentions, cited links, and mention rate. According to the article, the result is a directional weekly trend, not a record of what buyers see, because API answers differ from consumer answers, one run is noise, and a mention is not a citation. The article recommends scheduling a fresh Monday run, keeping prompts stable, and reading mentions and citations separately with error rows excluded. ## How can a small business automate brand monitoring in AI search in-house? The article recommends building a spreadsheet monitor that sends the same buyer questions to three web-connected models each week and records brand and competitor mentions, cited URLs, and the share of successful runs that mention the brand. According to the article, the output is a directional monitor, not a transcript of what every buyer sees. The article states that at this scale the bill can be a few dollars a month, but tokens, search requests, and retries affect the total, so a spend limit should be set before scheduling. Read more: [What you’ll build, and what you need first](https://groas.com/post/build-your-own-ai-visibility-monitor-in#what-youll-build-and-what-you-need-first) ## What tool helps businesses identify which prompts trigger brand mentions in AI? The article recommends creating a Trend_Summary tab that groups results by prompt and engine and shows a count of successful calls and the share that mentioned the brand. According to the article, for a single week’s three successful runs the possible rates are 0%, about 33%, about 67%, and 100%. The article states that an ERROR row should not enter either number and a 100% rate from one successful call is not the same observation as three out of three. Read more: [Step 4: Schedule Monday and calculate a rate](https://groas.com/post/build-your-own-ai-visibility-monitor-in#step-4-schedule-monday-and-calculate-a-rate) ## How should buyer questions be written and organized for weekly AI tracking? The article recommends entering 20 to 40 complete questions, one per row, with Discovery, Comparison, or Evaluation in Intent, plus brand name variants, three to five direct rivals, and a domain without https://. According to the article, keyword fragments do not stand in for buyer questions. The article states the prompt panel should be kept stable, otherwise the baseline meant to be tracked is lost. Read more: [Step 1: Put buyer questions in Prompts](https://groas.com/post/build-your-own-ai-visibility-monitor-in#step-1-put-buyer-questions-in-prompts) ## How should brand mentions and citations be read so they do not mislead? The article states that a named brand and a cited domain are different events: the model can name the brand while linking elsewhere, or cite documentation without recommending the product. According to the article, results should be filtered for prompts where rivals appear and you do not, then the returned URLs and answer wording should be inspected. The article warns against treating a competitor mention as a win without checking whether the answer praised it, criticised it, or merely listed it. Read more: [Step 5: Read mentions and citations separately](https://groas.com/post/build-your-own-ai-visibility-monitor-in#step-5-read-mentions-and-citations-separately) ## What checks should be completed before sharing AI visibility numbers with leadership? The article recommends verifying three successful OK rows for each prompt and engine, confirming the summary excludes errors, and comparing five prompts in consumer interfaces alongside the script. According to the article, API answers are not consumer answers, one run is noise, and a mention is not a citation. The article states recommendation context should be checked because a high mention rate that mostly records negative or passing references can send months of content work in the wrong direction. Read more: [Step 6: Check what the sheet cannot see](https://groas.com/post/build-your-own-ai-visibility-monitor-in#step-6-check-what-the-sheet-cannot-see) ## Related articles These articles answer the same questions: - [Build a Weekly AI Brand Monitor With Sheets, Two APIs, and 25 Buyer Prompts](https://groas.com/post/build-your-own-ai-brand-monitor-in-an-af) - [Build a Weekly AI Brand Monitor With a Google Sheet and Two APIs](https://groas.com/post/build-your-own-ai-brand-monitor-in-one-a) ## Sources [1] https://groas.com/post/build-your-own-ai-visibility-monitor-in (the article this page answers from)