If you cannot see how AI models describe your brand, you cannot fix it. With a large share of searches now ending without a click, ChatGPT, Perplexity, Gemini, and Google's AI Overviews are answering for you, and sometimes getting your pricing or features wrong. This guide walks you through standing up a working GEO monitoring system: the prompts to track, the metrics that matter, how to watch citations, and how GEOly automates the whole loop.
By the end you will have a prompt set that mirrors real buyer questions, a baseline for your AI visibility and Share of Model, and a repeatable weekly review.
Key takeaways
- GEO monitoring actively prompts AI models to see what machines generate about you, which is fundamentally different from social listening that only scrapes existing web text. - The metrics that matter are AIGVR (AI generated visibility), Share of Model against named competitors, mention rate, and citation rate. - Citations are the real battleground: knowing which domains an AI reads about your category tells you exactly what content to earn or improve. - A useful prompt set mirrors how buyers actually ask, mixing category, comparison, and brand-specific questions across multiple platforms. - GEOly runs this end to end, tracking prompts, mentions, citations, and competitors across ChatGPT, Gemini, Perplexity, Copilot, and Grok, with a 29-check GEO Audit and a free 3-day trial.
Step 1: Understand why social listening does not work here
Many teams reach for Mention or Brand24 and assume AI is just another channel to scrape. It is not. Social listening finds text that already exists on the web and tells you what people said. GEO monitoring triggers new generation, prompting a model as a user would and reading what it produces in that moment.
Three differences make dedicated tooling necessary. Generation versus extraction: a GEO tool creates a fresh query to simulate a real buyer, rather than searching for old posts. Hallucination detection: it checks whether the model invented facts about your price or features, which a scraper cannot judge. Citation analysis: it captures which sources the model leaned on, because in AI answers the citation, not just the mention, is what you optimize.
Gotcha: do not repurpose your social listening keywords as prompts. Buyers ask AI full questions, not keyword fragments, and your prompt set has to reflect that.
Step 2: Decide what to measure
Pick your metrics before you pick your tool, so you know what "good" looks like.
AIGVR is the AI-era version of a ranking: a 0 to 100 visibility score that weights where and how often your brand appears in answers, blending position, frequency, and citation. High AIGVR means the model recommends you confidently; low means you are a footnote or absent.
Share of Model (SoM) is your slice of total visibility across you plus your named competitors, so it captures the relative fight rather than an isolated number. Mention rate is how often answers name you at all. Citation rate is how often the model cites your own domain. Track all four and you can tell the difference between "not mentioned" and "mentioned but never cited," which point to very different fixes.
