AI Visibility (Brand Visibility in AI Answers)

AI answers are invisible to the operator: there's no ranking you can look up and no analytics on recommendations an AI system makes. Without active measurement, a business simply doesn't know whether ChatGPT recommends it when someone asks "Who's a good provider for X?" — ignores it, or spreads false information about it.

Measurement works through systematic, repeated queries: a defined question catalogue (purchase, comparison, and information questions from your target audience) is regularly submitted to the relevant AI systems and analysed — which brands are mentioned, in what order, with which sources and what tone. This creates a share of voice in AI answers, comparable over time and against competitors.

Measurement reveals the gaps (which questions is your brand missing, where are competitors cited, which sources do the AI systems use) — GEO actions close them: create content for exactly these questions, populate the third-party sources cited (directories, specialist articles), keep facts consistent. The next measurement round then shows what worked. GEO Tracking AI from HVNH AI automates this exact cycle.

AI visibility can be tracked systematically without expensive tools. The core is a fixed question catalogue: 15 to 30 formulations that potential customers would actually ask — from general ("What does an AI phone assistant do?") to purchase-ready ("Backoffice automation providers for mid-market"). You submit these questions on a regular schedule, say monthly, to the relevant systems: ChatGPT, Perplexity, Google with AI Overviews, and depending on your audience, also Copilot or Gemini. For each question, document: is your company mentioned? Which competitors appear? Which sources are cited?

Over time, this spreadsheet reveals a clear picture: the cited sources show which content and directories feed the answers — that's exactly where optimisation starts. It's important to factor in fluctuation: AI answers aren't deterministic, the same question can be answered differently. So the trend across multiple measurements counts, not the single result. Additionally, it's worth checking server logs and analytics: access from AI crawlers and referral traffic from ChatGPT or Perplexity proves your content is being read and used as a source.

The most common error is drawing conclusions from single queries. AI systems aren't deterministic — the same question can name different brands and sources in successive queries. Proper monitoring requires repetition: ask the same question multiple times and evaluate frequencies rather than overweighting single answers. Second mistake: a catalogue that's too narrow. If you only check direct brand queries, you're not measuring how well you perform on generic purchase-decision questions — the ones potential customers ask without prior knowledge.

Third mistake: no competitor comparison. AI visibility is a relative metric — appearing in eight of twenty relevant questions only makes sense if you know your main competitor covers fifteen. Share of voice in direct comparison gives more insight than any absolute value and shows where your optimisation needs to focus.