"Are we visible in AI?" is the question every Australian business is starting to ask, and it contains a hidden mistake: there is no single "AI" to be visible in. ChatGPT, Google's Gemini and Perplexity are separate products with different owners, different source material and different habits when it comes to naming businesses.
Being everywhere in one and invisible in another is the norm, not the exception.
This article walks through how the three major engines differ in practice, why those differences matter for where you invest, and why any provider selling you one blended "AI visibility score" is averaging away the information you actually need.
Three engines, three editorial personalities
It helps to think of each engine as a publication with its own editorial habits. All three combine a language model with retrieval from the live web, but what they retrieve, how fresh it is, and how visibly they credit sources all differ.
Those differences are structural: they come from who owns the engine and what it was built to do. That makes them worth understanding even though the products themselves change often.
ChatGPT: the default question box
ChatGPT is where conversational search behaviour took hold, and for many Australians it's simply where questions now go, including buying questions. It answers from its trained knowledge and can search the live web when a question needs current information, weaving results into a conversational reply.
Citation behaviour varies with mode and question type: sometimes sources are linked, sometimes a business is named with no link at all. That last case matters commercially: a recommendation with no click leaves no trace in your analytics, which is why ChatGPT visibility has to be measured by asking, not by waiting for referral traffic.
Gemini and AI Overviews: search with an author
Gemini's defining feature is its plumbing: it sits on top of Google's index, the freshest and deepest map of the web there is, and it's connected to the rest of the Google ecosystem: Maps, Business Profiles, reviews. That makes classic Google signals unusually important to how Gemini answers about local Australian businesses.
AI Overviews compound the effect by inserting generated answers above the ordinary results on everyday Google searches, which means Google-side visibility now decides two surfaces at once. If your Google fundamentals are weak, this is the family of engines where it shows first.
Perplexity: citations first
Perplexity built its identity on showing its work: answers arrive with sources attached, prominently and by default. Its audience skews toward deliberate researchers (people comparing options and checking claims), which makes it smaller than ChatGPT but disproportionately high-intent.
The practical consequence for a business is that Perplexity visibility depends heavily on being the kind of source worth citing: pages with explicit facts, and third-party coverage that corroborates them. It's also the easiest engine to audit yourself, because it always shows you exactly who it trusted.
67%
of buyers now use AI as their primary research method before contacting an agent, per the 2026 State of AI SEO in Real Estate industry benchmarks
Why a single "AI visibility score" misleads
Imagine you're cited constantly in Perplexity, occasionally in Gemini, and never in ChatGPT. A blended score might read "60% AI visibility": a number that sounds informative and tells you almost nothing.
It hides that you're absent from the engine with the largest audience, and it can't tell you why: the fix for a ChatGPT gap (often authority and entity clarity) is different from the fix for a Gemini gap (often classic Google signals). Averages also flatter volatility: AI answers shift with phrasing and over time, so a single-day, single-engine snapshot dressed up as a score is closer to a horoscope than a metric.
What engine-specific monitoring looks like
The workable alternative is unglamorous: a fixed set of real buying questions for your category, asked on each engine separately at a regular cadence, with results recorded (named or not, cited or not, and who was named instead). Over weeks that log becomes a per-engine trend line you can actually manage against, and it tells you where your next dollar goes.
If you want the fuller background on how these engines assemble answers in the first place, start with our plain-English guide to AI search. Or ask us to run the baseline for you with a free AI visibility check.
Usually the Google family first: Gemini and AI Overviews reward the local fundamentals you should have anyway, and AI Overviews reach people who never open a chatbot. But let your own baseline decide: measure all three before spending on any one.
No. The same qualities (explicit facts, clear structure, consistent identity, third-party proof) serve all three. What differs by engine is emphasis and measurement, not the content itself. Three separate content strategies is a sign someone is overselling.
The same principles carry over: assistants that answer commercial questions retrieve from the same web and reward the same clarity. Track the big three closely and spot-check others occasionally; chasing every assistant individually isn't worth a small business's time.
Generative engines are probabilistic: phrasing, timing, mode and even chance shift the output. That's exactly why one-off screenshots prove nothing, and why visibility only becomes measurable as a trend across repeated structured checks.