01Gemini
Gemini visibility: where inconsistent facts about you show up first
Quick answer
Gemini blends retrieved sources with model knowledge. The practical consequence is that entity consistencywhether every source agrees on who you are — carries more weight here than raw page-level ranking.
It is also the hardest surface to evidence, because a meaningful share of its answers carry no citation at all. We report what we can observe and do not model the rest into a number that looks more certain than it is.
Key takeaways
- Mixed grounding is the defining feature. A confident statement about your brand may have no citation attached, which means nothing to trace and nothing to correct directly.
- Model knowledge lags. A fact absorbed during training can persist after your site is fixed, so corrections land here slower than on Perplexity.
- Entity work is the highest-yield fix on this surface, because what gets recalled is an entity rather than a URL.
- It is not the same as AI Overviews. Different surface, sampled separately.
- Surface fragmentation matters. Gemini appears across several products with different behaviour; we sample the one your buyers actually use.
- Nobody can edit what a model says. We correct sources and re-sample. That is the honest limit.
What makes it different to measure
- Mixed grounding. Answers blend retrieved sources with model knowledge, so a confident statement about your brand may have no citation attached to it at all.
- Model knowledge lags. Facts absorbed during training can persist after your site has been corrected. Entity fixes take longer to show here than on retrieval-first platforms.
- Surface fragmentation. Gemini appears in several products with different behaviour. We sample the surface your buyers actually use rather than assuming one reading covers all of them.
- Attribution is partial. Which makes presence harder to evidence than on Perplexity, where citations are listed by design.
Why entity work matters disproportionately here
When a model answers partly from memory rather than from a retrieved page, the thing being recalled is an entity, not a URL. If your founding year, product tiers, market coverage or leadership are stated differently across your site, your press coverage and old directory profiles, the recalled version is unpredictable.
Fixing that is entity optimisation, and Gemini is usually the platform where the improvement is most visible once corrections propagate — just not the fastest.
What we record
| Measure | How we capture it | Why it matters here |
|---|---|---|
| Cited or uncited | Whether the answer attributes anything at all | A large share do not, and that is itself a finding |
| Brand presence | Named, cited, or absent, coded separately | Named-without-citation is common and invisible to analytics |
| Factual accuracy | Coded against the canonical fact set built during the audit | This is the measure that moves most after entity work |
| Characterisation | How you are described when no source is attached | The clearest read on what the model believes about you |
| Competitor set in answer | Which rivals appear alongside you | Shows whether you are in the consideration set at all |
How long corrections take
Slower than elsewhere, and worth saying at the start of an engagement rather than in month three. Where Perplexity often reflects a corrected fact within weeks, Gemini can take considerably longer because the incorrect version may have been absorbed during training rather than retrieved.
We therefore sequence entity work early, and we set expectations that this surface is the last to confirm it worked.
What this means for you
- Build the canonical fact set first. Without it there is nothing to code accuracy against, and accuracy is the measure that actually moves here.
- Expect the slowest confirmation. If Gemini has not moved in month two, that is normal rather than evidence the work failed.
- Do not accept an agency claim to fix what a model says. Sources can be corrected. Model output cannot be edited by anyone.
A limitation worth stating
Because a meaningful share of Gemini answers carry no citation, presence here is harder to evidence than on any other surface we measure. We report what we can observe and label it Observed. We do not model the gap into a number that looks more certain than it is.
Any vendor giving you a confident single figure for Gemini visibility should be asked how they handled the uncited answers.
02Common questions
Questions people ask about Gemini
Why is Gemini harder to measure than other platforms?
Because a meaningful share of its answers carry no citation. It blends retrieved sources with model knowledge, so a confident statement about your brand may have nothing attached to it that you can trace or correct.
Why does entity work matter more on Gemini?
When a model answers partly from memory rather than a retrieved page, the thing being recalled is an entity, not a URL. If your facts differ across your site, your press coverage and old directory profiles, the recalled version is unpredictable.
How long do corrections take to appear here?
Longer than on retrieval-led platforms. A fact absorbed during training can persist after your site is corrected, where Perplexity often reflects the same fix within weeks. We set that expectation before starting rather than explaining it in month three.
Is Gemini the same as AI Overviews?
No. They are different surfaces, sampled separately. AI Overviews are grounded in Google's index and ranking systems; Gemini blends that grounding with model knowledge and appears across several products with different behaviour.
What do you record for Gemini?
Whether the answer cites anything and whether it cites you; how your brand is characterised when no source is attached; factual accuracy against the canonical fact set built during the audit; and which competitors appear in the same answer.
Can you fix a wrong fact that Gemini states about us?
We can correct every source we can reach and re-sample to see whether it propagated. We cannot edit what a model says, and no agency can. Anyone claiming otherwise is describing something they do not control.
Sources
Every figure on this page comes from one of the following. Where a claim is ours rather than a source’s, it carries an evidence label instead.
- Google I/O announcements, 19 May 2026. Context for the scale of Google’s AI surfaces. Primary
- Google Search Central — Optimizing your website for generative AI features (15 May 2026, updated 10 July 2026). Primary
- Rankability — State of AI Search, 2026. Source for cross-platform overlap and structural citation signals. Independent study
- Searlixa sampling across client engagements, labelled Observed with sample and date. Our own
Ask an assistant
Hand this page to an assistant and ask it to summarise or challenge the argument. If it gets something wrong about us, that is itself a useful finding.