Google Gemini visibility for SaaS
Source: First Page Sage, Top Generative AI Chatbots by Market Share. July 2026, US monthly-active-user estimates.
Gemini is not AI Overviews, and the difference is the whole point
AI Overviews and AI Mode are surfaces inside Google Search. A user is searching, and Google puts a generated answer above or instead of the results. The Gemini app is a separate product with its own interface, its own conversation model and its own behaviour, reached deliberately rather than as a by-product of searching.
This distinction is where most SaaS AI search programmes quietly fail. A site with pages for AI Overviews and AI Mode looks like it has Google covered. It does not: it has covered the search surfaces and left the assistant, which on its own accounts for more than a quarter of assistant usage, entirely unaddressed.
Gemini grounds many of its answers in Google Search, so strong conventional search performance is a real input rather than a legacy concern. But grounding is selective, the retrieved set is small, and what gets pulled in is passage-level rather than page-level. Ranking well and being grounded are correlated, not the same thing.
Which crawler does what, and what blocking it costs you
Training crawlers and retrieval crawlers are different things. Blocking a retrieval crawler removes you from the answers. Blocking a training crawler does not. Getting the two confused is the most common self-inflicted AI visibility problem we find.
| User agent | Purpose | What blocking it costs you |
|---|---|---|
| Google-Extended | Retrieval | The control that governs whether your content can be used to ground Gemini answers. Blocking it removes you from Gemini and from Google's AI surfaces. It is not a training-only switch, which is how it is commonly described and commonly misconfigured. |
| Googlebot | Search index | The crawler behind Google Search, which is the index Gemini grounds against. Blocking it removes you from both conventional results and the AI surfaces above them. |
Our own policy is published at surge45.com/robots.txt, with a line per agent and the reasoning in the file. If we are going to advise on crawler policy, our own should be readable.
How Gemini surfaces sources
Gemini shows sources less prominently than Perplexity and more variably than ChatGPT. A user often sees a confident answer with sources one interaction away, which changes what visibility is worth: being the substance of the answer matters more than being one of the links under it.
That reframes the objective. On Perplexity the goal is to appear in a visible source list. On Gemini the goal is to be the thing the answer is made of, cited or not, because the buyer forms their shortlist from the prose.
It also makes measurement harder and more important. Counting link citations understates Gemini visibility badly. The number worth tracking is whether the brand is named in the answer text, and whether what is said about it is accurate.
Observed to change Google Gemini visibility
Conventional Google search performance
Gemini grounds against Google Search, so the ordinary discipline of ranking for the right queries feeds it directly. This is the one platform where an existing SEO programme is a genuine head start.
Getting Google-Extended right
It is the single highest-leverage line in a robots.txt file for Google's AI surfaces, and the one most often set wrong by teams who read it as a training opt-out.
Entity clarity across Google's own properties
Knowledge Panel accuracy, consistent business information, and a clear description of what the product is. Google resolves entities before it grounds answers.
Passage-level answers on ranked pages
Grounding pulls passages, not pages. A page that ranks but buries the answer in the twelfth paragraph loses to a lower-ranked page that answers in the first.
Sold for Google Gemini, and not worth buying
Treating AI Overviews work as Gemini work
They are different products with different triggers and different user intent. Overlap in the underlying index does not make them the same surface, and reporting them together hides which one is failing.
Blocking Google-Extended as a data-protection measure
It removes you from the second-largest assistant and from AI Overviews. If the concern is training data, that is a different conversation with different controls.
What we do on Google Gemini
- 1Test Gemini separately from AI Overviews and AI Mode, on the same prompt set, and report them separately.
- 2Audit Google-Extended and Googlebot directives, and confirm what is currently blocked rather than what someone believes is blocked.
- 3Measure brand mention in answer text, not just link citations, because link counting badly understates this surface.
- 4Fix entity consistency across Google's own properties before touching content.
- 5Restructure the pages that already rank so the answer sits in the passage a grounding pass would pull.
Google Gemini questions we get asked
The rest of the assistant layer
ChatGPT51.3%
The largest assistant by a distance, and the one whose citation behaviour changes most between a model answering from memory and a model running a search.
Read the mechanismClaude10.3%
Third by usage, but over-indexed among engineering and technical buyers, which makes its share understate its commercial weight for developer-facing SaaS.
Read the mechanismGrok2.8%
Fourth by usage, and the only assistant whose answers respond to what people are saying about you this week rather than this year.
Read the mechanismPerplexity2%
Small by usage and disproportionately useful, because it is the one assistant that shows its working: sources are listed on screen, so citation share is directly observable rather than inferred.
Read the mechanismMicrosoft Copilot1.3%
Small in consumer usage, structurally important in enterprise, and the one platform where the work is mostly not AI work at all.
Read the mechanism