How Gemini decides which brands to recommend

Ask Gemini "what's the best CRM for a five-person sales team" and you might get an answer built entirely from what the model already knows — or you might get an answer Gemini assembled seconds ago from a live Google search it ran on your behalf, with source links attached. Which one happens isn't random, and it isn't the same mechanic as ChatGPT's plain recall or Perplexity's always-on citation trail. Gemini decides, per question, whether to ground its answer in a live Google Search — and that decision, more than almost anything else about the model, determines what actually gets you recommended. This post is that mechanic, explained plainly, and what to do about it.

Grounding is a decision, not a default

We've already written about how ChatGPT decides what to recommend: it's prediction from learned associations, sometimes topped up with live retrieval when browsing is on. Gemini works on the same basic principle — a model predicting plausible next words, not consulting a stored leaderboard — but Google has documented a specific extra step in the middle: dynamic retrieval. When Gemini is grounded with Google Search (the feature Google exposes both in the Gemini apps and to developers via the API), the model first estimates how likely a search would actually improve its answer. Below a threshold, it just answers from what it already knows, the way a browsing-off ChatGPT would. Above that threshold, it silently issues one or more real Google searches, reads the results, and folds them into the answer — typically surfacing the source pages it used as clickable citations underneath.

That single design choice is the whole story. A vanity question ("what is [your brand]") is exactly the kind of prompt the model is confident it can answer from training alone, so it often doesn't search — meaning nothing you publish this week will move that answer, because Gemini never went and looked. A sharper buyer question — "best CRM for a five-person team that hates data entry" — is exactly the kind of prompt more likely to trip the search threshold, because the model has more to gain from checking what's actually being said right now. That's the version of the question worth testing, and the version most people never think to type at themselves.

Why that makes Gemini different from ChatGPT and Perplexity

Once grounding fires, Gemini isn't reading some separate "AI index" — it's reading the same Google Search results a human typing the query would see, built from Google's own crawled and ranked web index. That's a meaningfully different mechanic from the other two engines we write about most:

Versus a plain ChatGPT answer: an ungrounded response is pure prediction from a frozen training snapshot — no live evidence at all, just whatever associations the model absorbed by its cutoff. Gemini's ungrounded answers work the same way. The difference only shows up once grounding kicks in, at which point Gemini isn't running its own independent web-retrieval system — it's tapping the search engine that already ranks the entire web for a living. Practically: classic SEO signals — indexation, crawlability, how well a page already ranks organically — carry more direct weight for a grounded Gemini answer than for any other AI engine we track, because Gemini's live evidence is Google's ranking.

Versus Perplexity: Perplexity retrieves and cites on essentially every question — showing its sources is the product. Gemini's grounding is conditional and, from what Google has published about the feature, tuned to fire only when the model judges a search is worth the cost. That means a citation from Gemini is rarer and more question-dependent than one from Perplexity — but when it happens, it's drawing on the deepest, most mature web index that exists, not a bespoke retrieval pipeline.

One more wrinkle worth knowing: the standalone Gemini app and API are one surface. Google's AI Overviews — the AI-written summaries that now sit above the regular blue links on many Google searches — are a related but distinct surface, built on the same model family but living inside the classic search results page and drawing on the search index Googlebot already crawls. If you've heard that Google-Extended (a separate crawler Google introduced to let sites opt in or out of having their content used for training Gemini and related AI features) controls what shows up in AI Overviews, be careful: Google has described AI Overviews as pulling from the standard, Googlebot-crawled search index, not from Google-Extended's training crawl specifically. The practical takeaway is simpler than the crawler taxonomy: if Googlebot can't reach and rank your page, neither the Gemini app's grounded answers nor AI Overviews will find much reason to mention you.

The levers that actually move it

Given that mechanic, four things measurably help:

• Your organic footing in Google Search itself. Because grounded Gemini answers ride on Google's own index, being crawlable, indexed, and reasonably well-ranked for the buyer question you care about is the single most Gemini-specific lever on this list — it doesn't move ChatGPT or Claude the same direct way.
• Third-party evidence shaped like the question. Once Gemini searches, it's reading the same review sites, comparison pages, and "best of" roundups any Google searcher would land on. The same evidence gap that decides ChatGPT's answers decides Gemini's grounded ones too — your own homepage is the weakest source in the results, third-party mentions are the strongest.
• Freshness. Dynamic retrieval exists specifically to catch what's changed since training. A comparison page you updated last month has a real shot at appearing in this week's grounded answer; a stale, years-old mention doesn't get the same benefit.
• Confirm the crawlers can actually reach you first. None of the above works if Googlebot or Google-Extended is blocked before it ever reads your page — check that with the free AI crawlability checker before you invest in content.

Why one manual check won't tell you which mode you're in

You can't tell, just by reading a Gemini answer, whether it was grounded or recalled from memory — and that's exactly the problem with checking by hand. Ask it once and you might catch an ungrounded answer that no amount of fresh content would have changed; ask a sharper, more buyer-shaped version of the same question and you might trip grounding and see a completely different, evidence-based result. Your AI visibility score exists precisely because a single sample from a single engine tells you almost nothing reliable on its own.

PingMyBrand runs 25 real buyer questions against Gemini specifically — alongside ChatGPT, Claude, and Perplexity — and shows you, per question, whether you were named, where you ranked, and whether Gemini cited your domain. You can also track Gemini on its own on the Gemini visibility tracker.

Run the free scan and go straight to the Gemini column of your report: no signup, about a minute, and you'll see exactly which of your buyer questions Gemini is answering from memory alone — and which ones it's actually going out and checking Google for.