Gemini SEO: How Google Search Grounding Decides Which Software Gets Named

When a property manager asks Gemini which maintenance coordination software to buy, the model does not answer from memory alone. With Google Search grounding on, it writes its own search queries, reads what Google returns, and attaches the pages it used as grounding chunks, per Google’s Gemini API documentation. That grounding step decides which vendors can be named.

This guide covers how grounding works, which pages it can reach, and what a property-ops vendor can change. It also says plainly what the AI Shortlist Index does and does not yet measure on Gemini. The commercial side, for one vendor’s row, is on getting recommended by Gemini.

What is Gemini SEO?

Gemini SEO is the work of being on the pages Gemini reads when it grounds an answer, and of giving those pages facts the model can quote. Because grounding runs on Google Search, most of it is ordinary search work aimed at a different query shape. The query is the model’s rewrite of a buyer’s question, and the reader is a model deciding what to repeat.

How does Google Search grounding work?

Google’s Gemini API documentation describes “Grounding with Google Search” as a tool the model can call during a request. The model decides whether a query needs fresh information, issues one or more Google searches, and uses the results while writing its answer.

The developer turns it on by adding the google_search tool to the request. Nothing about the prompt changes.

The response then carries a groundingMetadata block. The documentation lists webSearchQueries, the searches the model issued, and groundingChunks, one entry per source with a URI and title. It also lists groundingSupports, which tie spans of the answer back to chunks, and searchEntryPoint, Search Suggestions that Google’s terms ask you to display.

Two details matter more than the rest. First, webSearchQueries is the model’s rewrite of the question. “What’s the best maintenance coordination software for a property management company with around 300 doors?” does not go to Google as typed.

The model turns it into something that looks like an ordinary search. The pages that rank for that rewrite are the only pages the model can ground on.

Second, the chunk URI is not the page’s real address. The Gemini API returns grounding chunk URIs as redirect links on vertexaisearch.cloud.google.com under a grounding-api-redirect path; following the redirect lands on the actual page.

A measurement that stops at the raw URI counts Google’s redirect host as the source of every citation. The Index resolves each one before it classifies anything.

Which pages can Gemini ground on?

Only pages Google returns for the rewritten query, and only when the model decides to search at all. Google’s documentation says the model may answer without searching when it judges the question does not need it, in which case groundingChunks comes back empty. For a question about which software to buy for 300 doors, a search is likely but not guaranteed.

If it searches, it writes queries. Our hypothesis, untested until Gemini runs exist: a 300-door maintenance prompt becomes searches like “best maintenance coordination software for property management companies” and “maintenance coordination software 300 units”. The candidate pages would then be whatever Google ranks for those strings.

The Index stores webSearchQueries so it can publish the real rewrites rather than guess.

A vendor gets named when two things are both true. A page in the returned set says something about the vendor, and the model chooses to repeat it. A vendor absent from every returned page will not be named.

What the candidate set looked like on ChatGPT

The closest evidence is the one ChatGPT run whose full consulted set was visible.

In an API capture of prompt mvn-best-01 on 3 September 2026, ChatGPT consulted 124 URLs: 38 on reddit.com, 16 on appfolio.com, 12 on buildium.com and 4 on propertymeld.com. The answer named Property Meld first and Latchel second. That run is retired from the edition and archived.

We expect a Gemini run on the same prompt to lean on a similar mix of forum threads, PMS vendor pages and listicles. Google’s organic results for “best X software” queries are dominated by those page types. That expectation is unverified and will be checked against the first edition with Gemini data.

What should a property-ops vendor do?

Grounding rides on Google Search. That collapses the mystery into four things to do, most of them familiar to anyone who has run search for a software company.

Rank for the rewrite

The searches the model writes look like ordinary queries: category plus buyer context. Open Search Console and look for queries you already appear for that pair “maintenance coordination”, “vendor network”, “resident benefits” or “access control” with a PMS name or a portfolio size. Those are the query shapes that feed grounding.

A page that ranks fifth for “maintenance coordination software AppFolio integration” is a page the model can read. A page that ranks fortieth is not.

Be on the pages that already rank

Because grounding chunks are drawn from Google’s results for the rewritten query, per the Gemini API documentation, the pages that matter are the ones Google ranks. For software categories those are often forum threads, PMS marketplace listings and comparison articles rather than the vendor’s own site.

If the r/PropertyManagement thread on maintenance coordination ranks and does not mention you, that thread is doing work for a competitor. If the AppFolio or Buildium marketplace page lists you, it is doing work for you. The practical guide to being recommended by Gemini turns this into an ordered work plan.

Answer the rewrite on your own pages

A vendor page that opens with the category, the portfolio range it fits, the PMS integrations and the price model in plain sentences gives the model something to quote. Per our vendor record, checked 2 September 2026, Property Meld’s pricing page states a per-unit monthly model of $1.60 to $2.00 per unit with a $160 monthly minimum. That is a fact a model can repeat without interpretation.

Compare that with a page that says “pricing tailored to your portfolio”. It gives the model nothing.

Make the entity unambiguous while you are there. Grounding happens inside Google’s stack, and Google keeps a knowledge graph.

Use one company name and description everywhere, an Organization node with sameAs links, and a SoftwareApplication node on the product page. Together they reduce the chance of a mix-up with a similar name. The structured data guide covers which types are documented.

Check Google-Extended

Google’s crawler documentation describes Google-Extended as the robots.txt token that controls whether crawled content may be used to train Gemini models and for grounding. It does not affect a site’s inclusion or ranking in Google Search. Some vendors blocked it in 2023 or 2024 as a training opt-out without realizing the same token now covers grounding.

Look at your robots.txt. This site allows it, along with GPTBot, OAI-SearchBot, PerplexityBot and ClaudeBot; the AI crawlers guide has the full file.

What will the Index record from Gemini?

The Index does not yet publish Gemini runs. The September 2026 edition’s numbers come from ChatGPT: 25 prompts in each of 20 categories, one run each, 500 runs in all. The Gemini adapter is built against the API with grounding enabled, and it stores the following per run.

Field in the response	What the Index stores
Answer text	Stored verbatim; vendor names are extracted from it
groundingChunks	Each URI resolved to its final URL, deduplicated, stored with its title as a citation of kind “grounding”
groundingSupports	Stored in the raw response for audit; not used in metrics
webSearchQueries	Stored as the list of searches the model issued for that run
modelVersion	Stored per run, so a model change is visible in the edition record
usageMetadata	Prompt and candidate token counts, converted to a logged cost in USD

Resolution is a HEAD request that follows redirects. If it fails, the adapter keeps the redirect URL rather than guessing, so the citation shows as unresolved instead of being attributed to the wrong domain. Resolved URLs are normalized, classified by page type and linked to any vendor whose domain they match.

When Gemini runs land, the maintenance category source map will show the resolved domains per engine. The changelog below will record the first edition that carries them, and the Index methodology states the run schedule and retention rules.

Are Gemini, AI Overviews and AI Mode the same thing?

No. The Gemini API with grounding is the closest repeatable proxy for the Gemini app, and it is not the app. The consumer app carries memory, personalization and a different system prompt, and it can call tools the API request does not enable.

Google AI Overviews and AI Mode are Search surfaces, not Gemini API surfaces. Appearing in one does not imply appearing in another. The AI Overviews guide covers that surface on its own terms.

The guide to ranking in ChatGPT covers an engine with runs in the current edition, a useful contrast because ChatGPT’s web search reaches a different index.

Limits

ChatGPT and Claude are measured today. Everything here about Gemini is mechanism drawn from Google’s documentation and the adapter that will capture it, plus hypotheses labeled as such.

Non-determinism will be the first limit once runs exist. Three runs per prompt and a published consistency figure make the variance visible; they do not remove it. A vendor named in one run and absent from the next two has a consistency problem, and the Index will show that rather than average it away.

Model drift is the second, because Google changes models without notice. The modelVersion field is stored per run. When a rank moves in the same month the model string changes, the reader can weigh whether the vendor did anything; the measurement framework explains how movement is defined.

A chunk is not a mention. A grounding chunk can point at a vendor’s site while the answer never names the vendor, and an answer can name a vendor with no chunk pointing at its domain. The Index will report both separately: named vendors from the text, source dependency from the chunks.

What changes month to month?

Google can alter what grounding returns, change the redirect scheme, or change the model. The methodology’s engine record and the Index changelog record each change with the edition it took effect. Anything here that stops being true gets a dated entry below.

The AI search optimization overview sits above this page and the other engine guides. If you sell into property operations and want your Gemini row explained once it exists, the Gemini commercial page is where the work is scoped.

Changelog

• 2026-10-28: Reformatted with a short answer, question headings and an FAQ. Corrected the capture section: the Index does not yet run Gemini, and the September edition is ChatGPT only. Removed an unsupported sentence about a monthly hand-run consumer-app sample and the claim that AI Overviews are captured through a SERP provider.
• 2026-09-26: The September figures now cite the 25 ChatGPT app captures per category; the API run of 3 September 2026 is described as retired from the edition and archived.
• 2026-09-03: Rebuilt for the AI Shortlist Index scope at /gemini-seo/, replacing the former /gemini-bing-copilot-seo/ page. Microsoft Copilot removed; the Index does not measure it. Added the grounding mechanism, the capture table and the redirect-resolution note.
• 2026-06-10: First published as “Gemini and Copilot SEO”, a general-purpose page with no property-ops scope and no Index data.