# Gemini SEO: How Search Grounding Shapes Recommendations > How Gemini's Google Search grounding picks the pages behind a software recommendation, what the AI Shortlist Index captures, and what a vendor can change. Canonical: https://webpossible.com/gemini-seo/ Source: https://webpossible.com/gemini-seo/ Format: Markdown version for AI agents. The canonical HTML page is at the source URL above. Last verified: 2026-09-03 --- Methodology guide # Gemini SEO: How Search Grounding Shapes Recommendations By Ryan York · last verified 2026-09-03 When a property manager asks Gemini which maintenance coordination software to buy, the model does not answer from memory alone. With Google Search grounding turned on, it writes its own search queries, reads what Google returns, and composes an answer with the pages it used attached as grounding chunks, as described in Google’s Gemini API documentation. That grounding step decides which vendors can be named. This guide covers how it works, what the AI Shortlist Index records from it, what the one run we have so far says, and what a property-ops vendor can do about the pages the answer rides on. ## What grounding is, according to Google’s own documentation 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 property manager’s question goes in as written. The response then carries a `groundingMetadata` block, and the Gemini API documentation lists what is in it: `webSearchQueries`, the searches the model issued; `groundingChunks`, one entry per source with a `web.uri` and a `web.title`; `groundingSupports`, which tie spans of the answer text back to chunk indices with confidence scores; and `searchEntryPoint`, a rendered block of Search Suggestions that Google’s terms ask you to display alongside a grounded answer. Two details matter more than the rest. First, `webSearchQueries` is the model’s rewrite of the question. A prompt like “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, and the pages that rank for that rewrite are the only pages the model can ground on. Second, `web.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. Any measurement that stops at the raw URI ends up counting Google’s redirect host as the source of every citation, which is useless. The Index resolves each one before it classifies anything. ## What the Index captures from a Gemini run The Index queries Gemini through the API, not the consumer app, with Google Search grounding enabled. For the September 2026 edition the configured model is `gemini-3.8-flash`. Each prompt runs three times, in a fresh session each time, spread across at least two days, with the engine’s default sampling settings. The [Index methodology](/ai-shortlist-index/methodology/) states all of this in full, including the run schedule and the retention rules. | Field in the response | What the Index stores | | --- | --- | | Answer text | Stored verbatim; vendor names are extracted from it | | `groundingChunks` | Each `web.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 | _Every Gemini run keeps the answer text, the resolved grounding URLs, the supports, the model’s own search queries, the model version, and token usage. The raw API response is stored alongside, unmodified._ Resolution is a plain HEAD request that follows redirects. If it fails, the adapter keeps the redirect URL rather than guessing, and that citation shows up as unresolved instead of being attributed to a domain it may not belong to. Resolved URLs are then normalized (tracking parameters dropped), classified by page type (vendor site, review platform, listicle, trade publication, directory, forum, association, WebPossible-operated, other), and linked to any vendor whose domain they match. The September 2026 site export records no Gemini runs at all. The engine’s status is “missing” with the reason “no runs recorded”, and the only completed run in the edition is a single ChatGPT run of prompt mvn-best-01 in the maintenance and vendor network category. Everything on this page about Gemini is therefore mechanism, drawn from Google’s documentation and from the adapter that will capture it, plus hypotheses labelled as such. When Gemini runs land, the [maintenance category source map](/ai-shortlist-index/maintenance-vendor-network-software/sources/) will show the resolved domains per engine, and this page’s changelog will record the first edition that carries them. ## The chain from prompt to citation It helps to walk the chain once, slowly, because each link is a place a vendor can be present or absent. A property manager types a question. The model reads it and decides whether to search. 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 it is not guaranteed, and an ungrounded answer draws on whatever the model learned in training. If it searches, it writes queries. Our hypothesis, untested until Gemini runs exist, is that a 300-door maintenance prompt turns into searches shaped like “best maintenance coordination software for property management companies” and “maintenance coordination software 300 units”, and that the candidate pages are whatever Google ranks in the top results for those strings. That is a hypothesis about query shape, not a finding. The Index stores `webSearchQueries` so that it can publish the real rewrites rather than guess at them. Google returns results. The model reads some of them, writes an answer, and attaches supports. 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; a vendor present on one of them might be. The one ChatGPT run we have shows what the returned set tends to look like for this category: of 124 citations on prompt mvn-best-01, 38 pointed at reddit.com, 16 at appfolio.com, 12 at buildium.com, and 4 at propertymeld.com, and the answer named Property Meld first and Latchel second. We expect a Gemini run on the same prompt to lean on a similar mix of forum threads, property management system vendor pages, and listicles, because Google’s organic results for “best X software” queries are dominated by those page types, but that expectation is unverified and will be checked against the first edition with Gemini data. ## What this implies for a property-ops vendor Grounding rides on Google Search. That collapses a lot of mystery into a short list of things to do, most of them familiar to anyone who has run search for a software company. Rank for the rewrite rather than the prompt. The searches the model writes look like ordinary queries: category plus buyer context. Open Search Console and look for the queries you already appear for that contain “maintenance coordination”, “vendor network”, “resident benefits”, or “access control” together 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, and 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 integration marketplace page lists you, it is doing work for you. The [practical guide to being recommended by Gemini](/get-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. Property Meld’s pricing page, for example, states a per-unit monthly model at $1.60 to $2.00 per unit with a $160 monthly minimum, a fact a model can repeat without interpretation. Compare that with a page that says “pricing tailored to your portfolio”, which gives the model nothing. Make the entity unambiguous. Google keeps a knowledge graph, and grounding is happening inside Google’s stack. An Organization node with `sameAs` links to LinkedIn, G2, and Crunchbase, the same company name and description everywhere, and a SoftwareApplication node on the product page reduce the chance the model confuses you with a similarly named company or an old product name. The [structured data guide](/structured-data-for-ai/) covers which types are documented and which are wishful. Check Google-Extended. Google’s crawler documentation describes Google-Extended as the robots.txt token that controls whether content Google crawls may be used to train Gemini models and for grounding, and says 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. ## Three Google surfaces that are not the same thing The Index measures the Gemini API with grounding. That 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. The methodology page publishes a monthly hand-run sample in consumer interfaces to check directional agreement, and it is a sample, not a merge. Google AI Overviews and AI Mode are Search surfaces, not Gemini API surfaces, and the Index captures them separately through a licensed SERP data provider. Being named in one does not imply being named in another. The [AI Overviews guide](/google-ai-overviews/) covers that surface on its own terms, and the [ChatGPT guide](/chatgpt-seo/) covers the engine that produced the one run in this edition, which is a useful contrast because ChatGPT’s web search reaches a different index. ## Limits, and what changes month to month Non-determinism is the first limit. Three runs per prompt and a published consistency figure make the variance visible; they do not remove it. A vendor named in one Gemini run and absent from the next two has a consistency problem, and the Index shows that rather than averaging it away. Model drift is the second. Google changes models without notice. The `modelVersion` field is stored per run and shown in the edition record, so when a rank moves in the same month the model string changes, the reader can weigh whether the vendor did anything. The [measurement framework](/ai-visibility/) explains how movement is defined and why a null movement is a real value. Resolution failures are the third. A grounding chunk whose redirect cannot be followed stays as a redirect URL in the run file. It counts as a citation; it does not get a domain. If that ever affects a top-20 domain in a source map, the map says so. A chunk is not a mention. A grounding chunk can point at a vendor’s site while the answer text never names the vendor, and an answer can name a vendor with no chunk pointing at its domain. The Index reports both, separately: named vendors come from the text, source dependency comes from the chunks. Read them together. Confidence scores in `groundingSupports` are stored and unused. They describe the model’s confidence that a sentence is supported by a chunk, which is a different question from whether a vendor was named. Every one of these can change with an edition. Google can alter what grounding returns, change the redirect scheme, or change the model. The methodology’s engine record and the [Index changelog](/ai-shortlist-index/changelog/) record each change with the edition it took effect. Anything here that stops being true gets a dated entry below. The [Index methodology](/ai-shortlist-index/methodology/) is the source of truth for every number, and the [AI search optimization overview](/ai-search-optimization/) sits above this page and the other engine guides. If you sell into property operations and want your Gemini row explained, the [Gemini commercial page](/get-recommended-by-gemini/) is where the work is scoped. ## Changelog - 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 from Google’s documentation, the capture table, the redirect-resolution note, and the disclosure that the 2026-09 edition records no Gemini runs. - 2026-06-10: First published as “Gemini and Copilot SEO”, a general-purpose page with no property-ops scope and no Index data. Part of [the methodology pillar](/ai-search-optimization/). What to do about it: [the practice page](/get-recommended-by-gemini/).