TL:DR;<\/strong><\/summary>\n\n- Gemini, AI Mode and AI Overviews all run on Google\u2019s index and AI reasoning pipeline. This means that you can optimize for all three simultaneously once you understand how the foundational technology works. <\/li>\n\n\n\n
- First, Google builds a behavioral profile of a querier to deliver personalized results. It then processes a query by looking for related queries, and building a \u2018custom corpus\u2019: a set of passages that directly relate to the ask. <\/li>\n\n\n\n
- Next, Google decides which passages better answer the query, and uses specialized models to summarize, compare, and extract from the custom corpus. <\/li>\n\n\n\n
- Finally, it decides what to paraphrase and what to cite, and gives a searcher a custom, contextual answer. <\/li>\n<\/ul>\n<\/details>\n\n\n\n
Next: <\/strong>In Part 2, we show how to turn this new understanding of Google’s AI into concrete steps you can take to increase your chance of appearing in its AI answers. <\/p>\n\n\n\nGemini, AI Mode, and AI Overviews All Work as One System<\/h2>\n\n\n\n
Google Search is the centre of everything.<\/p>\n\n\n\n
Gemini, AI mode, and AI overviews share the same data sources, the same retrieval pipeline, and the same ranking logic based on clarity, depth, and authority. The only differences are in how the answer is displayed, not how it\u2019s chosen.<\/p>\n\n\n\n
Google\u2019s index <\/strong>is the data source that powers every new AI experience Google releases.<\/p>\n\n\n\nGemini, Google\u2019s large language model, <\/strong>sits on top of that index. <\/strong>It interprets the same information, but builds reasoning chains and generates personalized answers. <\/p>\n\n\n\nAI Mode and AI Overviews<\/strong> are just different ways that Google\u2019s AI output shows up to users.<\/p>\n\n\n\nThat\u2019s why this guide treats them together. <\/p>\n\n\n\n
Because when you understand Gemini\u2019s reasoning process or how AI Mode decides which passages to cite, you know how Google\u2019s entire AI ecosystem evaluates and presents information.<\/p>\n\n\n\n
That means you can optimize for all three systems using the same techniques \u2014 more on this in Pt. 2. <\/p>\n\n\n\n![\"Google's](\"https:\/\/singularity.digital\/wp-content\/uploads\/2025\/12\/How-Gemini-and-Google-AI-Mode-Rank-Your-Content-02-1-1024x576.png\")
<\/figure>\n\n\n\nHow Gemini & Google AI Mode Ranks Your Content<\/h2>\n\n\n\n![\"Gemini's](\"https:\/\/singularity.digital\/wp-content\/uploads\/2025\/12\/image-1.jpeg\")
<\/figure>\n\n\n\nSource: IPullRank<\/a><\/p>\n\n\n\nStage 1: Understanding User Context and Personalization<\/h3>\n\n\n\n
Before Gemini fetches a single page, it builds a live picture of the searcher \u2013\u2013 who they are, what they\u2019ve been exploring, and how that shapes what they mean by their query. This will determine what Google decides to show a user throughout the rest of its AI processing.<\/p>\n\n\n\n
To do that, Google transforms a user\u2019s activity into vec<\/a>tor embeddings<\/a>. These are numerical representations of behavior built from past queries, clicks, locations, and devices. These embeddings are stored as part of the system\u2019s stateful context<\/a>, which acts like a running memory of each person\u2019s interactions with Google. <\/p>\n\n\n\nThat means Gemini can reference its stored context to interpret a user\u2019s intent over time.<\/p>\n\n\n\n
For example, someone who\u2019s been researching marketing automation and CRM integrations will activate different vector associations than a nonprofit team lead searching the same words. And when the model later performs query fan-out (discussed in the next stage), those embeddings guide the direction of expansion and the type of content retrieved.<\/p>\n\n\n\n
Takeaway for SaaS teams<\/strong>: To gain visibility in Google\u2019s AI results, you have to deeply understand your ICPs \u2013 their interests, workflows, pain points, buying behaviors, and even word choice. Then, you need to ensure your content consistently reflects yourICP\u2019s buyer journey<\/a> and speaks directly to their intent and positioning. Matching your ICP’s context shortens your vector distance<\/a>, which should increase the likelihood of being in the AI’s final answer<\/p>\n\n\n\n![\"User](\"https:\/\/singularity.digital\/wp-content\/uploads\/2025\/12\/User-Context-and-Personalization-1024x576.png\")
<\/figure>\n\n\n\nStage 2: Expanding the Query Through Fan-Out<\/h3>\n\n\n\n
Once Google has created a contextual picture of the querier (based on their demographics, past search behaviors, locations and devices), it looks at what<\/em> the person typed and decides which retrieval path to take.<\/p>\n\n\n\nIf the user has given Google a specific URL to look at, the system switches into browsing mode<\/a>, simply visits that page, and reads it directly.<\/p>\n\n\n\n400But when the user enters a broad query such as \u201cbest CRM for small teams,\u201d Gemini activates what Google calls query fan-out<\/a>.<\/strong> And instead of treating the phrase as one request, it creates dozens of related variations, including synonyms, comparisons, pricing filters, integrations, or industry constraints. \u201cBest CRM for small teams\u201d could branch into:<\/p>\n\n\n\n\n- \u201cTrello vs Asana for finance teams\u201d<\/li>\n\n\n\n
- \u201cproject management software with SOC 2 compliance\u201d<\/li>\n\n\n\n
- \u201caffordable SaaS collaboration tools for remote teams\u201d<\/li>\n<\/ul>\n\n\n\n
![\"Google's](\"https:\/\/singularity.digital\/wp-content\/uploads\/2025\/12\/image-8-1024x879.png\")
<\/figure>\n\n\n\nSource: Semrush<\/a><\/p>\n\n\n\nThese query variations will then be passed through Google’s content_fetcher function. Where browsing<\/em> gets information from single pages, content_fetcher<\/em> begins large-scale retrieval<\/p>\n\n\n\nTakeaway for SaaS teams:<\/strong> Instead of writing one page per target keyword, write more comprehensive content that answers user questions more fully. Describe your features, industries, and integrations in clear, entity-rich<\/a> language, and use real-world details of your product and results, so Gemini\u2019s content_fetcher<\/em> is more likely to recognize your relevance across its fan-out landscape.<\/p>\n\n\n\nStage 3: Building the Custom Corpus <\/h3>\n\n\n\n
After Gemini expands the query through fan-out, it needs to gather the actual material it will process. Each of those sub-queries sends results through the content_fetcher pipeline, pulling text from relevant pages. This is how Google starts constructing what it calls a custom corpus (in this patent)<\/a>, which is essentially a mini index, built just for that specific question.<\/p>\n\n\n\nThen, the system creates the vector embeddings (numerical representation of meaning) of every query, passage, and document, and measures how closely these embeddings align with the combined intent behind the user\u2019s query and its expanded variations. <\/p>\n\n\n\n
As a result of this evaluation, passages with the highest similarity scores stay in the index, and weaker matches are filtered out.<\/p>\n\n\n\n
Next: <\/strong>In Part 2, we show how to turn this new understanding of Google’s AI into concrete steps you can take to increase your chance of appearing in its AI answers. <\/p>\n\n\n\n Google Search is the centre of everything.<\/p>\n\n\n\n Gemini, AI mode, and AI overviews share the same data sources, the same retrieval pipeline, and the same ranking logic based on clarity, depth, and authority. The only differences are in how the answer is displayed, not how it\u2019s chosen.<\/p>\n\n\n\n Google\u2019s index <\/strong>is the data source that powers every new AI experience Google releases.<\/p>\n\n\n\n Gemini, Google\u2019s large language model, <\/strong>sits on top of that index. <\/strong>It interprets the same information, but builds reasoning chains and generates personalized answers. <\/p>\n\n\n\n AI Mode and AI Overviews<\/strong> are just different ways that Google\u2019s AI output shows up to users.<\/p>\n\n\n\n That\u2019s why this guide treats them together. <\/p>\n\n\n\n Because when you understand Gemini\u2019s reasoning process or how AI Mode decides which passages to cite, you know how Google\u2019s entire AI ecosystem evaluates and presents information.<\/p>\n\n\n\n That means you can optimize for all three systems using the same techniques \u2014 more on this in Pt. 2. <\/p>\n\n\n\n Source: IPullRank<\/a><\/p>\n\n\n\n Before Gemini fetches a single page, it builds a live picture of the searcher \u2013\u2013 who they are, what they\u2019ve been exploring, and how that shapes what they mean by their query. This will determine what Google decides to show a user throughout the rest of its AI processing.<\/p>\n\n\n\n To do that, Google transforms a user\u2019s activity into vec<\/a>tor embeddings<\/a>. These are numerical representations of behavior built from past queries, clicks, locations, and devices. These embeddings are stored as part of the system\u2019s stateful context<\/a>, which acts like a running memory of each person\u2019s interactions with Google. <\/p>\n\n\n\n That means Gemini can reference its stored context to interpret a user\u2019s intent over time.<\/p>\n\n\n\n For example, someone who\u2019s been researching marketing automation and CRM integrations will activate different vector associations than a nonprofit team lead searching the same words. And when the model later performs query fan-out (discussed in the next stage), those embeddings guide the direction of expansion and the type of content retrieved.<\/p>\n\n\n\n Takeaway for SaaS teams<\/strong>: To gain visibility in Google\u2019s AI results, you have to deeply understand your ICPs \u2013 their interests, workflows, pain points, buying behaviors, and even word choice. Then, you need to ensure your content consistently reflects yourICP\u2019s buyer journey<\/a> and speaks directly to their intent and positioning. Matching your ICP’s context shortens your vector distance<\/a>, which should increase the likelihood of being in the AI’s final answer<\/p>\n\n\n\n Once Google has created a contextual picture of the querier (based on their demographics, past search behaviors, locations and devices), it looks at what<\/em> the person typed and decides which retrieval path to take.<\/p>\n\n\n\n If the user has given Google a specific URL to look at, the system switches into browsing mode<\/a>, simply visits that page, and reads it directly.<\/p>\n\n\n\n 400But when the user enters a broad query such as \u201cbest CRM for small teams,\u201d Gemini activates what Google calls query fan-out<\/a>.<\/strong> And instead of treating the phrase as one request, it creates dozens of related variations, including synonyms, comparisons, pricing filters, integrations, or industry constraints. \u201cBest CRM for small teams\u201d could branch into:<\/p>\n\n\n\n Source: Semrush<\/a><\/p>\n\n\n\n These query variations will then be passed through Google’s content_fetcher function. Where browsing<\/em> gets information from single pages, content_fetcher<\/em> begins large-scale retrieval<\/p>\n\n\n\n Takeaway for SaaS teams:<\/strong> Instead of writing one page per target keyword, write more comprehensive content that answers user questions more fully. Describe your features, industries, and integrations in clear, entity-rich<\/a> language, and use real-world details of your product and results, so Gemini\u2019s content_fetcher<\/em> is more likely to recognize your relevance across its fan-out landscape.<\/p>\n\n\n\n After Gemini expands the query through fan-out, it needs to gather the actual material it will process. Each of those sub-queries sends results through the content_fetcher pipeline, pulling text from relevant pages. This is how Google starts constructing what it calls a custom corpus (in this patent)<\/a>, which is essentially a mini index, built just for that specific question.<\/p>\n\n\n\n Then, the system creates the vector embeddings (numerical representation of meaning) of every query, passage, and document, and measures how closely these embeddings align with the combined intent behind the user\u2019s query and its expanded variations. <\/p>\n\n\n\n As a result of this evaluation, passages with the highest similarity scores stay in the index, and weaker matches are filtered out.<\/p>\n\n\n\nGemini, AI Mode, and AI Overviews All Work as One System<\/h2>\n\n\n\n
<\/figure>\n\n\n\nHow Gemini & Google AI Mode Ranks Your Content<\/h2>\n\n\n\n
<\/figure>\n\n\n\nStage 1: Understanding User Context and Personalization<\/h3>\n\n\n\n
<\/figure>\n\n\n\nStage 2: Expanding the Query Through Fan-Out<\/h3>\n\n\n\n
\n
<\/figure>\n\n\n\nStage 3: Building the Custom Corpus <\/h3>\n\n\n\n
![\"CRM](\"https:\/\/singularity.digital\/wp-content\/uploads\/2025\/12\/How-Gemini-and-Google-AI-Mode-Rank-Your-Content-06-PT-2-426x1024.png\"){kind=icon}
<\/figure>\n\n\n\n\n