Guide · AI search optimization
AI search optimization: what GEO is and how it works
AI search optimization is the practice of getting AI assistants and AI search features to find, trust and recommend your brand. Here is what GEO, AEO and LLMO mean, how AI answers get built, and a three-stage method you can run yourself: find, measure, fix.
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The AI search optimization loop
Find · Measure · Fix- Find the questions buyers ask AI about your category
- Measure who AI names, in what order, and which sources it cites
- Fix what blocks you: site access, missing answers, source gaps
- Re-measure the same prompts, then repeat
In short
AI search optimization is the practice of making your brand easy for AI engines like ChatGPT, Gemini, Perplexity and Google’s AI Overviews to find, understand and recommend. It is also called generative engine optimization (GEO), answer engine optimization (AEO) or LLM optimization (LLMO), and it works as a loop: find the questions that matter, measure where you stand, fix what keeps you out. Rankry is an AI search optimization platform built around that loop.
The terms
What is GEO, and how is it different from AEO and LLMO?
The industry has not settled on one name. The labels overlap heavily and most practitioners use them interchangeably. Here is where each one comes from.
| Term | Stands for | Where it comes from | Usual focus |
|---|---|---|---|
| GEO | Generative engine optimization | A 2023 research paper by researchers at Princeton and other institutions, presented at KDD 2024 | Being included and cited in answers written by generative AI |
| AEO | Answer engine optimization | An older SEO term from the era of featured snippets and voice assistants | Being the direct answer to a question, now including AI answers |
| LLMO | Large language model optimization | Industry usage | How large language models describe and recommend your brand |
| AI SEO | SEO for AI search | Industry usage | Applying SEO practice to AI features such as AI Overviews and AI Mode |
| AI search optimization | A plain-English umbrella term | Descriptive usage | All of the above: visibility in any AI-generated answer |
Rankry uses “AI search optimization” as the umbrella term. When people say GEO, they almost always mean the same work.
Under the hood
How do AI engines build an answer?
Knowing the mechanics tells you where optimization can work. Most AI search answers go through the same stages, even though each engine keeps its details private.
- 01
The question arrives
A user asks in natural language, often with context like location, budget or use case. The engine may rewrite it into one or more search queries.
- 02
The engine decides whether to search
Some questions are answered from what the model learned in training. For others it searches the web first. Perplexity and Google’s AI features are built on search; ChatGPT, Claude, Gemini and Grok decide per question.
- 03
Pages are retrieved
The engine pulls candidate pages from a search index, its own or a partner’s. Pages that are blocked, not indexed or unreadable without JavaScript drop out at this stage.
- 04
The model writes the answer
It combines retrieved text with what it already knows, chooses which brands to name and in what order, and writes a summary in its own words.
- 05
Sources are shown
Most engines show some of the pages they used as citations or links. Which pages get shown, and how, differs from engine to engine.
End to end
How does AI search optimization work end to end?
AI search optimization works as a loop with three stages. Find the questions your buyers ask AI and the engines they use. Measure how often each engine names you, in what position, next to which competitors, and from which sources. Fix the causes: pages AI can’t read, questions you have no good answer for, and third-party sources that mention competitors but not you.
Then measure again with the same prompts. The loop matters because AI answers vary from run to run and models change, so a one-off audit goes stale quickly. Treat it the way you treat SEO: a recurring process with a stable set of tracked questions, not a single project with an end date.
The sections below walk through each stage, with what you can do by hand and where a platform like Rankry takes over the repetitive parts.
Stage 1 · Find
How do I find the prompts that matter?
Your prompts are the questions you will be measured on. Pick them the way you would pick keywords, but phrase them the way people talk to an assistant.
- Start from the buyer’s problem, not your product name
- Cover each stage: discovering options, comparing, choosing, and checking alternatives
- Include “best X for Y” and “alternatives to [competitor]” questions
- Add a location to prompts if you serve a city or region
- Mine sales calls, support tickets and site search for real wording
- Check which Google searches you already appear for in Search Console
- Group prompts into categories so you can see where you are strong or weak
- Keep the set stable once you start, so runs stay comparable
Stage 2 · Measure
What should I measure in AI search?
Measure the same things for you and your competitors, on the same prompts and engines, every run.
Visibility
The share of answers that name you. The headline number for whether you are in the conversation at all.
Position
Your average rank when named. Being listed first or second matters when answers name several brands.
Share of voice
Your mentions against the competitors you track. It shows who AI favors in your category.
Sentiment
Whether answers describe you positively, neutrally or negatively, and what exactly they say.
Sources
Which domains and pages each engine cites, and which of them mention competitors but not you.
Coverage by engine
Which engines name you. A gap in one engine usually has a specific cause worth finding.
Stage 3 · Fix
How do I fix what keeps AI from naming my brand?
Match what you measured to the fix. Most problems fall into one of these patterns.
| What you see | Likely cause | What to do |
|---|---|---|
| Missing from one engine only | That engine’s crawler is blocked, or the sources it prefers don’t cover you | Check robots.txt for that engine’s search agent; look at which domains it cites for your prompts |
| Missing from most engines | Little content or third-party coverage answers the prompt with you in it | Publish a page that answers the question directly; get listed on the review sites and lists AI cites |
| Named, but low in the list | Competitors are mentioned more often or more favorably in the sources | Earn reviews, comparisons and mentions; make your differentiators explicit on your own site |
| Named with wrong facts | Outdated or inconsistent information in sources AI relies on | Correct your own pages first, then profiles, directories and listings |
| Competitors’ pages cited, yours not | Your pages don’t answer the question as directly, or aren’t retrieved | Add answer-first sections, clear headings, tables and FAQs; confirm the pages are indexed |
| Your page cited, but you aren’t recommended | The page is used for a fact, not as a recommendation | State who the product is for and how it compares, in plain words AI can quote |
With Rankry
How does Rankry support each stage?
Rankry is an AI search optimization platform: it covers find, measure and fix in one place.
Find: prompts and opportunities
Prompts written from your site at onboarding, your own prompts or a CSV, and AI Volume estimates. The Opportunities page flags tracked prompts no brand owns yet and, with Search Console connected, Google searches you don’t track yet.
Measure: up to nine AI engines
ChatGPT, Claude, Gemini, Perplexity, Grok and Meta Muse through their APIs with web search, Google AI Overviews and AI Mode from Google’s live results, and Copilot as Azure GPT with Bing web search. Visibility, Position, Share of voice, Sentiment and Diversity per engine.
Measure: competitors and sources
Competitors found automatically from your first report, a Compete page with head-to-head views, and a Sources page that marks each cited domain as Yours, Gap or Mixed.
Fix: AI Readiness
A 36-check audit of your homepage across crawl access, rendering, structured identity, content architecture, citation readiness and freshness, graded A to F.
Fix: Action plan and Tasks
Three steps in order: Fix your website, Win open questions, Beat your competitors. Each action card can be added to Tasks and tracked through to done.
Fix: Content Studio
Draft blog articles, comparison pages, FAQ sections and more from a task or opportunity, then edit and export as Markdown, CSV or Word (DOCX).
Guides
Keep reading
What is AI search optimization?
AI search optimization is the practice of getting AI assistants and AI search features, such as ChatGPT, Gemini, Perplexity and Google’s AI Overviews and AI Mode, to find, trust and recommend your brand. It combines technical access, content that answers buyer questions, and presence in the third-party sources AI relies on.
What is GEO?
GEO stands for generative engine optimization: shaping your content and presence so generative AI systems include and cite you in their answers. The term was introduced in a 2023 research paper by researchers from Princeton and other institutions, and it is now used broadly for AI search optimization.
Is GEO the same as AEO?
In practice, mostly yes. AEO, answer engine optimization, began with featured snippets and voice assistants and now covers AI answers too. GEO was coined for generative AI. Both aim to make your brand the answer, and the tactics overlap almost completely.
Does AI search optimization replace SEO?
No. Engines that search the web retrieve pages from search indexes, so crawlable, indexed, well-structured pages remain the foundation. AI search optimization adds new measurements and puts more weight on third-party sources and on answering questions directly.
How long does AI search optimization take to work?
Changes that engines pick up through search, like a new answer page or an unblocked crawler, can show up after the page is re-crawled. Changes that depend on new reviews, articles and mentions take longer. What a model learned in training only changes with new model versions.
Can anyone guarantee a brand gets recommended by AI?
No. AI engines decide what to include, answers vary between runs, and the engines change often. What you can do is remove blocks, give engines clear answers to quote, earn mentions in the sources they cite, and measure whether your share of answers grows.
What tools do I need for AI search optimization?
At minimum: access to your site and robots.txt, the AI assistants themselves for manual checks, and a spreadsheet. As you scale, a tracking platform saves time by running the same prompts on a schedule across engines and keeping every answer. Search Console helps you find real queries to turn into prompts.
Which content formats do AI engines cite?
No engine publishes a rule. Pages that answer a question in their first sentences, use clear headings, and include specifics such as comparisons, tables, prices and steps are easier to quote. For recommendation questions, reviews, forum threads and comparison articles from other sites also commonly appear among the sources.
How does Rankry help with AI search optimization?
Rankry finds the prompts that matter, measures how AI engines answer them, and turns the gaps into an Action plan, tasks and draft content. It covers ChatGPT, Claude, Gemini, Perplexity, Grok, Meta Muse, Google AI Overviews, AI Mode and Copilot.
Run the loop on your own brand
Start with a report on how AI answers your buyers’ questions today. Rankry builds the prompts from your site.
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