How to Rank in Gemini (2026 Playbook)

Key takeaways Read this if nothing else

01
I never start how to rank in Gemini work until Google can crawl, index, and parse the URL.
02
Answer-first writing, structured data, and consistent entity names are what I use to make a page extractable.
03
Topical authority and freshness only help after the page is reachable; I apply freshness when the query is time-sensitive.
04
I treat gemini seo as an extension of SEO: the same FAQ, URL, and density habits that supported 7 million organic impressions are the base I still measure against citation gaps.

Why I treat Gemini as a Google Search problem first

I treat Gemini ranking as a Google Search problem first. Gemini sits inside Google's product stack, which is how I still frame it when I read The Verge's Gemini product coverage, so a URL that is already crawlable, indexed, and competitive in classic results is the one I bother optimizing for citation-style reuse. Schema, answer-first copy, clusters, and freshness only start after that gate. I use the same order when a team asks how to rank in google ai mode in 2026: get the page eligible, then make it extractable. That is the working sequence I run before any Gemini-specific rewrite.

Gemini lives inside Google's AI ecosystem

When I map how to rank in Gemini, I start from where Gemini actually lives. Gemini is part of Google's AI ecosystem, so content that is already strong in Google Search is more likely to be eligible for Gemini-style surface reuse and citation. I do not treat a Gemini mention as a separate index. If a page already ranks and gets impressions in classic Search, I treat that as a prerequisite, not a coincidence.

I check Search Console coverage and classic rankings before I rewrite a paragraph for extractability. A page that is blocked, canonicalized away, or stuck in a discovery state is not a citation candidate in my workflow. I use the same eligibility lens I use for other Google AI surfaces: if Google cannot fetch the HTML, I do not expect Gemini to quote it. The ecosystem framing keeps my backlog honest about what is even eligible. That is the eligibility I optimize first.

I optimize for eligibility before I chase citations

I optimize for eligibility before I chase citations. That order is non-negotiable on my projects. I confirm Google can fetch the URL, that it is indexed, and that the canonical is the URL I actually want cited. Only then do I open the page for answer-first edits or structured data. Google's structured data introduction sits downstream of that gate in my sequence: markup helps machines parse a page that is already in the index, not a page the crawler never sees.

If Search Console still shows a coverage error, I do not spend a sprint on prompt-shaped headings. I fix robots, noindex, internal links, and rendering first. Citation tactics without eligibility are work I cannot measure. That is the order I write on the brief so the writing team does not start with schema while the URL is still blocked. I keep that sequence on every new URL, not only on launches.

How this work sits next to other Google AI surfaces

The work I do on how to rank in Gemini sits next to other Google AI surfaces rather than in a silo. AI Overviews, AI Mode, and Gemini-assisted answers all depend on Google being able to fetch, parse, and evaluate the same URL. When I ship a crawl fix or a clearer entity block, I expect that work to travel. I do not maintain a Gemini-only content calendar.

I still write engine-specific notes, extract length, heading shape, freshness on time-sensitive queries, but the foundation is shared. If a page is strong enough for classic Search and for AI Overviews, I treat it as more eligible for Gemini reuse than a page I never indexed. Technical quality, entity consistency, and an indexed URL are the shared inputs. Gemini-specific drafting is the last layer I add, not a parallel site. That is why my tickets say Google eligibility first, then answer extract, then measure citations.

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Step 1: Get the page crawlable and indexed

I open every playbook on how to rank in Gemini with crawlability and Google indexation. A URL that is not in the index is not a citation candidate, no matter how clean the prose is. I do not start citation work on a URL Google cannot fetch. That is the same order I use on other answer engines; I wrote more on how to rank in chatgpt with the same gate. Until Search Console shows the URL as indexed, schema and answer-first copy wait.

Indexation is the gate I never skip

If Google cannot fetch a URL, I do not expect that URL to show up in Gemini-assisted answers. There is no citation tactic that bypasses a blocked crawl. I treat indexation as a hard gate: the page must return a 200, allow robots, stay free of noindex, and appear in Search Console as indexed before I call it eligible. Google systems still need to access, parse, and evaluate the page before it can appear in AI-assisted answers, which is why I keep fetch failures at the top of the backlog.

I watch coverage reports weekly on the URLs I care about. A sudden crawled-currently-not-indexed status or a robots block ends the citation conversation until it is fixed. I have shipped answer blocks on pages that were never indexed; those blocks never got reused. I log coverage next to every citation check so I do not misread a fetch problem as a writing problem.

What crawlable means when I inspect a URL

When I inspect a URL I run the same four access checks every time. I fetch robots.txt and the page-level robots meta or X-Robots-Tag for a noindex or nofollow. I confirm at least one internal link reaches the URL from an already-indexed page. I then render the HTML the way Googlebot would: main content in the initial response, not trapped behind a client-only shell I cannot see in view source. If any of those four fail, I stop.

I also check the canonical, the HTTP status, and whether the sitemap I fetched actually lists this URL. I do not call a page crawlable because it loads in my browser. Crawlable means Googlebot can request it, receive indexable HTML, and discover it without a secret URL. That is the inspection I paste into the ticket before anyone rewrites the lead. Those four checks take minutes and save me a wasted writing sprint.

When I move from crawl work to citation work

I move from crawl work to citation work when Search Console shows the URL indexed and my four access checks are clean. That is the handoff. Extraction tactics, answer-first lead, question-shaped headings, entity strings, structured data, start on that indexed URL, not on a staging path Google never saw.

I often run the same handoff in parallel for other engines. ChatGPT, Perplexity, and Copilot do not share Google's index, but I still refuse to polish extracts on a page I have not confirmed is publicly fetchable. For Gemini, the Google index is the actual gate. Once it is open, I put citation work on the same backlog as the next technical ticket so the page does not stall between indexed and extractable. I date that handoff in the sheet so the next sprint starts from extractability, not from another robots debate. Parallel engine checks happen the same week.

Step 2: Make entities obvious with structured data

Once the URL is indexed, I make entities obvious with structured data and with on-page language that does not drift. Structured data and consistent brand, category, and audience strings help Google parse the page, which supports interpretation and citation-style reuse for how to rank in Gemini. I use the same identity discipline when I write more on how to get cited by perplexity: one name, one category, no aliases on the same URL. Markup without consistent names is work I redo later.

What structured data does for machine reading

I add JSON-LD so Google can read the content type and the entities on the page without guessing from noisy HTML. Structured data helps Google understand page entities and content types, which supports clearer machine interpretation of a page. That interpretation is what I want Gemini-related systems to inherit. I mark Article, FAQPage, Organization, or Product only when the visible page actually contains that content. I do not add types I cannot defend in the HTML.

I validate the markup after every deploy. If the entity name in JSON-LD does not match the H1 and the byline, I treat that as a bug. Machines get a cleaner graph when the visible strings and the markup agree. That is the whole job of this step: reduce ambiguity about who the brand is, what the page is, and who it is for. I keep the JSON-LD in the initial HTML, not injected late.

The entity fields I keep identical on every page

The strings I refuse to let drift are brand, category, product type, and audience. I pick one legal or trading name and use it in the H1, the first paragraph, the Organization markup, and the author or publisher field. Category and product type stay identical on hub and spoke pages so Google does not have to reconcile platform, tool, and software as if they were different things. Audience is a single phrase, who the page is for, repeated where it matters, not a new persona every URL.

If a designer wants a catchier label, I keep the canonical string in the body and in schema anyway. Drift is how entity clarity falls apart. The guidance I follow is simple: state brand, category, and audience consistently so the page is parseable. I audit those four fields before I publish, and I reject copy that invents a fifth name for the same thing.

Same names on owned pages and third-party profiles

I extend the same names off-site. Owned pages and third-party profiles must use the same brand string, the same category, and the same audience label. That includes LinkedIn, Crunchbase, Wikipedia if it exists, app stores, and partner directories. If third-party profiles feed conflicting names, I do not expect Gemini to pick a clean entity. Gemini-related SEO guidance I follow says brand, category, and audience should stay consistent across owned and third-party profiles.

I keep a one-row identity sheet: legal name, short name I allow, category, product type, audience, and the URLs of the profiles I control. When a listing drifts, I correct it. I do not need every profile to rank. I need them not to contradict the site. That is how I actually follow entity-clarity guidance instead of only marking up my own HTML. Identical names on owned pages and on profiles I control is the off-site half I can ship.

Step 3: Write the answer in the first screen

I treat answer-first drafting as the writing step that sits after crawl and entities. Gemini still needs a span it can lift without rewriting my page. I put the claim in the first screen, then the proof. Direct, concise answers are easier for AI systems to extract, which is why I write that span first, a pattern I treat as standard Gemini-oriented drafting. I do not start this step for how to rank in Gemini until robots, indexation, and entity strings are already stable.

Lead with the direct answer, then the proof

I open with one sentence that could stand alone as an extract. Then a short definition. Then evidence, caveats, and the longer argument. That order is not a style preference. If the first paragraph is setup, the extractable span is gone.

When I draft, I write the claim as if someone asked the question in a prompt. I do not warm up. I state the answer, define the term in one clause, then show how I know. Proof comes from a method, a date, a source, or a constraint I actually observed. Caveats sit after the claim so they do not split the extract.

I keep the first screen to a few short paragraphs so a machine can take the lead without pulling in a nested aside. History, walkthroughs, and edge cases live below that block. The lead is claim, definition, proof, in that sequence, every time I ship a page meant for citation. I reread the first screen as a standalone block before I publish.

Question-shaped headings that match how people prompt

I turn the way people actually prompt into H2s and H3s. I do not paste the primary keyword into every heading. I take the question shape, how, what, when, why, and match the words a person would type into Gemini or Search. The heading is a label for the answer block under it, not a ranking trick.

If the prompt is what crawlable means for a URL, that becomes the H3, and the first sentence under it answers it. I keep one idea per heading so the extract does not mix two claims. I also avoid stuffing how to rank in Gemini into headings that are really about writing or schema. The page already has a title. Subheads exist to match the follow-up questions I hear in the field.

I check headings against real prompts from Search Console queries and from chats teams send me. If a heading would never be asked, I rewrite it until it would.

What I cut so the extract stays clean

I cut hedges, throat-clearing intros, and nested clauses from the block I want extracted. Phrases like it depends or there are many factors stay out of the first screen unless the whole answer is a condition. If a caveat matters, I put it in its own short sentence after the claim, not inside it.

I also cut in this article we will cover and any sentence that only points at later sections. Those do not stand alone. Relative clauses that stack three qualifications get split or deleted. I want one subject, one verb, one object in the extractable span.

Pronouns that point off-screen get replaced with the entity. This and it are fine inside a paragraph that already named the thing. They are not fine as the first word of an answer block. I reread the lead as if it were pasted into a chat with no surrounding page. If it needs the H2 to make sense, I rewrite until it does not.

Step 4: Cover the subject until you look consistently relevant

I build topical authority because AI-generated answers tend to favor sources that stay relevant and trustworthy across a subject. I treat that as the working assumption for citation-style reuse in Gemini. One mega-article does not do that job. I map a hub and supporting pages, keep entity language stable, and I stop shipping thin URLs that cannot support a citation. This step sits after answer-first writing because coverage without a clean extract still fails. I would rather publish fewer pages that hold together than a scatter of one-offs.

Clusters beat one-off posts

I map a subject into a hub and supporting pages instead of one mega-article. The hub states the core definition and the decision a reader has to make. Each supporting URL owns one sub-question: a comparison, a setup step, a failure mode, or a measurement method. Internal links go both ways so Google can see the cluster as one topic, not a pile of orphans.

I do not clone the hub into every spoke. The spoke opens with its own answer, then points back. If two pages would extract the same sentence, I merge them. Breadth without a unique extract is still a one-off wearing extra URLs.

When a team wants how to rank in Gemini on a new theme, I start with the cluster map before I write a word. The map is a list of questions, owners, and the entity strings that must not drift. I only add a URL when I can name the extract it will contribute.

Trust signals I actually control

I stick to signals I can actually put on the page: a byline with a real name, sources I cite, visible dates, and consistent entity language. I do not invent trust theater. If I cannot name the author, I do not pretend there is one. If I used a source, I link it. If the page changed, the date changes.

Brand, category, product type, and audience stay identical to the strings I set in structured data. Drift between the byline, the body, and a third-party profile is a parsing problem I created. I keep those strings in a short glossary so writers do not improvise.

I also keep the same facts in the same order when I mention a method twice. That is not voice. That is a machine being able to reconcile two paragraphs as one entity. Dates sit near the claim they qualify, not only in a footer. I would rather leave a date off than put a date I cannot defend.

Thin pages I stop publishing

I run a coverage test before a URL goes live. Can this page answer one prompt without borrowing a paragraph from another URL? If the answer is no, I do not publish it. Thin in my notes means no unique extract, no named entity, and no source a reader could check. Those pages do not support how to rank in Gemini. They dilute the cluster.

I also stop drafts that only restate the hub with synonyms. If I cannot point to a new question, a new constraint, or a new measurement, the draft stays in the doc. Word count is not the test. The test is whether Gemini could cite this URL for a reason the hub cannot cover.

After publish I watch whether the page earns impressions or citations on its own query. If it never does, I merge it back into the hub rather than leaving a dead spoke. I log the reason so I do not republish the same thin idea next quarter.

Step 5: Refresh pages when the query cares about now

I use freshness as a lever for how to rank in Gemini only when the query cares about now. Recently updated pages are easier to present as current, which is why I treat freshness as a useful lever on time-sensitive queries and not on every URL. I do not churn evergreen definitions to look active. When I do update, I update in place, keep the slug, and show a last-updated date I can defend. That policy is how I read freshness against Gemini-style answers.

When freshness is a real lever

I separate time-sensitive prompts from evergreen definitions. Pricing changes, product names, API limits, election results, and as of this year comparisons get a refresh cycle. Definitions of crawl, index, entity, and schema do not. If the answer would be the same in 18 months, I do not touch the date to look new.

I decide this from the query, not from a content calendar. If Search Console shows the query with a year, a version, or a now, freshness can move the needle. If the query is a definition, I leave the page until a fact is wrong. I write that decision in the sprint note so the next editor does not refresh the wrong URL.

For gemini seo work I apply the same split. A page that explains how citations work stays until the mechanism changes. A page that lists current Gemini surfaces gets a dated review. Mixing those two jobs on one URL is how I used to wreck both.

How I update without wrecking URLs

I update in place. The slug stays. The title stays unless the title is now false. I change the body, the visible last-updated date, and any structured date field that I already ship. I do not create /v2 or a new path for a refresh. New paths split equity and confuse which URL should be cited.

Last-updated is a date I can defend from the diff. If I fixed a typo, I do not bump the date. If I changed a number, a product name, or a step, I bump it and I say what changed in one sentence near the top. That sentence is itself extractable.

Redirects exist for true merges, not for cosmetics. If I must change a slug, I 301 once and I update every internal link in the cluster in the same deploy. I check indexation after, because a refresh that drops out of Google Search is not a freshness win. Eligibility still starts with crawl.

What I leave alone on purpose

I leave evergreen pages alone on purpose. Definitions, process explainers, and entity glossaries do not get a rewrite so the sitemap looks busy. Freshness is a signal, not a habit. If I bump dates on pages that did not change, I add no new fact and I spend editor time I needed for the time-sensitive URLs.

I also leave working extracts alone. If the first screen already answers the prompt, I do not improve the voice in a way that lengthens the clause. Voice edits that nest caveats back into the claim are how I used to lose citations. I will fix a factual error. I will not restyle a clean extract for the sake of a publish.

The backlog I keep is a list of URLs with a reason to change: a wrong number, a renamed product, a new surface, or a missing FAQ. No reason means no edit. That is how I keep gemini seo freshness work from turning into churn.

Step 6: Keep the page fast, parseable, and reachable

I treat technical quality as eligibility work for how to rank in Gemini, not a separate craft. Google systems still have to fetch the URL, render the HTML, and evaluate the main content before any Gemini-assisted answer can reuse it. If the page is blocked or wrapped in a shell the crawler cannot parse, I skip extractability edits. I debug access first, then rendering, then structured data. That order stops me rewriting copy on a URL that never entered the index.

Access, render, and parse in that order

When I debug a URL I walk a fetch path in one direction. HTTP first: status code, redirect chain, content-type, and whether the body is HTML. Then I ask whether the main article is in the initial HTML or only after a client-side render. Then I inspect the main content, headings, paragraphs, tables, and only after that the structured data. Google's structured data overview is still the page I use to confirm how machines read types and entities on a URL.

If the HTML never contains the answer, markup will not invent it. I check that the extractable span exists in the source, not only in a screenshot of the live page. I also fetch the same path from a clean session, because a page that works in my logged-in browser is not the page Google fetched. Access, render, parse: I refuse to skip a step just because the design looks finished.

Clean URLs and HTML I can defend

I keep URLs short, stable, and readable because both classic ranking and extractability depend on a machine being able to name the page. I avoid parameter strings, session IDs, and duplicate paths that resolve to the same article. The slug should match the entity I already locked in Step 2, brand, category, product type, so the URL and the H1 do not argue with each other.

HTML I can defend means one H1, heading order that follows the outline, and paragraphs that do not hide the answer inside nested widgets. I strip leftover tracking attributes and empty containers that bloat the main content node. When I review a template, I ask whether a crawler that never runs our JavaScript still sees a complete answer. If the answer lives only in a tab that is not in the source, I move it into visible HTML. Clean markup keeps extractability and Search ranking on the same URL.

What I monitor after a deploy

After a deploy I re-fetch the live URL, not the staging host. I confirm the 200, the canonical, and that robots still allow the path. I check Google Search Console for coverage errors on that URL and on the template if we shipped a layout change. I render the page with JavaScript disabled and read the first screen aloud: if I cannot hear a direct answer, Gemini will not extract one either.

I also watch Core Web Vitals at the URL level for a few days, not because a single LCP regression proves we lost citations, but because a timeout can drop the page from the fetch path I just described. Indexation status, HTML parity with staging, and the structured data test are the three checks I refuse to skip. If any of those fail, I roll the template back before I touch copy.

Step 7: Measure the gap between rankings and citations

Ranking in Search and being cited in a Gemini-assisted answer are not the same event. I run a search-versus-citation pass so I can see which URLs Google already ranks that never appear as sources in the AI answer. That gap is the work. I do not guess at new topics until I know which ranked pages failed extraction. Semrush supplies the ranking side; prompt checks supply the Gemini side. Then I fill the missing span on purpose.

The search-versus-citation pass I run

I export the queries where we already rank on Google, I have used Semrush for that ranking side, and I line them up against the sources Gemini actually names for the same prompts. I run the prompts in a logged-out session and I record the named sources before I look at our own URL, so I do not talk myself into a citation that was not there. The interesting rows are the ones where we sit on page one in Search and never appear in the citation list. Those URLs passed crawl and ranking and still failed extraction.

I log the prompt, the ranked URL, the cited domains, and a one-line note on why we lost: no direct answer in the first screen, entity drift, a thin supporting page, or a fresher competitor. I do not treat a missing citation as a ranking problem if Search Console already shows impressions. I treat it as an extractability problem on a URL that is already eligible. I repeat the same prompts a week later so a one-off answer does not drive the backlog.

Filling gaps on purpose, not at random

I fill in a fixed order. First I add the missing answer: a direct span in the first screen that can stand alone. Second I repair entities: brand, category, product type, and audience strings that drifted from the rest of the cluster. Third I add the FAQ the prompt actually asks, in question-shaped headings, not a dump of related keywords. Then I re-run the same prompt set.

I do not publish a new URL to cover how to rank in Gemini if an existing ranked page can carry the span. New URLs are for cluster gaps I already mapped in Step 4, not for every missed citation. After the edit I wait for recrawl, then I check whether we moved from ranked-but-uncited to cited. If we did not, I look at the cited competitors' first paragraphs and I match the extract shape, not their wording. Random blog posts do not close this gap.

What I log so the next sprint is shorter

Each sprint I keep one sheet. The columns I actually fill are prompt, date checked, our ranked URL, Search position from the export, whether Gemini cited us, which domains it cited, the gap type, the edit I shipped, and the recrawl date. Gap type is one of answer, entity, FAQ, freshness, or technical. I add a last-checked date so I do not re-audit the same prompt twice in a week.

I also note which Google AI surface I tested, because a citation in one place is not a citation in another. The log is what keeps my work on how to rank in Gemini from resetting every month. Next sprint I sort by gap type, not by vanity prompts, and I only reopen URLs where the recrawl window has passed. I archive prompts that stay cited for two consecutive checks so the sheet does not grow forever.

Where gemini seo and classic SEO reinforce each other

I do not keep a Gemini backlog and an SEO backlog. The same pages that earn Search impressions are the pages I make extractable for how to rank in Gemini. Density, clean URLs, and FAQ blocks were classic on-page work; those pages also became the ones I could measure in AI answers. I once worked a program that reached 7 million organic impressions across Google and Bing; those URLs were the ones I later found easiest to get cited.

Density, clean URLs, and FAQs I still use

I still write a clear primary phrase in the title, H1, and first paragraph, then I stop. That is density as I practice it: enough for a parser to lock the topic, not a count I chase. Clean URLs stay human-readable and stable. FAQs are real questions from the prompt set and from Search Console queries, written as headings with a short answer underneath.

The 7 million organic impressions I saw on Google and Bing did not come from a Gemini-only playbook. They came from pages that were crawlable, on-topic, and easy to quote. Those same habits are what I now call Gemini SEO when a team asks for a separate tactic: the extract is cleaner because the classic on-page work already put a standalone answer on the page. I did not add a new content type. I made the existing type easier to reuse.

Why I do not run a separate gemini seo silo

I keep one backlog per URL. AEO tasks sit on the same tickets as SEO tasks: indexation, title, first-screen answer, FAQ, internal links, recrawl. If I split the work, designers ship a template that breaks the extract, and writers ship a Gemini draft that never gets indexed. One owner, one URL, one definition of done.

When a stakeholder asks for a Gemini SEO workstream, I show them the sheet from Step 7 instead. The rows already tell us which ranked pages need an answer span, an entity repair, or a freshness pass. That is AEO on top of SEO, not beside it. I still use the same Search Console and ranking exports I used before Gemini existed. The only new column is whether we were cited. Separate silos duplicate research and they let technical regressions hide in the AI lane. I would rather ship four extract edits on ranked URLs than open a parallel content calendar.

The reinforcement loop I keep running

Weekly I run the same loop. Index: is the URL crawlable and in Google? Extractability: is there a direct answer in the first screen, with entities that match the rest of the cluster? Authority: does the hub still cover the subject, or did we ship a thin page? Freshness: only if the query cares about now. Measure: search-versus-citation pass, log the gap, fill it, recrawl.

I repeat that on the same URLs. I do not restart from a blank keyword list every Monday. This is the playbook I hand a team that asks how to rank in Gemini: Google eligibility first, then structured data, answer-first copy, topical coverage, selective freshness, technical access, and a measurement loop I can run again. Classic SEO and this work share the fetch path. If the page cannot be accessed, parsed, and evaluated, none of the later tactics matter.

Frequently asked

How to rank in Gemini if my site already ranks on Google?

Gemini sits inside Google’s AI ecosystem, so strong Search pages are more likely to be eligible for citation. I still treat crawlability, entity clarity, and answer-first writing as the next layer. Ranking on Google is a starting point, not a citation. I then check topical authority and consistent brand, category, and audience statements.

Is gemini seo a separate discipline from on-page SEO?

I do not treat Gemini SEO as a separate discipline. On-page work still matters because Google systems need to access, parse, and evaluate the page first. I layer answer-first writing, entity clarity, and topical authority on top of crawlable, indexed content. Structured data helps interpretation; it does not replace technical quality or clear page copy.

Does structured data guarantee a Gemini citation?

No. Structured data helps Google understand page entities and content types, which supports clearer machine interpretation. I still need crawlable, indexed pages, technical quality, and topical authority before a citation is even possible. I use schema as a clarity layer, not a citation switch. Answer-first writing and consistent entity statements matter as much.

How often should I refresh a page for Gemini visibility?

I refresh when the query is time-sensitive, not on a fixed calendar. Recently updated pages are easier to present as current for those queries. For stable topics I update when facts, entities, or answers change. Freshness is a lever, not a ranking schedule. I still keep crawlability and entity clarity intact after every edit.

How can I tell that Gemini is using my pages?

I look for my URL or brand appearing as a cited source inside Gemini answers for the queries I care about. Ranking on Google is not proof of reuse. I compare those citations against crawlable, indexed pages with clear entities. I treat a visible citation as the signal, not Search position or structured data alone.

About the author

Furkan Yaman — AEO & GEO practitioner

Furkan Yaman is an AEO and GEO practitioner. He has grown AI citations 200% quarter over quarter, taken a client from 0% to over 70% AI visibility in three months, and built AI Rank Checker to measure brand visibility across ChatGPT, Perplexity, Google AI, Grok and Claude. Rankus AI is where he documents what works.

How to Rank in Gemini (2026 Guide)