STAGE30TMtrademark
Questions
Plain answers to what people actually ask us. Where there is a number in an answer, it carries where it came from and when it was measured. Where the honest answer is that it depends, the answer says what it depends on.
Get the claim onto pages you do not own, and make sure OAI-SearchBot can read yours. ChatGPT gets its search results from Bing, so a page Bing has not indexed cannot reach it. Then it reaches for the claim that many independent sites repeat. One page on your own site saying you are the best is one source. The same fact on Wikipedia, Wikidata, a trade publication and a review platform is a consensus, and consensus is what it quotes.
An engine picks the brand it can name, source and repeat without hedging. When the only material about a brand sits on the brand’s own site, the engine has little to retell. The Stage30™ check shows which sources the engines used and where the brand is missing from them.
ChatGPT found more about the competitor that it could check. An engine builds a recommendation from the sources it can reach. The competitor shows up in the places the engine reads, and the brand shows up mainly on its own site. The Stage30™ check shows which sources the engine pulled the competitor from, and whether the brand appears in any of them.
You can change what it has to work with, which changes the answer. You cannot type the answer yourself. The practical work is making the true things about you verifiable in more than one place, structured so a machine does not have to infer them, and answering your buyers’ real questions in public where a crawler can reach the answer.
It depends which engine, and the spread is wide. Perplexity crawls your pages itself, so work on your own site can surface quickly. ChatGPT waits on Bing. Gemini is the slowest of the four, because it starts from the opinion Google has built about your domain over years. Anyone promising all four on the same schedule has not looked at how they differ. Set a baseline before you start, or you will have no way to say what the work did.
AI search optimization is the work of making a brand the name an AI engine returns when a buyer asks a question in its category. The unit of the work is a claim about the brand, repeated across enough independent and machine readable sources that an engine can state it without hedging. In practice that is three things: structured entity data on your own site, the same facts verifiable on sources you do not control, and answers to your buyers’ real questions published where a crawler can reach them. Stage30™ measures the result as a Resolution Score out of 100, and the free check shows which of the three is holding you back.
AI search visibility is whether an AI engine can find, verify and name your brand when it answers a question in your category. It has a floor and a ceiling. The floor is whether the engine can read you at all, which depends on crawler access and on whether your pages carry text a machine can quote. The ceiling is whether the engine names you first when several brands qualify. Most brands that check assume they have a ceiling problem and find they have a floor problem.
Ask the engines the question your buyer would ask, record the whole answer, and count whose name comes back. That is the measurement, and everything else is inference. A Stage30™ Brand Resolution Check puts your buyer’s question to ChatGPT, Perplexity and Claude live, quotes each answer word for word, lists the sources each engine pulled from, and scores the result out of 100. Run it twice on different days before you trust a single number, because the engines move. Every reading Stage30™ publishes carries the date and time it was taken, for that reason.
SEO and AI search SEO share a toolkit and answer different questions. SEO earns a position on a list of links, and the buyer still chooses from the list. AI search SEO decides which single brand the engine names when it writes the answer, so the list never reaches the buyer. The overlap is real: crawlability, clean markup, fast pages and credible links matter to both. The divergence is that an engine reads the whole web’s account of you rather than your site’s account of itself, so work that never touches your own pages, such as a corrected Wikidata entry or a trade citation, can move the answer further than a homepage rewrite.
Any brand whose buyers open with a question rather than with a brand name. The test takes a minute: type your buyer’s actual question into ChatGPT and see whose name comes back. If a competitor’s name comes back, the engine has settled your category already and you are outside the decision. If your own name comes back hedged, wrapped in ’options include’ or ’you may want to consider’, you are on the list and the answer belongs to someone else. Considered purchases, professional services and B2B feel this first, because those buyers ask the most questions before they ever type a brand name.
Change what it reads, then measure whether the answer changed. There is no correction button and no support ticket for a brand fact. An engine builds each answer in the moment from sources it can reach, so a wrong answer is usually a wrong or missing source, repeated. Find the pages the engine actually cited, correct the ones you control, get the true version onto the ones you do not, then ask the question again on a date you write down. It is a measurement problem before it is a content problem.
There is no meaningful correction channel for a brand fact. You can report safety problems, and you can control crawling through robots.txt, and neither of those edits an answer. The leverage sits in the sources, so the first job is knowing which sources the engine used. The free check names them.
Ask ChatGPT the buyer’s question more than once and write down what came back. One ask is an anecdote. The engines build each answer fresh, so the same question can name a brand on Tuesday and skip it on Wednesday. In one Stage30™ study, the same question about one brand ran 35 times on dated runs and read anywhere from 32 to 70. That’s why Stage30™ reports a range as well as a score, and dates every reading.
A reading, from 0 to 100, of how confidently an AI engine can pick your brand as the answer. It has two halves. One moves with how many engines named you on that run, and it changes between runs. The other comes off your own pages and holds still. That second half is the part any work you do actually moves, which is why Stage30™ reports both.
Because AI keeps no standing ranking. Each answer is built in the moment, and it can land differently for another person on the same day. Stage30™ has measured one site 35 times on one question and seen it read anywhere between 32 and 70. Every reading it publishes is dated to the minute for that reason, and one run should never be read as your brand.
More than once, and the vendor should tell you the number. On the site Stage30™ has measured most, 35 readings of one question over two weeks ranged from 32 to 70. A single run sitting inside a spread that wide tells you very little on its own. What is worth reading is the floor: the part of the score that comes off your own pages and holds still between runs. Stage30™ reports both halves for that reason, and prints the run count and the timestamp on every reading.
They asked different questions, at different times, from different places, and most do not publish how the score is built. Two numbers built that differently cannot be compared, and averaging them produces a third number that means less than either. Read a trend inside one consistent method and ignore the gap between tools. If a tool will not show you the question set, the run count and the date, the score is decoration.
Read the referral traffic you can see, and label the rest unmeasured. Most AI answers are read without a click, so the referrals in your analytics are a floor rather than a total. Anyone handing you a complete attribution model for AI answers has built it out of assumptions. The part that can be measured honestly is whether you are named, on which question, by which engine, on what date.
No, and the difference is the list. SEO competes for a spot on a page of links, where the buyer still chooses. The engines return one answer, often with no list at all, and the brands named inside that answer are the whole field. A site can rank first on Google and go unnamed by the engines, and Stage30™ sees it on real runs.
Yes. Most engines start from pages a search system has already found, and Google says the same SEO basics apply to its AI answers. Google’s AI answers run many related searches behind one question, so a page that answers a narrow question can make the cut even when it would never rank for the broad term. The old tricks stopped paying years ago: Google now discounts bought links and filters out mass-produced pages. The work that holds up is the same for Google and the engines: a specific page, a consistent message, and other sites that confirm it. Milk Sandwich, an apparel brand Cast Iron LA incubated, moved from an average Google position of about 13 to about 7 after its Stage30™ build. (Google Search Console, February to May 2026.)
GEO, generative engine optimization, aims to get your content quoted inside an AI answer, as one source among several. Stage30™ works on being the brand the engine names. Getting cited is useful. Being the answer is the outcome that produces a customer.
Sometimes, because each one reads a different copy of the web. ChatGPT reads Bing, Gemini reads Google, Claude reads Brave, and Perplexity reads its own. Each copy holds a different version of your brand, so one question can come back four ways. When they do agree, that agreement is worth having. There is more on this, engine by engine, on the engines page.
Block the ones that only take, and allow the ones that can send you a customer. GPTBot feeds OpenAI’s model training. OAI-SearchBot is what decides whether you can appear in a ChatGPT answer. Blocking GPTBot keeps you out of training and leaves you findable. Blocking OAI-SearchBot makes you invisible to the largest AI engine there is. The same distinction matters at Google: Google-Extended governs AI use, while Googlebot is what actually crawls, so blocking Googlebot to stay out of AI takes you out of Google Search with it.
One decides whether you can appear in a ChatGPT answer, the other feeds model training. OAI-SearchBot surfaces sites in ChatGPT’s search features. GPTBot crawls content that may be used to train OpenAI’s foundation models. ChatGPT-User is a third agent, which fetches a page when a person asks for it directly. Blocking GPTBot keeps you out of training and leaves you findable. Blocking OAI-SearchBot makes you unfindable in the largest AI engine there is, which is the expensive version of the same decision.
No. Google-Extended has no user agent of its own and never requests a page. Googlebot does the crawling, and Google-Extended is a robots.txt product token controlling whether that crawl may be used for Gemini and other Google AI products. Google states it does not affect a site’s inclusion or ranking in Google Search. The practical consequence: blocking Googlebot to stay out of AI takes you out of Google Search with it, and Google-Extended is the control that separates the two.
It is a plain text file at the root of your site that lists your important pages in language a model can read, in the way robots.txt lists rules for crawlers. It is cheap to publish and it will not rescue a site that has nothing readable on it. Publish one after your pages say something worth pointing at, not before.
It makes your facts unambiguous, which is worth doing, and no engine has published that it is a citation factor. Structured data states in code what your page states in prose: who you are, what you sell, what it costs, who wrote it. A model that would otherwise have to infer those things does not have to. Treat it as removing guesswork rather than as a lever with a promised return, and be careful with anyone selling it as the latter.
Yes. Stage30™ writes a buyer’s question from the brand’s own page, puts it to three engines live, and quotes each answer in full. The check names who came back, links the sources the engines read and prices the two biggest fixes.
The Stage30™ audit is $500, once. The audit opens every finding the check counted, prices each fix and orders the fixes by their expected effect on the answer. The plan is the brand’s to keep.
No. There is no placement to buy inside the answer itself. Paid units, where an engine runs them, are a separate surface and should never be reported to you as visibility. What money buys is the work: getting the true claim onto sources the engine reads, and making your own pages readable to the crawlers that decide.
Stage30™ does not sell a monthly retainer. The report is free, the Resolution Audit is $500 once, and build work is quoted against the fixes your own run found. Agencies in this space commonly price monthly and the range is wide. Before you sign one, ask for the question set, the run count and the dates behind whatever number they showed you. If they cannot produce those three things, you are buying a dashboard.
Cast Iron LA, an advertising and AI consulting company in Los Angeles. The methodology is by John Otto Barbush and Luis Emilio Ramirez. Stage30™ is a U.S. trademark application, serial no. 99719523, published for opposition 1 September 2026.
Because Stage30™ measured the noise first. Ask the same engine the same question about the same unchanged site three times and the answers differ. On one brand in our own corpus the score read 51, 66 and 51 across three runs six minutes apart, with every page-side score identical and nothing on the site changed. So a Stage30™ reading is the median of three runs rather than a single draw, and a movement is only reported when it is larger than what your own record moves by on its own. The range your score has actually occupied travels with every number, so you can see the weather rather than take our word for the forecast.
The Stage30™ canon is the rulebook behind every check and audit: 707 rules across 23 documents. Each rule was written after a real page, on a real run, went wrong. 506 of the rules are enforced in code, and 2,029 tests run on the engine every time it changes. Every number in a report ships with its source and its date, or it doesn’t ship.
Every claim Stage30™ prints carries one of four labels. Observed means we ran it, and we name the engine, the question and the date. Reported means somebody else measured it, and we name who and when. Inferred means it’s our read, and we say so. To be tested means a dated hypothesis: change the page and ask again. A second AI model, one that didn’t write the document, reads every Stage30™ document before it ships.
No. The engines write each answer fresh, and nobody controls them. Nobody knows exactly what an engine will do next, so Stage30™ asks, writes down what came back, changes one thing, and asks again in front of the client.
Enter your site and we put your buyer’s question to ChatGPT, Perplexity and Claude, live. The check is free and takes about a minute.
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