How to get your business found in AI search

To get your business found in AI search, you need to be in the sources these engines retrieve from and then make your content easy for them to quote. In practice that means five things done together: rank and get indexed for the real questions your buyers ask, structure each page so the direct answer sits in the first two or three sentences, earn genuine mentions and reviews on the third party sites AI engines trust (Reddit, review platforms, best of roundups), keep your facts consistent everywhere a machine can read them, and allow the AI crawlers (such as OAI SearchBot and PerplexityBot) to fetch your pages. Above all, measure first: ask the real engines the questions your buyers ask, score whether you show up, fix the biggest gap, then measure again. You cannot improve what you cannot see.

Right now, somewhere, a buyer is asking an AI assistant to recommend a business like yours. They will read the answer, note the two or three names it offers, and act on them. The only question that matters for you is whether your name was in that answer. If it was not, you were cut from the shortlist before the buyer ever thought to visit your site.

This is not a trend waiting to arrive. Hundreds of millions of people now use AI assistants every week, most Google searches end without anyone clicking a result, and the majority of business buyers lean on AI before they ever speak to a seller. Your buyers are building their shortlist inside these tools, often long before they reach your sales team.

Here is the uncomfortable part. You can see every Google ranking you hold, yet you cannot see a single one of the AI answers that now decide whether buyers reach you. This guide closes that gap. It covers what AI search is, how engines choose who to recommend, a measure first method anyone can run, engine by engine notes, and a quick start plan you can begin this week. One caution runs throughout: many of the statistics circulating about AI search come from vendors and PR firms and disagree with each other, so treat the figures as directional and lead with what you can measure for yourself.

What is AI search and why does it matter for your business now?

AI search is when a buyer asks an assistant like ChatGPT, Gemini, Perplexity or Google AI a question and gets one synthesised answer, often naming a few specific businesses, instead of a page of blue links. It matters now because that behaviour has gone mainstream, and the answer, not the link, has become the new front page. If your business is not inside that answer, you are cut from the shortlist before the buyer ever reaches your site.

You will meet this across several distinct surfaces, not one. ChatGPT, Google AI Overviews (the summary that sits above the classic results), Google AI Mode (a fuller conversational version of Search), Gemini, Perplexity and Microsoft Copilot each build their answers differently and cite very different sources. What satisfies one may be ignored by another, so think of AI search as a set of channels that happen to look alike, rather than a single place to win.

The scale is already here, not coming. Google’s AI Overviews reached around 2 billion monthly users in 2025, up from 1.5 billion earlier in the year, and ChatGPT passed more than 700 million weekly active users by mid 2025. Those are not early adopter numbers; they describe a mass market discovery channel that your buyers are using today.

That shift changes what ranking is worth. Reported clickstream data put the share of US Google searches that end without a click at roughly two thirds in early 2026 (treat this as directional), and Pew Research Center found that when an AI summary appeared, people clicked a traditional result only 8 percent of the time, against 15 percent when no summary was shown, across about 68,000 searches in 2025. The lesson holds either way: ranking well is no longer enough on its own. You need to be mentioned and quoted inside the answer itself.

Buying has also moved upstream. In Forrester’s 2026 Buyers’ Journey survey of around 18,000 business buyers, 94 percent said they used AI during their most recent purchase, and 55 percent said they compare vendors inside AI tools before contacting a seller. By the time someone speaks to your sales team, the AI has often already drawn up the shortlist. This is sharpest if you sell software or run an online store: see AI visibility for SaaS and AI visibility for ecommerce.

The traffic itself is still modest. Semrush reported that ChatGPT referrals to US websites grew roughly 200 percent year on year into early 2026, although AI referrals remain a small share of total web traffic. So treat this as low volume and high value: fewer visits, but ones that arrive already informed and ready to act.

One note on scope before we go further. AI search optimisation overlaps heavily with two adjacent ideas: what generative engine optimisation means on the generative side, and answer engine optimisation explained on the answer engines. You can go deeper on both in the GEO pillar; this guide stays practical and keeps your business, not the jargon, at the centre.

How do AI engines decide which businesses to recommend?

AI engines do not invent sources. They retrieve passages from a live web index they trust, then summarise and attribute them, so being recommended comes down to two things: being in the candidate pool at all, and being the clearest, best evidenced passage for the exact question asked. The signals that recur are retrievability, a resolvable brand identity, quotable answer first content, and corroboration from trusted third party sites.

Retrieval comes before synthesis. If you are not indexed, or not ranking for the sub question being asked, you cannot be selected, full stop. Classic SEO is therefore the entry ticket, not an alternative to AI search, and the rest of this guide assumes you have cleared it.

Then there is entity clarity. Engines resolve your brand name to a known entity before they trust it, which is why consistent profiles matter: a complete, accurate Google Business Profile, a Wikidata entry, matching details across the directories you appear in, and an honest Wikipedia presence only if you genuinely qualify for one. The aim is simply that the model knows who you are and does not confuse you with someone else.

Passage level extraction is the next lever. Models lift short, self contained answers out of pages, so the way you structure content decides whether you get quoted at all. A buried, hedging paragraph rarely makes the cut; a crisp two sentence answer sitting under a clear heading often does.

Corroboration tends to beat self promotion. Analyses of AI citations report that most cited content sits on third party earned sites rather than brand owned pages, with Reddit among the most cited domains. Treat that as directional, but the direction is clear: what others say about you on sites engines already trust often carries more weight than what you say about yourself.

Finally, query fan out. Google has confirmed that AI Overviews and AI Mode may break one question into several searches and pick the best passage for each sub query, so covering the whole question space, not just your single money keyword, is what gets you surfaced. You can read more on the signals engines use to choose brands.

A caution runs through all of this: many of these percentages come from vendors and PR firms, and they disagree with each other. Treat the figures as directional. The direction of the signals is robust; the exact numbers often conflict.

Is getting found in AI search different from SEO?

Getting found in AI search is an extension of SEO, not a replacement for it. Everything that makes you crawlable, indexed and ranked still decides whether you are even in the pool an AI engine can draw from, so strong technical SEO and genuinely useful content remain the foundation. What is new is a layer on top: writing for passage level extraction, earning mentions on the sites engines cite, and measuring whether engines actually name you, because rankings alone no longer tell you that.

Start with the overlap, because it is large. Indexability, page speed, a sensible information architecture, internal linking, topical depth and matching the intent behind a query all feed AI retrieval exactly as they feed ordinary ranking. As set out above, engines retrieve from the index and then synthesise, so none of this groundwork becomes optional; if anything it matters more now.

The layer AI search adds is specific. You write answer first so a model can lift a clean passage. You keep your entity consistent across profiles so the engine trusts who you are. You earn third party corroboration on the sites that actually get cited. And you make sure retrieval bots can reach your pages, which is a crawler access question sitting on top of the usual indexing one.

Google’s own position reinforces this. The company has said plainly that answer engine optimisation and generative engine optimisation are still SEO: a page has to be indexed and snippet eligible before it can appear in any AI feature at all. The biggest mistake is to treat GEO as a separate discipline you do instead of SEO, when it sits on top of it. You can see the full comparison in GEO versus SEO in full and a closer look at what answer engine optimisation covers.

What genuinely changes is measurement. Search Console does not report whether an AI answer mentioned you, so you need a new way to see it: asking the real engines the questions your buyers ask and checking whether your business is named in the reply. That is the gap the rest of this guide, and tools like ONEGO, exist to close.

How to get your business found in AI search: a step by step method

Work the loop in order rather than doing everything at once: measure where you stand, understand why, fix the highest impact gap, then measure again. Start by confirming you are retrievable and ranking, then make your content answer first, then earn corroboration and keep your facts consistent, and manage crawler access so the engines can actually fetch you. Prioritisation is the whole point, because effort spent where engines do not look is wasted.

Win the organic ranking first. As covered above, engines retrieve from the index before they synthesise, so a page that is crawlable, fast and well structured, targeting the specific sub questions your buyers ask, is the entry ticket to the candidate pool. If you are not in that pool for a query, no amount of clever phrasing will put you in the answer.

Next, structure each page answer first. Make every H2 a real question or a plain claim, then put a complete answer in the opening 40 to 60 words so an engine can lift it whole. Follow with short paragraphs, scannable lists and a summary near the top. Engines reward pages they can parse and quote without hunting.

Then add evidence to every claim. Attach a specific sourced statistic or a named quote, and cite the primary source rather than a blog that cites a blog. The Princeton Generative Engine Optimization study (2024) found that adding statistics, quotations and cited sources increased how often generative engines included a page, though the size of the lift varied by topic. Treat that as a reason to be concrete, not as a magic formula.

Earn third party corroboration. Participate genuinely in relevant Reddit and Quora threads, pursue honest reviews, and work your way into credible best of roundups through real outreach rather than placement you have paid for. Keep your facts consistent across your site, directories, profiles and social accounts, and for a local business fix any inconsistent name, address and phone so the engines resolve you as one entity.

Manage crawler access deliberately. Allow OAI SearchBot, PerplexityBot, Google Extended, Applebot Extended and ClaudeBot in robots.txt, then check that your firewall or CDN is not silently blocking them, because a bot family that cannot fetch you cannot cite you. Finally, add Organization schema with a stable identifier and sameAs links as low cost groundwork for entity clarity, while remembering that schema alone does not win citations.

Run this as a loop, not as a one off launch. For the full ordered version you can tick through, follow the ordered get recommended checklist and repeat it as your measurements move.

Where to show up across ChatGPT, Gemini, Perplexity and Google

Each engine cites different sources, so treat them as five surfaces rather than one. ChatGPT leans on encyclopaedic and third party sources and discovers pages by links, Gemini and Google’s AI surfaces pull from Google’s index and Google Business Profile, Perplexity rewards freshness and live retrieval, and all of them favour pages that are clearly structured and widely corroborated. Use the shared method above, then follow the specific notes for the engine that matters most to your buyers.

For ChatGPT, entity clarity and encyclopaedic presence help disproportionately, and OAI SearchBot must be allowed to fetch you. For Google AI Overviews and AI Mode, the levers are indexing, snippet eligibility and covering the sub questions behind a query, with Google Extended governing Gemini and training access separately from normal indexing. Gemini relies strongly on Google signals and your Google Business Profile, so local and entity data carry real weight. Perplexity cares more about freshness and recency, so show clear dates and genuinely refresh your cornerstone pages. Bing indexing underpins Copilot grounding, so Bing Webmaster Tools and IndexNow are a sensible low cost hedge.

Where the main AI engines pull their sources, which crawler to allow, and what wins on each surface

Name the remaining surfaces once so the piece is complete: Microsoft Copilot is grounded in Bing, Apple Intelligence and Siri crawl with Applebot (Applebot Extended is the token that controls whether your content feeds their generative features), and xAI’s Grok, Meta AI and DeepSeek each run their own retrieval. The method is the same across all of them; only the sources and crawlers differ. For the detailed play on each surface, see how to show up in ChatGPT, how to get mentioned in Gemini answers, how to rank on Perplexity and how to appear in Google AI Overviews.

How do you measure whether AI mentions your business?

You measure by asking the real engines the questions your buyers actually ask and recording whether and how your brand appears, then watching that pattern over time because the answers vary from one run to the next. The honest version is a loop: define the buyer questions, run them across ChatGPT, Gemini, Perplexity and Google AI, score whether you show up, work out why, fix the biggest gap, and measure again. A single check is noise, so you follow the trend rather than any one reading.

Start manually if you have to. Keep a weekly log of your top ten target prompts, run each one across the main engines, and note whether your business appears, where it sits, and who turns up instead. Accept from the outset that results are volatile: the same question can name you on Monday and skip you on Thursday, which is exactly why the weekly cadence and the running record matter more than any single screenshot.

The real edge is breadth. Most tools look at Google only, or at two or three chat engines, yet your buyers move freely between all of them. Measuring across five engines (ChatGPT, Gemini, Perplexity, Google AI Mode and Google AI Overviews) is what tells you whether you are invisible everywhere or simply weak on one surface, and those are very different problems with very different fixes.

This is where ONEGO fits. It asks the real engines the questions your buyers ask, scores whether you show up with the ONE Score from 0 to 100, explains the reasoning behind that score, and hands you a prioritised fix list. See how the ONE Score measures AI visibility for the detail. A single understandable number with the reasoning attached beats a bare share of voice figure, because it tells you what to do next rather than simply how you feel. The built in competitor leaderboard takes that further, turning share of voice into action by naming exactly which businesses the engines recommend in your place.

Two habits keep the picture honest. First, refresh weekly, because citations churn and the sources an engine leans on this month may drop out the next. Second, segment AI referral traffic inside your own analytics, since it often hides as organic, branded or direct and you will undercount it otherwise. If you want a baseline before committing to anything, you can run a fast five minute check by hand, or start a free AI audit, which begins a 3 day free trial you can cancel anytime, and see where you stand across all five engines.

What should a local or small business do with no marketing team?

Focus on the handful of things that move AI answers for local and service businesses, and ignore the enterprise noise. Complete and polish your Google Business Profile, make your name, address and phone identical everywhere they appear, gather genuine reviews on the platforms your sector actually uses, and write plain, answer first pages for the questions customers really ask. These fundamentals feed Gemini and Google AI surfaces directly, and you can start on every one of them without a paid stack.

Begin with the Google Business Profile, because Google AI surfaces and Gemini pull from it heavily. Fill in every field: categories, services, hours, service area, photos and a genuine description. A thin or half complete profile gives the engines little to work with, and they will favour a rival who has done the work.

Next, make your name, address and phone consistent across your website, the directories you are listed in and every profile you own. When those details conflict, engines hedge or quietly pick the business with the cleaner, more corroborated data, so a mismatched suite number or an old mobile can cost you the answer.

Then earn genuine reviews on the platforms that matter for your category, whether that is Google, a trade directory or a sector specific site. Consensus across the web weighs heavily, and a model reading a dozen consistent reviews will state your strengths with far more confidence than it will for a business with none. Pair that with a clear About page and a set of plain answers to the real questions local buyers ask: what you do, where you work, what it costs, how quickly you can come out. Write each one answer first, so the useful sentence sits at the top where an engine can lift it cleanly.

Finally, do not talk yourself out of it on the grounds that you are too small. Unmet demand is high and the competition for the definitive local answer is still thin, so an ordinary local business that gets these fundamentals right now has a real chance to become the name engines reach for. AI visibility for local businesses covers the specifics for your sector.

Why does your competitor show up in AI answers when you do not?

Usually because the engine finds cleaner, better corroborated signals for them. They rank or get cited for the exact sub question your buyer asked, their brand is mentioned and reviewed on the third party sites engines trust, and their facts line up across the web. The model then states those facts with confidence and skips the business whose signals are missing or contradictory. The fix is to see which questions they win, understand the gap, and close it, rather than guess at what the machine is thinking.

Start by reading a competitor leaderboard, so you can see who the engines actually name for your buyers’ questions and how often each rival turns up. That alone usually dissolves the mystery, because the pattern is rarely random: the same two or three names recur on the questions where you are absent.

The reasons a rival wins tend to be ordinary. They hold a stronger ranking for the sub query, so they sit in the index the engine retrieves from. They have earned more mentions on sites beyond their own, which matters because analyses of AI citations suggest most cited content sits on third party earned sites rather than brand owned pages, with Reddit among the most cited domains (treat that as directional). Their entity data is clearer, or their content is simply fresher. Often it is a combination, and the leaderboard shows you which one is doing the damage.

From there, two moves do most of the work. Find the listicles and roundups the engines already cite for your category, the “best in your city” and “top tools for” pages, and earn a genuine place in them rather than publishing your own and hoping. And benchmark on the exact questions your buyers ask, not vanity terms, so the gap you are measuring is real and worth closing. This holds as much for an agency managing many clients, where AI visibility for agencies and the agency playbook go deeper, as it does for a plumber: the engines reward the same signals everywhere.

The point of all this is restraint. You will not match a strong competitor on every front at once, and you do not need to. Turn the gap into a prioritised fix list, take the highest leverage item first, and work down it. Why a competitor shows up and you do not walks through the diagnosis in more detail.

Which common mistakes keep businesses invisible in AI search?

The costly mistakes are nearly all avoidable: treating generative engine optimisation as something separate from SEO, expecting schema or an llms.txt file to win citations on their own, accidentally blocking the retrieval crawlers, chasing one platform or one quirk, and letting your facts drift out of sync across the web. Burying the answer below a long preamble belongs on the list too, because it stops a model lifting a clean, quotable passage.

The first trap is treating GEO as a replacement for SEO rather than a layer on top of it. As established, engines retrieve from the index before they synthesise, so if you are not crawlable and ranked you are simply not in the pool they draw from. Everything else in this guide assumes you have cleared that entry ticket first.

Then come the shortcuts people hope will do the work for them. An llms.txt file is the clearest example: Google has said repeatedly that it does not use the file, and no major engine has confirmed it as a retrieval signal, so adding one neither helps nor harms. Schema is a subtler version of the same mistake. It is worth having for clarity, but one analysis by Search Atlas found no clear correlation between schema coverage and how often a page was cited, so treat structured data as groundwork that helps machines read you, not a lever that wins citations on its own.

Blocking the wrong crawler is the mistake that quietly removes you from the results entirely, and the names matter because the crawlers do different jobs. GPTBot is largely about training data collection, while OAI SearchBot is the one that powers citations inside ChatGPT search, so blanket blocking anything labelled OpenAI can cut you out of the surface you actually want. Google Extended governs whether your content feeds Gemini, and it sits separately from the Googlebot that handles normal indexing, so a rule meant to keep you out of one can accidentally close the other. Check your robots.txt against each of these by name before you assume you are being ignored on merit.

A different kind of mistake is trying to buy the signal. Paid mention packages and fake Reddit posts are detectable, they risk platform bans and brand damage, and they miss the point entirely: the thing engines are reading is genuine discussion, and manufactured discussion does not read as genuine for long. Earn the mentions instead, as the get recommended checklist sets out.

The last two mistakes are about the content itself. Burying your answer below a long preamble pushes the engine to quote a competitor who gets to the point faster. And letting your facts conflict across the web, where your opening hours, your address or your product details say one thing on your site and another on a directory, gives the model a reason to trust a cleaner, more consistent source over you. Finally, be sceptical of the numbers you read about all of this. Many circulating GEO statistics come from tools and PR firms with a commercial interest in the answer, and they frequently disagree with each other, so lead with what you can measure for yourself rather than what a vendor chart claims.

What changes in AI search, and how fast?

AI visibility is volatile and it decays, so it needs monitoring rather than a single audit you file away. Citation sources swing sharply as models update, a strong ranking no longer guarantees a citation the way it once did, and a single engine can change who it favours within weeks. That is exactly why weekly tracking beats a set and forget approach, and why you optimise for durable quality and corroboration rather than a temporary pattern in one engine.

Start with how much churns. A single model update can replace a large portion of the domains an engine was citing last month, which means a result you see today is not a result you can count on next month. The practical consequence is that a win is never banked. You confirm it, then you keep watching it.

Ranking in the top ten is also a weaker guarantee of a citation than it used to be. It still helps, but passage relevance and coverage of the specific sub question now carry more of the weight, so a page that answers the exact thing being asked can be pulled in ahead of a higher ranking page that only covers the topic broadly. Reporting also suggests AI Overviews and AI Mode tend to cite overlapping but not identical sets of URLs, so the way to show up in both is to cover the sub questions a buyer actually asks rather than optimising only for the head term.

The sourcing behind ChatGPT search has shifted over time and is not fully disclosed, which is a good reason to hedge across engines rather than bet your visibility on one pipeline you cannot see inside. The broader forecasts move too: Gartner predicted that traditional search volume could fall by around a quarter by 2026 as people move to AI assistants, and while that full drop has not landed and Google still handles the large majority of searches, the direction is clear enough to plan around even if the exact figure is not.

None of this means the ground is impossible to stand on. The parts that hold across engines and across updates are the unglamorous ones: general quality, clarity, and corroboration from sources other than you. Optimise for those, measure weekly so you catch a drop in the week it happens rather than the quarter it happens, and you can see how to measure AI visibility over time for the mechanics of tracking it.

Your 30, 60 and 90 day plan to get found in AI search

Start with measurement, then fix the foundations, then compound with corroboration. In the first 30 days you find out whether engines name you at all and clear the obvious blockers. In days 30 to 60 you restructure your priority content and tighten your entity signals. In days 60 to 90 you earn mentions, cover the full question space, and lock in weekly tracking so the gains hold. This section is about sequence, not technique: the how of each lever lives earlier in this guide, and the point here is deciding what to do when.

Days 1 to 30: measure and unblock

Begin by measuring your ONE Score across the five engines, so you know your starting position in ChatGPT, Gemini, Perplexity, Google AI Mode and Google AI Overviews rather than guessing. Confirm the retrieval crawlers are allowed, fix any indexation problems, and resolve the inconsistent facts that undermine you. Then read your competitor leaderboard to see who the engines name instead of you and on which questions, which tells you where the gap actually is.

Days 30 to 60: restructure and clarify

With the blockers cleared, rewrite your priority pages answer first, leading with the quotable response and supporting it with sourced statistics and quotes. Complete your Organization schema and your Google Business Profile, and align your About page and product pages so they read as the canonical source of truth about your business. The aim is that whatever an engine retrieves about you says the same thing.

Days 60 to 90: earn and cover

Now compound it. Pursue genuine reviews and third party mentions, build topic clusters that cover the sub questions around your core offer, and work your way into the roundups and comparison pieces the engines already cite. This is the slowest part to move, which is why it comes after the foundations are solid.

Ongoing

Track weekly, remeasure after each fix, and tie every change back to a movement in the score, so you learn which efforts pay. The single most important rule across the whole plan is to pick the highest impact gap first rather than attempting everything at once, because prioritisation is what makes a plan like this work rather than stall. You can see how ONEGO works for the measurement side, and follow the ordered checklist to work through for the full sequence.