When a Boulder homeowner opens ChatGPT and types "best foundation repair company near me," something happens that most local business owners have never thought about. The model does not browse the way a person does. It pulls a small set of sources it already trusts, reads what they say, and assembles an answer. The business that gets recommended is rarely the one with the prettiest website. It is the one that shows up in the sources the model decided to trust.

We spend our days getting Front Range businesses cited inside those answers, so we read the new research on this closely. A recent Siege Media study of bottom-of-funnel buying prompts is the clearest picture we have seen yet of what AI actually leans on at the moment of a recommendation. The findings reorder a lot of conventional local SEO advice, and they open a real opening for businesses on the Front Range that are willing to do the work.

The headline number: brand websites barely get a vote

Siege Media analyzed roughly 1,000 high-intent buying prompts and 57,095 total citations across ChatGPT, Perplexity, Gemini, and Google's AI experiences. The pattern they found is hard to overstate. Across industries, somewhere between 80 and 95 percent of the citations behind AI buying advice came from third-party sources rather than the brand's own website.¹

Read that again, because it changes the job. When AI recommends a product or a service, the brand's homepage, its services page, and its carefully optimized landing page are mostly sitting on the bench. The model is quoting review sites, community discussions, comparison articles, and publishers. Most tracked brands sat in low single-digit citation visibility on their own behalf.¹

This is the opposite of how a lot of owners think about their website. They pour money into the site, then assume the site is what AI reads. The site still matters, and we will come back to why. But the evidence says the moment of recommendation is decided largely off your own property.

Reddit is the loudest voice in the room

The single most-cited domain in the Siege dataset was Reddit. It appeared in roughly 62 percent of bottom-of-funnel AI responses, more than all the traditional publishers combined.¹ For comparison, YouTube showed up in about 25 percent of responses and G2 in around 5 percent, while Forbes landed near 7 percent and CNBC and Business Insider near 3 percent each.¹

There is a reason for this, and it is worth understanding rather than gaming. When a buyer asks an AI tool to compare options, pick an alternative, or name the best choice for a specific situation, the model has to weigh credibility and choose supporting evidence. In those judgment-heavy moments, lived experience and community consensus tend to win over polished marketing copy. The models reach for the places where real people described real outcomes.

A second Siege study adds an important caution to this. They analyzed 802 Reddit posts with an AI-detection tool and found that professional and tech communities are increasingly full of AI-generated content, while everyday communities still run on firsthand human experience. Tech, marketing, SEO, and SaaS subreddits averaged an AI-likelihood score of 0.215, with r/localseo near 0.33, while non-tech communities scored close to zero.² The takeaway for a local service business is reassuring. The communities most relevant to you, such as r/Boulder, r/Denver, and home and trade subreddits, are still overwhelmingly human, which is exactly why AI models trust them.

Comparison content beats your sales page

The Siege buying-advice study found that "X vs. Y" pages and listicles were the most-cited formats, while product pages and homepages appeared in single-digit citation rates.¹ This holds across the major assistants, though the mix differs. Google's AI experiences lean heavily on Google.com itself as a meta-source, Perplexity surfaces a broader range of recently updated URLs because it weights real-time retrieval, and ChatGPT consistently favors structured comparison pages and discussion-heavy domains.¹

For a local business this is a strategic gift, because comparison and "best of" content is exactly the kind of thing your local market is short on. There is no shortage of national listicles about CRM software. There is a real shortage of an honest, current page that compares the foundation repair options in Louisville and Lafayette, or that lays out how to choose a physical therapy practice in Longmont. That gap is the whitespace.

What this means for Front Range businesses

Here is how we translate the research into work that actually moves a local business into AI recommendations.

Earn presence in the sources, not just on your site

If 80 to 95 percent of recommendation citations come from third parties, then your visibility plan cannot stop at your own website. It has to include the review ecosystem, the local subreddits and forums where people ask for recommendations, and the publishers and directories that cover your market. We are not talking about spamming Reddit with your link, which gets you banned and ignored. We are talking about being genuinely present and genuinely well-reviewed in the places the models already read.

Build the comparison content your market is missing

Because comparison pages and listicles get cited far more than sales pages, one of the highest-leverage things a Front Range business can publish is a fair, specific, locally grounded comparison or buyer's guide. "How to choose a roofer in Boulder after a hailstorm" or "What to look for in a Denver bookkeeping service" does double duty. It serves the human reader, and it gives AI models a structured local source to cite when no national publisher has bothered to cover your town.

Get your reviews in order

Review platforms are part of the trust layer the models pull from. A thin or stale review profile is not just a conversion problem anymore. It is a citation problem. Consistent, recent, specific reviews on the platforms that matter for your category feed the exact ecosystem AI reaches into when it builds a recommendation.

Keep your own site clean and consistent

The brand site still earns its keep. It is where the model verifies the facts it found elsewhere, where your name, service area, and specialties get confirmed, and where structured data tells the machine what you are. Think of your site as the source of truth the model checks against, not the source of the recommendation itself. Both jobs matter, and they are different jobs.

The opening for local businesses

The discouraging read of this research is that your website matters less than you hoped. The encouraging read, and the one we believe, is that AI recommendation is winnable through honest work that national brands rarely bother to do at the local level. Most of your competitors are still polishing their homepage and ignoring the third-party ecosystem entirely. The owner who shows up in the local discussions, earns real reviews, and publishes the comparison content their town actually needs is going to be the one the model reaches for.

The map of where AI looks is now public. The businesses that read it carefully and act on it will be the ones getting recommended on the Front Range over the next few years.

Footnotes / Sources

There is a sentence about your business that you did not write, that you cannot see in any dashboard, and that more and more of your customers read before they ever land on your site. It is the sentence ChatGPT, Gemini, or Perplexity produces when someone asks about you or about your category. For a local business, that sentence is now part of your reputation whether you manage it or not.

The good news is that this sentence is not random, and it is not permanent. It is assembled from sources, and sources can be influenced. Recent work from Siege Media lays out a clean way to think about how AI forms its description of a brand and how to shift it. We have been applying the same logic for Front Range businesses, and the local version of the playbook is more winnable than the national one.

AI describes you by counting votes

The most useful idea in the Siege framing is that an AI model does not have an opinion about your business. It has a tally. When the model needs to describe you, it pulls the third-party sources it trusts, reads how those sources describe you, and reflects the description that shows up most often. Siege calls the phrase that wins this tally your descriptor consensus, and they make the point plainly: the phrase that repeats the most across your sources is your current winning vote, whether you want it to be or not.¹

This matters because it tells you where the work is. You cannot argue with ChatGPT directly. You change what it says by changing what its sources say. And as we have covered elsewhere, those sources are overwhelmingly third-party rather than your own website. A separate Siege buying-advice study found that across industries, 80 to 95 percent of the citations behind AI recommendations came from outside the brand's own domain.² Your homepage is not casting the deciding vote. The review sites, community threads, and comparison articles are.

The three-step audit, adapted for a local business

Siege lays out a three-step audit for finding and then shifting your descriptor consensus.¹ Here is the method, with the local adaptation we use for Front Range clients.

Step one: the prompt audit

Run a set of 10 to 20 buyer-intent prompts across ChatGPT, Perplexity, and Gemini, and capture each response word for word.¹ Siege's examples are things like "best [category] for [use case]," "[competitor] vs [you]," and "[you] alternatives."¹ For a local business, the prompts get geographic. We run queries like "best [service] in Boulder," "top-rated [service] near Longmont," and "[competitor] vs [client] in Denver." For each response, write down four things: did you appear at all, how were you described, which competitors were positioned better, and what specific words the model used.

Step two: the source audit

For every response, log every source the model cited. Capture the prompt, which assistant cited it, the URL, and what type of source it is, such as a review platform, a community thread, a local publisher, or a directory.¹ Then tally the source types. The ones that come up again and again are the sources currently casting votes on your business. For a local company, this list tends to surface Google reviews, Yelp, regional news sites, chamber and directory listings, and the local subreddits. That list is your real target list, not some generic SEO checklist.

Step three: the descriptor audit

Read the top five to ten third-party sources for each prompt and pull out the exact phrases used to describe you. Tally them. The phrase that repeats most is your current consensus.¹ The output Siege recommends is a clear gap analysis covering what the consensus currently says, what you want it to say, and which sources need to change.¹ We hand Front Range clients exactly this: the sentence the internet currently writes about them, the sentence we are aiming for, and the specific pages and profiles standing between the two.

A worked example

Say a Lafayette HVAC company runs the audit and finds that across ChatGPT, Perplexity, and Gemini, the recurring description is some version of "an affordable option for basic repairs." That is the consensus, pulled from a handful of older reviews and a thin directory listing. The owner, though, has spent the last two years building a high-end heat pump and electrification practice. The gap is glaring: the market describes the business they used to be, not the one they are now.

The fix is not to rewrite the homepage and hope. It is to go back to the source list from step two and shift the language where the votes are. New reviews that mention heat pump installs and energy efficiency. A local publisher piece on electrification rebates that quotes the owner. A spot on the regional "best HVAC for heat pumps" roundup that did not exist yet, because nobody in the area had written it. Each of those is one more vote for the descriptor the owner actually wants, cast in a place the model already trusts.

Changing the description takes earned work, not edits

Once you know the gap, the work is to shift the language across the sources that vote. That means earned media and digital PR that frame you the way you want to be framed, reviews that use the language of the outcome you deliver, and presence in the listicles and roundups that cover your category and your area. You are not editing one page. You are nudging a consensus.

Siege is honest about the timeline, and we are too. They estimate it takes roughly 60 to 90 days to shift a descriptor's share of voice across earned media and reviews, with listicles and affiliate roundups lagging another 30 to 60 days because they reindex more slowly.¹ An early signal that it is working is your target descriptor starting to appear in AI responses within the first 30 days, even before the broader share-of-voice number moves.¹ This is patient work. It is also durable work, because once the consensus shifts it tends to hold.

Why the local version is more winnable

National brands fight over descriptor consensus in categories with thousands of competing sources. A SaaS company trying to own a phrase is up against an enormous, crowded trust layer. A Boulder or Erie service business is not. The number of sources describing a local service company is small, the local communities that feed AI are still overwhelmingly human and trusted, and most local competitors have never run an audit like this in their lives.

That asymmetry is the opportunity. The sentence AI writes about your business is being formed right now from a handful of sources, most of which you can identify in an afternoon and influence over a quarter. The owners who treat that sentence as something to manage, rather than something that simply happens to them, are going to be the ones AI describes the way they actually want to be described.

Footnotes / Sources

Why Every AI Engine Cites Different Signals (And What That Means for Local Visibility)

When a business owner asks us to "get them into AI search," they usually picture one switch. Flip it, and the business shows up everywhere a customer asks a question. We wish it worked that way, but it doesn't. We ran more than 30 high-intent local queries across the major answer engines on the last two days of May 2026, captured the raw answers, and the clearest finding was this: there is no single AI search. There are several engines, and each one reaches for a different set of signals when it decides which local business to name.

That sounds like bad news. It's actually the opening. Because the engines disagree, a business that is invisible on one can still be the named answer on another, and the movement from "named by one engine" to "named by all of them" is something our team can engineer. The first step is understanding what each engine actually rewards.

The engines aren't reading the same web

Every answer engine has to do two things: find candidate sources, then decide which ones to trust and cite. Where they differ is in what they pull from and what they weight. One engine treats a Reddit thread as gospel. Another barely registers it. One anchors a price to whichever page publishes the cleanest number. Another won't quote a price at all without a brand-name source behind it.

We watched this happen in real answers. Ask three engines "best med spa in Boulder Colorado" and you get three different lead names. Perplexity and ChatGPT led with one business, Grok led with another, and Claude led with a third entirely. No business filled the whole row. That fragmentation is the signal-set differences playing out in public.

Here is how the major US engines behave, grounded in what we observed and, where we're inferring rather than confirming, said plainly.

ChatGPT (OpenAI)

ChatGPT runs web search underneath its answers now, and for local queries it tracks closest to the Google Maps and aggregator layer. In our captures it leaned on review counts and listing presence, and it was the engine most likely to omit a strong local business that simply hadn't built that footprint. One Boulder med spa was named by Perplexity, Claude, and Grok but left out of ChatGPT's top block entirely. The business was good. It just hadn't accumulated the reviews-and-listings signals ChatGPT reaches for first.

The practical read: ChatGPT rewards the same things that win the Maps pack. Review volume, accurate listings, and presence in the aggregators it tends to pull from. If you're strong on Maps, you're in good shape here. If you're not, ChatGPT is where that gap shows up first.

Google Gemini

Gemini is Google's standalone chatbot, and it has direct access to Google's index plus the Knowledge Graph. We didn't run Gemini in this round, so we're being careful here rather than confident. Based on its architecture and our prior work, Gemini tends to favor entities Google already understands well: businesses with a complete Google Business Profile, consistent name-address-phone data across the web, and structured data Google can map to a known entity. Recency matters too, because Google's freshness signals carry into Gemini's answers.

We're queuing Gemini for the next installment of our study so we can replace this inference with captured data. For now, treat it as "optimize the way Google already wants you to, and assume Gemini inherits most of it."

Google AI Overviews

This one gets confused with Gemini constantly, so it's worth separating clearly. Gemini is the chatbot you visit on purpose. Google AI Overviews is the AI answer block that appears at the very top of a normal Google search results page, above the regular blue links, without you asking for it. Same company, different surface, and they don't always cite the same sources.

AI Overviews draws heavily on pages that already rank well in classic Google search and that carry clear E-E-A-T signals: experience, expertise, authoritativeness, and trust. It favors content with strong structure, schema markup it can parse, and freshness. We didn't capture AI Overviews this round either, so this is observed-elsewhere and inferred, not measured by us yet. The takeaway holds regardless: AI Overviews rewards traditional SEO strength more than any other engine on this list, because it's sitting on top of traditional Google. If you rank on page one and your schema is clean, you're a candidate.

Perplexity

Perplexity does the most aggressive real-time retrieval of any major engine, running each query against its own crawler index with a re-ranker on top, then showing numbered citations inline. For local queries in our study, it leaned on Google Maps review counts and aggregator listings, much like ChatGPT, but it surfaced a wider set of named businesses and was more willing to cite a specific local page directly.

On pricing questions, Perplexity did something we saw across multiple engines: it anchored the number to whatever page published the cleanest, most specific figure. When we asked "dental implants Boulder cost," Perplexity, Claude, and Grok all pulled the same "$3,150 start to finish" number from one local practice's pricing page. Not an aggregator. One structured pricing page that happened to publish the most citable number available.

The read for Perplexity: reviews and listings get you considered, and a clean, dated, number-dense page gets you cited on the questions where specifics matter.

Claude (Anthropic)

Claude rewarded structure more than any other engine we tested. With web search on, it named businesses, but the ones it favored tended to have clean, editorially organized pages: clear headings, well-formed content, information laid out so a model can parse claims without guessing. Claude was the engine where a well-built page beat a thin page with a slightly bigger review count.

For a local business, that means Claude responds to the work that's furthest from "buy more reviews." It rewards genuinely good pages. Clear service descriptions, structured pricing, FAQ sections that answer real questions, and schema that backs up what the page says. That's slower to build than a listing, but it's also more durable, and it tends to help everywhere else too.

Grok (xAI)

Grok was the most distinctive engine in the study, and it's the clearest proof that AI optimization is several jobs. Grok visibly leaned on Reddit and forum chatter. It surfaced individual practitioners by name, sometimes ahead of the businesses they worked for, because real people had recommended them in threads. It cited a large number of sources per answer, from 55 to over 100 in some cases, and a lot of that was social and forum content.

You cannot win Grok with reviews and schema alone. Winning Grok is partly a genuine-participation-on-Reddit job: being a real, recommended presence in the threads where people ask for recommendations in your category and city. That can't be faked without becoming spam, and Grok's heavy forum weighting means the businesses that show up are often the ones humans actually vouched for.

Microsoft Copilot and Bing Chat

Copilot is built on the Bing index, so its candidate set comes from what Bing has crawled and ranked. We didn't capture Copilot directly this round, so we're inferring from its known foundation. In practice that means two things matter more for Copilot than for the others: being verified and present in Bing Places, and ranking in Bing's organic index, which a lot of local businesses ignore entirely because they fixate on Google.

That neglect is the opportunity. Bing Webmaster Tools verification and a Bing Places listing take an afternoon, and almost nobody in a typical local market has bothered. If Copilot or Bing Chat is pulling answers for your category, the field is thin.

DeepSeek, briefly

DeepSeek is worth a mention for completeness rather than for local strategy. It's a strong general model, but for US local-business queries its web-grounded retrieval is less mature and far less used by US customers than the engines above. We wouldn't tell a Front Range business to spend a dollar optimizing for it today. We mention it so you know we considered it and concluded it isn't where your local customers are asking yet.

What this means for a local business

Pull all of that together and one conclusion is unavoidable. "AI optimization" is not one job. It's at least four jobs, and which ones matter depends on your category and city.

• Reviews and accurate listings win ChatGPT and Perplexity, and they help everywhere. This is the foundation.
• Clean, structured, schema-backed pages win Claude and feed the pricing answers every engine reaches for. A single dated, number-dense pricing page was the most citable asset in our entire study.
• Genuine forum and Reddit presence win Grok, and that's earned, not bought.
• Bing verification and a Google entity Google already understands win Copilot and set you up for AI Overviews and Gemini.

A vendor selling you a one-trick AI fix is selling you one engine's answer. The reason the work pays off is the same reason it's harder than a single switch: because the engines disagree, the named set in most local categories is still fragmented, and that means the seats are open. They won't stay open. The businesses that move while the field is split are the ones that end up named on every engine at once.

If you want to know which engines already name businesses in your category and city, and which signals each one is rewarding there, that's the question our team answers first on every engagement. The read is most valuable now, while the answers are still moving.

Footnotes and sources

1. SEO Believer research team, "Who AI Search Actually Cites: A Study of 30+ High-Intent Local Queries Across 8 Front Range Service Verticals," data window 2026-05-31 to 2026-06-01. Live captures from Perplexity, Claude, Grok, and ChatGPT. Findings on per-engine signal differences, the fragmented med-spa named set, and the single-source pricing-page mechanic come directly from this study.
2. Engine behaviors marked as observed-not-confirmed (Gemini, Google AI Overviews, Microsoft Copilot, DeepSeek) were not captured in this round and are inferred from each engine's known architecture and our prior work. These are queued for capture in the next installment.
3. Siege Media research on generative engine optimization and how AI answer engines select and cite sources, consulted for cross-engine signal patterns.

How Often AI Search Actually Changes (And Why We Monitor Weekly)

There's a claim floating around the generative engine optimization world that AI search changes so fast you need someone watching it every single week or your visibility falls apart. We want to be careful here, because the version of that claim that gets repeated most often is overstated, and we don't think it serves you to repeat marketing that the evidence doesn't support.

So let's do this honestly. We pulled the 2026 volatility data and looked at what it actually says about how fast AI citation behavior moves. The real picture is more interesting than the scary version, and it leads to a more defensible conclusion about what ongoing monitoring is for.

The short answer: meaningful changes happen roughly monthly, sometimes faster, and almost never with advance notice. That's exactly why we monitor weekly. Not because the ground shifts every seven days, but because when it does shift, you want to find out in days rather than discovering it a quarter later when your leads have already dried up.

What "changes every week" gets wrong

If you measure the AI engines week over week, most of what they cite stays put. BrightEdge tracks thousands of prompts every week across ChatGPT, Gemini, Google AI Mode, Google AI Overviews, and Perplexity, spanning nine industries. Their finding is striking: 96.8% of cited domains saw zero change from one week to the next.¹

Read that again, because it's the opposite of the panic narrative. In any given week, the overwhelming majority of source citations are stable. If somebody tells you the algorithm rewrites itself every seven days, the data doesn't back them up.

So the honest framing isn't "AI search changes weekly." It's closer to this: AI search is mostly stable week to week, the meaningful shifts cluster around platform updates that land roughly monthly, and the changes that do happen tend to be sudden and binary rather than gradual.

The cadence the evidence actually supports

When we look at where the real movement comes from, it lines up with the release schedules of the platforms themselves.

Google has held to roughly three-to-four-week release cycles since late 2025.² The May 2026 core update began rolling out on May 21 and finished around June 4, which is a typical window. On top of the core updates, Google shipped five structural changes to AI Overviews and AI Mode on May 6, 2026, described as the biggest change since AI Overviews launched. Inline citations moved to sit next to the specific text they support, hover previews started showing site names, and a new "Expert Advice" block began pulling first-hand perspectives from forums and review sites.³

That's the pattern. The engines don't quietly drift a little every week. They hold mostly steady, then a named update lands and reshuffles which sources get pulled and how citations are displayed. The cadence of those updates is monthly more than weekly.

The change is binary, not gradual

Here's the part that matters for why monitoring is worth doing at all. When a domain loses citations, it usually doesn't slide down a few points. BrightEdge found that domains went from being cited to not being cited at all on a given prompt. Among the roughly 3% of domains that moved in a given week, 87% of those moves were declines.¹ Visibility tends to switch off rather than fade.

The variation by industry is worth knowing too. Government and institutional sites were the most stable, with under 4% change, because once an AI engine trusts a .gov source that trust holds. Finance was the most volatile, with over half of tracked finance domains seeing changes. Health and medical sites saw 34% change, and every single one of those changes was a decline.¹ So the rate of change isn't uniform. Your exposure depends on what business you're in.

The case that proves why you watch

The single best illustration of why monitoring earns its keep happened in late 2025, and it had nothing to do with any AI company changing its own model.

Around September 10, 2025, Google removed the num=100 parameter from its search results, the setting that let tools pull the top 100 results in one request instead of ten. That parameter was widely used by the data providers that scrape Google, including the providers that supply search data to OpenAI, since ChatGPT doesn't crawl Google directly. When the parameter disappeared, the data pipeline feeding ChatGPT's citations changed underneath it.⁴

The result: ChatGPT's Reddit citation share fell from roughly 60% to about 10% over six weeks. PR Newswire, Forbes, and Medium absorbed most of the displaced share.⁴ A Semrush study across more than 230,000 prompts over three months confirmed the same broader pattern, that AI citation sources are volatile and subject to sudden shifts.⁵

Sit with what happened there. A business optimizing for ChatGPT visibility had its citation landscape rearranged by an upstream change at a completely different company, with no announcement, no warning, and no entry in any changelog. The only way to know it happened was to be measuring.

Why weekly monitoring is the right response

This is where the honest version of the claim lands, and we think it's a stronger argument than the overstated one.

You don't monitor weekly because the algorithm changes weekly. You monitor weekly because:

• Changes arrive without warning. The Reddit collapse came from a third-party parameter change. Google's structural AI Overviews update wasn't pre-announced to the businesses it affected. You can't schedule your attention around a release calendar nobody publishes.
• The shifts are binary. Since visibility tends to switch off rather than decay, there's no slow signal that gives you a month of runway. You're either still cited or you're not, and the gap between those two states is where leads live or die.
• The detection window is the whole game. If a meaningful shift lands and you're checking monthly, you might run three or four weeks blind. Checking weekly turns that into days. For a business that depends on AI search referrals, that difference compounds.
• Recovery starts with diagnosis. When citations drop, the first question is always why. Weekly snapshots give you a before-and-after you can actually reason about, instead of a vague sense that things got worse sometime last quarter.

Put plainly: the landscape is stable enough that you don't need to rebuild your strategy every week, and volatile enough that you can't afford to look away for a quarter. Weekly monitoring is the cadence that matches that reality. It's frequent enough to catch the sudden shifts and infrequent enough to avoid chasing noise.

What our team actually watches

Monitoring only helps if it's measuring the right things, so here's what we track on a weekly basis for the businesses we work with.

We run a fixed set of queries through ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews and log the full responses, so we have a verbatim record of what each engine said and which sources it cited. We watch for the moment a brand stops appearing, because that binary switch-off is the signal that matters most. We note which competitor or publisher absorbed the citation when a business loses one, since that tells us where the engine's trust moved. And we keep an eye on the platform release calendars and the broader volatility reporting, so when a core update or a structural change lands we already know to look harder that week.

None of that requires pretending the engines reinvent themselves every seven days. It just requires showing up consistently, because the cost of a missed shift is measured in lost referrals, and the changes that cause those losses don't send a calendar invite.

The honest bottom line

AI search is mostly stable week to week. The meaningful changes cluster around platform updates that land roughly monthly, and the most damaging shifts can come from upstream changes you'd never see coming. When visibility drops, it tends to drop all at once rather than fading slowly.

That combination is the real argument for monitoring. Not weekly upheaval, but weekly vigilance against changes that arrive on their own schedule and hit hard when they do. We'd rather tell you the accurate version and let it stand on its own, because it's a better reason to keep watching than the scary one ever was.

Sources

Who AI Search Actually Cites

A Study of 30+ High-Intent Local Queries Across 8 Front Range Service Verticals

[New Data: June 2026]

Most of what gets written about AI search is theory. People speculate about how ChatGPT "probably" picks sources, or they quote a vendor's whitepaper, and then everyone nods. We wanted something harder than speculation. So we did the obvious thing that almost nobody does: we sat down and asked the AIs the exact questions a local customer would ask, captured the answers word for word, and recorded who got cited and who got skipped.

This is the first installment of an ongoing measurement we run for the Front Range. It is small, it is honest about its limits, and it already overturns a few things we believed going in. If you own a service business in Boulder, Denver, or any comparable metro, the short version is this: AI search is already recommending businesses by name in your category, the named set is unstable from one AI to the next, and in several verticals a single source is quietly feeding the answer that every AI repeats. That last part is the opening.

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Why we ran this

Our team has been ranking businesses in search since 1995. We watched the shift from directories to Google, from ten blue links to the local map pack, and now from the map pack to AI answers that name a handful of businesses and stop. Each shift moved the scarce real estate. The map pack compressed ten results into three. AI answers can compress three into one.

When a prospect asks us "do I really need to worry about AI search yet," we did not want to hand them a trend chart. We wanted to show them their own market, their own category, and the actual answer an AI gives today. That is what this study is.

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Methodology

We treat this like an experiment, because that is the only way to keep ourselves honest. Here is exactly what we did, including the parts that are thin.

What we asked. We ran more than 30 high-intent local queries, the kind that signal someone is close to spending money. Examples: "best med spa in Boulder Colorado," "dental implants Boulder cost," "best plastic surgeon Denver," "braces cost Boulder," "best HRT clinic near me" across six metros. We deliberately mixed two query shapes: "best [category] in [city]" discovery questions, and "[procedure] cost [city]" pricing questions, because they behave very differently.

Which AIs. We queried six AIs directly and captured their live output as page text:

AI	How we queried it	Coverage
Perplexity	Live web UI, signed-out and signed-in	Primary AI, most queries
Claude	Live web UI, signed in, web search on	Full coverage on the core dentistry and med-spa set
Grok	Live web UI, signed in	Full coverage on the core set, 55 to 105 cited sources per answer
ChatGPT	API (gpt-4o-mini, web search on)	Sampled queries used as a cross-check
Google AI Overviews	Live Google Search, signed-out, captured 2026-06-02	20 queries. An AI Overview block rendered on 11 of them (55%); the other 9 returned a local 3-pack with no overview, which is itself a finding
Gemini	Live web app, signed-out, Google Search grounding on, captured 2026-06-02	8 queries, including all five highest-value bet queries. No sign-in was required; the signed-out app answered and grounded in Google Search

We had planned Gemini and Google AI Overviews for a later installment. We were able to run both this round, so they are in the findings below rather than deferred. One honest note on Gemini: signed-out access used the Flash model, so a signed-in Pro session could return a different named set, and we will widen Gemini coverage next round.

Which verticals. Eight service categories: med spas and aesthetics, dentistry (split into implant-cost, cosmetic, and veneers intent), orthodontics, cosmetic and plastic surgery, fertility and IVF, LASIK and cataract, HRT and TRT, and personal injury law.

Which markets. Boulder and Denver for the full vertical sweep. For the HRT category we widened to six metros to test whether the pattern travels: Boulder, Denver, Phoenix, Nashville, Tampa, and Austin.

When. Captured live on 2026-05-31 and 2026-06-01.

Tools. AI answers were captured directly from the live interfaces. For map-pack and rank-position work we use our own measurement tool, RankGridEngine, which pulls geo-gridded local rankings so we can see how a business surfaces across a city rather than from a single point. For this installment the headline findings come from the direct AI captures; the grid tool is how we will track movement over time in follow-ups.

The honest limits. This is a deliberately small, recent sample. We ran six AIs now, not the whole market. We ran two cities for most verticals and six for one. We captured a snapshot in time, and AI answers drift week to week. Google AI Overviews rendered on 55% of the queries we ran; the rest returned a local 3-pack instead, so AIO coverage is partial by design, not by omission. Gemini answered signed-out on the Flash model, and a signed-in Pro session could differ. We are not claiming statistical significance across the entire Front Range. We are reporting what these specific AIs actually returned for these specific high-intent queries on these specific dates. Where a number is not in our captures, we do not invent one. When we say "every AI we tested," we mean the AIs listed above, not the whole market.

One more limit worth naming, because it is the kind of thing that quietly skews local results. Our live AI queries were run from a Boulder IP address. For the Boulder-area verticals, that is exactly right: it matches what a real local searcher in Boulder would see. For the multi-metro HRT comparison, where we asked about Denver, Phoenix, Nashville, Tampa, and Austin, a Boulder IP can nudge which local businesses an AI chooses to show, so those out-of-town results should be confirmed with location-set queries before anyone leans on the exact named set. Setting the query's geographic location for each city, for example using Ahrefs location-set SERP data so the AI sees the market a local searcher there would see, is our standard going forward. We are flagging it not as a hole in the findings but as plain scientific honesty about what a single vantage point can and cannot tell you.

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Key findings

1. AI already names businesses by name, and the named set changes from one AI to the next

The single most useful thing we learned: there is no stable winner. Ask the AIs the same "best med spa in Boulder" question and you get a different lead name almost every time. Perplexity and ChatGPT led with Mōv Aesthetics. Grok led with Skin Deep MD. Claude led with Vasu and Rinnova. One Boulder med spa, RESTŌR, was named by Perplexity, Claude, and Grok but omitted entirely by ChatGPT's top block.

Adding Gemini this round made the picture more fragmented, not less. Gemini named a set that barely overlapped any other AI: Rinnova, Choice Skin & Laser, Alchemy Face Bar, and Boulder Plastic Surgery & IV Center, with no RESTŌR, no Mōv, no Vasu, and no Luxe Room. On "where should I get Botox in Boulder" it named yet another set: Rinat Aesthetics, Alcheme Bioenergetic Medspa, Flatiron Aesthetic Surgery, Boulder Plastic Surgery, and Vanguard Skin Specialists. Google AI Overviews, for its part, did not even render a block for these med-spa discovery queries. It fell back to the local 3-pack, where RESTŌR led on the review-weighted "best med spa" and "fillers" queries.

That instability is the whole opportunity. If one business owned every AI's answer, there would be nothing to win. Because the named set is fragmented across six AIs now, a business can move from "named by one AI" to "named by all of them," and that movement is what we can engineer.

2. On pricing questions, one source often feeds every AI

This is the finding we keep coming back to. When we asked "dental implants Boulder cost," Perplexity, Claude, and Grok all anchored the single-implant number to the same source: one local practice's "$3,150 start to finish" pricing page. Not a directory or comparison site, one well-built pricing page. Every AI reached for it because it was the cleanest, most specific, most quotable number available.

Google AI Overviews then confirmed it from the inside. When we ran the same query on Google this round, the AI Overview block named that same practice's "$3,150 for a single tooth" bundled pricing alongside the broader range. So four AIs plus Google's own overview now converge on one well-built pricing page. That is the strongest version of this finding we have, and it is exactly the kind of convergence a business can earn its way into.

Gemini is the honest exception, and it points to where the work goes next. On both "dental implants Boulder cost" and "braces cost Boulder," Gemini returned generic national-style ranges and named no local practice and no comparison site at all. So owning the well-built pricing page wins Perplexity, Claude, Grok, and Google AI Overviews today, but it does not yet move Gemini, which is not pulling a single local cost source. Gemini looks like it is won by being a well-known, established business in Google's own index rather than by a cost page.

We found a related shape in orthodontics. Ask "braces cost Boulder" and the cost figures trace back almost entirely to a single directory or comparison site, though which one depends on the AI. Perplexity and Grok leaned on a local "dentists ranked" comparison site; Google AI Overviews leaned on national aligner-brand sites instead. Same mechanic, a different site in front per AI.

The lesson is blunt: pricing questions reward whoever publishes the clearest dated, number-dense page with the behind-the-scenes labels that help AIs read it, and most of the AIs will reach for it. Right now, in these categories, that is one source. There is room for exactly one more, and being that source means getting quoted by most of the AIs at once. Gemini is the reminder that no single page wins every AI, so a real program also works to make you a well-known, established name in Google's index.

3. Whether AI search is worth your time depends on your line of work

We checked each kind of business to see how the AIs answer for it. The question was simple: when someone asks an AI to recommend a business like yours, does it already have a fixed set of favorites it always names, or is there still room for a newcomer to become one of those names? The answer isn't the same from one industry to the next.

Your business	What the AIs do today	What it means for you
Dentist, "implant cost" (Boulder)	Every AI pulls the price from the same single website	The easiest win we found. Publish one clear, well-built pricing page and you can become a source the AIs quote.
Orthodontist, "braces cost" (Boulder)	One comparison site owns the price answer, and it's beatable	Almost the same easy opening as dental implants.
Med spa (Boulder)	The AIs don't agree on a favorite yet	Winnable by collecting more reviews and keeping your online listings accurate and complete.
Dentist, cosmetic / veneers (Boulder)	Each AI names a different handful of practices	Room to go from being mentioned by one AI to being mentioned by all of them.
Cosmetic / plastic surgery (Boulder)	Open, no clear favorite, but trust matters a lot here	A real opening, though slower, because the leaders already have hundreds of reviews.
LASIK / cataract eye surgery	One well-known clinic plus big national chains already hold the answer	Tougher. The early-mover advantage here is already taken, so it takes more work and more time. Not the first place to start.
Fertility / IVF	A couple of regional chains already hold the answer	Tougher. The early-mover advantage is gone, so winning here takes more work, more time, and more patience. Not the first place to start.
Personal injury law	Crowded with directories and heavy advertisers	Tougher. The ad and directory spend is entrenched, so it takes more work and a longer runway. Not the first place to start.

These three are specific verticals we tested and ruled out as the ones to chase first. That is not the same as "give up." It means the easy early-mover advantage in those categories has already been claimed, so they take more work, more time, and more patience to win. The smart play is to start where the ground is still open, build the wins and the proof there, and come back to the tougher categories with momentum.

The point for an owner is this: don't ask "can anyone win at AI search?" Ask "can I win in my industry, in my town, and where do I start?" For dentists and orthodontists asking about cost, the answer is yes, and cheaply, right now. For med spas, cosmetic dentists, and HRT clinics, the early-mover advantage is still sitting there for whoever grabs it first, and it will not sit there forever. For fertility clinics and injury firms, it is a longer climb, so we would not point your first dollar there. None of that means the door is shut. It means we would sequence the easy, open wins ahead of the hard, crowded ones.

4. The telehealth fear is unfounded, and the real competitor is the franchise

We expected AI to push national telehealth brands for "HRT near me" style queries. It did not. Across all six metros and every AI we tested, AI surfaced local, in-person clinics. Zero Hims, zero Defy, zero Hone in any local-intent answer. The answers are map-pack driven, so they pull brick-and-mortar.

Both of Google's products reinforced this directly this round. Google AI Overviews rendered an HRT block in five of the six metros (Boulder, Denver, Phoenix, Nashville, and Tampa; Austin returned a local 3-pack with no block), and every one named local in-person clinics: Radiant Health for Women and Boulder Longevity Institute in Boulder, Cunningham Clinic and the CU Anschutz Menopause Clinic in Denver, Tutera Medical in Phoenix. Gemini did the same for Boulder, naming Boulder Valley Health Center, Boulder Women's Clinic, and Boulder Longevity Institute. No telehealth brand appeared on either Google product, in any metro. Interestingly, AIO and Gemini named different local leaders for the same Boulder query, so even inside one company's two products the named set is not locked.

The real competition turned out to be multi-location franchises colonizing the local map pack. Where a TRT franchise had planted several locations (Austin, Phoenix), it crowded the answer. Where it had one or none (Nashville, Boulder), local independents owned the ground. The widest openings were Nashville and Boulder; the tightest were Austin (franchise lock) and Tampa (local giants with 500 to 1,000-plus review moats).

5. Different AIs reward different things

Grok visibly leaned on Reddit and forum chatter, surfacing individual practitioners by name because people recommended them in threads. Perplexity and Claude leaned on Google Maps review counts and directory and comparison-site listings. ChatGPT tracked closest to that Maps-and-directory layer and was the most likely to leave out a strong local business that had not built up those reviews and listings.

Adding Google's two AIs this round filled in the rest of the map. Google AI Overviews leans on top-ranking pages plus Google's own record of your business and the Maps layer, with a clear appetite for dated, number-dense pricing pages; it is the AI most likely to show up when there is a clean number or a settled set to summarize. Gemini, drawing on the same Google index, rewards how well-known and established your business is in that index, which is why its discovery answers favored long-established, highly-reviewed names and why it stayed generic on cost queries where it had no single local source to pull. Both Google products consistently named the Maps-prominent set and consistently left out the strong-but-quiet local names that Grok surfaces from Reddit.

This matters because "optimize for AI search" is not one job. Winning Grok is partly a show-up-genuinely-on-Reddit job. Winning Perplexity and ChatGPT is largely a reviews-and-listings job. Winning Claude rewards a clean, well-organized page. Winning Google AI Overviews rewards top-ranking content, the behind-the-scenes labels that help AIs read your page, and a dated number-dense page. Winning Gemini rewards being a well-known, established name in the Google index. Five different things to satisfy, one business to get cited.

6. Google AI Overviews shows up for cost questions, not for "best of" questions

This is the clearest new pattern from the Google data. We ran 20 queries through Google Search, and an AI Overview block rendered on 11 of them, about 55%. The split was not random. Every pricing question we ran triggered an overview: dental implant cost, braces cost, LASIK cost, and IVF cost. So did the discovery questions in consolidated, high-authority categories: fertility, and HRT in five of the six metros. But the fragmented Boulder discovery questions, "best med spa," "best cosmetic dentist," "best dentist for veneers," "best orthodontist," "best plastic surgeon," "best cosmetic surgeon," did not trigger an overview at all. Google fell back to the local 3-pack instead.

The read is simple. When there is a settled number or a settled set of names, Google summarizes it in an overview. When the field is genuinely fragmented, Google declines to summarize and hands you the map pack. For an owner, that is a tell about where the two games are. In categories where Google still shows the 3-pack, the fight is local SEO and reviews. In categories where the overview has already taken over, the fight is to be one of the few sources that overview is built from. Both are winnable, but they are not the same work, and the overview-vs-3-pack split tells you which one you are in.

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What this means for your business

We write these findings ROI-first, because the point is not to be interesting, it is to protect revenue.

1. When AI leaves you out, you slowly stop being considered, and nothing warns you. When AI names three businesses for "best [your category] near me" and you are not one of them, nothing breaks visibly. No alert fires. You simply stop being considered by the growing share of buyers who ask an AI before they ask a friend. The customers you lose this way never knew you existed, so you never see them in your "why didn't we close" review. The damage you cannot see is more dangerous than the kind you can.

2. The cheapest win is the pricing page. If you are in a category where one source currently feeds the cost answer, a single dated, number-dense pricing page, built with the behind-the-scenes labels that help AIs read it, can make you the second source the AIs pull from, across most of them at once. We watched Perplexity, Claude, Grok, and Google AI Overviews all reach for one practice's $3,150 implant figure. This is the highest-probability, lowest-cost AI-citation win we found in the entire study. For a dentist or orthodontist, that is real traffic from people who are still deciding, captured for the cost of one well-built page. The one caveat: Gemini stayed generic on cost and did not pull a local source, so the pricing page wins four of the AIs we tested but not yet Gemini, which is a reason to pair it with the work in point 5 of becoming a well-known, established name.

3. Pick your battle by category, and sequence it. If you are a fertility clinic or a personal injury firm, AI search is a tougher, longer climb, because the early-mover advantage in those categories is already taken by entrenched regional names and heavy ad and directory spend. We would not point your first marketing dollar there. That is not "give up," it is "start somewhere easier and come back with momentum." If you are a med spa, dentist, orthodontist, or HRT clinic in a city without many big chain competitors, the ground is open right now and the early-mover advantage is still there for the taking. It will not stay open. The businesses that move first get cited; the ones that wait have to overcome the big stacks of reviews the early movers built. So grab the open verticals now, while you still can.

4. Your competitor is probably a chain, not a national app. For local health and wellness, the threat AI surfaces is the multi-location chain planting flags in your map pack, not a telehealth brand. The defense is local: a well-kept Google Business Profile, a steady stream of new reviews, and local content. That is winnable for an independent who moves before the chain does.

5. There is no single "AI optimization" lever. Because each AI rewards different things, a real program works on reviews and listings (Perplexity, ChatGPT), clear pages with the behind-the-scenes labels that help AIs read them (Claude and Google AI Overviews), a genuine presence on Reddit and forums (Grok), and becoming a well-known, established name in the Google index (Gemini) at the same time. We watched the same Boulder query return a different named set on five AIs, and even Google's own two products disagreed with each other. Anyone selling you a one-trick fix is selling you one AI's answer.

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Suggested visualizations

[CHART: AI agreement matrix. Rows = the businesses named for "best med spa in Boulder." Columns = Perplexity, Claude, Grok, ChatGPT, Gemini, Google AI Overviews. Filled cell = named by that AI. Visually shows how fragmented the named set is and how no business fills the whole row. Gemini's column is almost entirely its own names, and the Google AI Overviews column is empty because no overview rendered for this query.]

[CHART: "One source feeds the answer" diagram. Center node = a single pricing page. Arrows out to Perplexity, Claude, Grok, and Google AI Overviews all citing the same "$3,150" number. Gemini sits to the side with a dashed line and a "generic ranges only, no local source" label. A second, empty node labeled "the open seat" sits beside it to show the capturable opening.]

[CHART: AI Overview trigger rate. A simple split graphic: of 20 Google queries, 11 rendered an AI Overview, 9 returned a local 3-pack. Group the 11 by what triggered them (all four cost queries; fertility and five-of-six HRT metros) versus the 9 that did not (the fragmented Boulder "best of" discovery queries). Annotation: "Cost questions get an overview. Fragmented 'best of' questions get the map pack."]

[CHART: Vertical openness ladder. Horizontal bar per vertical, sorted from most open (dentistry implant cost) to toughest (fertility, personal injury), color-coded green at the open end to amber at the tougher end. The tougher categories are labeled "early-mover advantage already taken, longer climb" rather than "closed." One glance shows where to start first and where the longer game is.]

[CHART: HRT across six metros. Small map or bar set showing local-independent visibility vs chain dominance per metro, with Nashville and Boulder at the open end and Austin and Tampa at the locked end. Annotation: "Zero telehealth brands appeared in any local answer, on any of six AIs including both Google products."]

[CHART: Signal-by-AI table styled as a graphic. Columns: Reviews and listings, Clear pages with the behind-the-scenes labels that help AIs read them, Reddit and forum presence, Being a well-known name in the Google index. AI logos placed under the signal each one rewarded most: Perplexity and ChatGPT under reviews, Claude and Google AI Overviews under clear labeled pages, Grok under Reddit and forum presence, Gemini under well-known name.]

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A note on who ran this

This study was produced by the SEO Believer research team. Our work goes back to the early commercial internet: our founder, Annette Thompson, has been getting organizations found in search since 1995, and most recently led the SEO and operations that took a nonprofit from a Domain Rating of 0.9 to 62 with no paid advertising, alongside the placement of more than 4,000 dogs into adoptive homes.

Her background is in Medical Technology and Biochemistry, and it shows in how we work. We run campaigns with a scientific approach: form a hypothesis, query the AIs directly, capture the raw answers, and report what actually happened rather than what the trend pieces predict. We would rather show you a small, honest, reproducible result than a large, confident, unfalsifiable one. This is the first installment of an ongoing measurement, and we will update it as the AIs, and the answers, change.

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Want to know who AI search names in your category and city? That is the question we answer first on every engagement. The whitespace closes as more businesses wake up to it, so the read is most valuable now.

Who AI Search Actually Cites

A Study of 30+ High-Intent Local Queries Across 8 Front Range Service Verticals

[New Data: June 2026]

Most of what gets written about AI search is theory. People speculate about how ChatGPT "probably" picks sources, or they quote a vendor's whitepaper, and then everyone nods. We wanted something harder than speculation. So we did the obvious thing that almost nobody does: we sat down and asked the AIs the exact questions a local customer would ask, captured the answers word for word, and recorded who got cited and who got skipped.

This is the first installment of an ongoing measurement we run for the Front Range. It is small, it is honest about its limits, and it already overturns a few things we believed going in. If you own a service business in Boulder, Denver, or any comparable metro, the short version is this: AI search is already recommending businesses by name in your category, the named set is unstable from one AI to the next, and in several verticals a single source is quietly feeding the answer that every AI repeats. That last part is the opening.

A note on naming: we have withheld the specific business names that surfaced in our captures out of professional courtesy to the local operators involved. The findings below are unchanged, the patterns are exactly what we observed, and the underlying data, including who got named in each query, is available on request.

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Why we ran this

Our team has been ranking businesses in search since 1995. We watched the shift from directories to Google, from ten blue links to the local map pack, and now from the map pack to AI answers that name a handful of businesses and stop. Each shift moved the scarce real estate. The map pack compressed ten results into three. AI answers can compress three into one.

When a prospect asks us "do I really need to worry about AI search yet," we did not want to hand them a trend chart. We wanted to show them their own market, their own category, and the actual answer an AI gives today. That is what this study is.

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Methodology

We treat this like an experiment, because that is the only way to keep ourselves honest. Here is exactly what we did, including the parts that are thin.

What we asked. We ran more than 30 high-intent local queries, the kind that signal someone is close to spending money. Examples: "best med spa in Boulder Colorado," "dental implants Boulder cost," "best plastic surgeon Denver," "braces cost Boulder," "best HRT clinic near me" across six metros. We deliberately mixed two query shapes: "best [category] in [city]" discovery questions, and "[procedure] cost [city]" pricing questions, because they behave very differently.

Which AIs. We queried six AIs directly and captured their live output as page text:

AI	How we queried it	Coverage
Perplexity	Live web UI, signed-out and signed-in	Primary AI, most queries
Claude	Live web UI, signed in, web search on	Full coverage on the core dentistry and med-spa set
Grok	Live web UI, signed in	Full coverage on the core set, 55 to 105 cited sources per answer
ChatGPT	API (gpt-4o-mini, web search on)	Sampled queries used as a cross-check
Google AI Overviews	Live Google Search, signed-out, captured 2026-06-02	20 queries. An AI Overview block rendered on 11 of them (55%); the other 9 returned a local 3-pack with no overview, which is itself a finding
Gemini	Live web app, signed-out, Google Search grounding on, captured 2026-06-02	8 queries, including all five highest-value bet queries. No sign-in was required; the signed-out app answered and grounded in Google Search

We had planned Gemini and Google AI Overviews for a later installment. We were able to run both this round, so they are in the findings below rather than deferred. One honest note on Gemini: signed-out access used the Flash model, so a signed-in Pro session could return a different named set, and we will widen Gemini coverage next round.

Which verticals. Eight service categories: med spas and aesthetics, dentistry (split into implant-cost, cosmetic, and veneers intent), orthodontics, cosmetic and plastic surgery, fertility and IVF, LASIK and cataract, HRT and TRT, and personal injury law.

Which markets. Boulder and Denver for the full vertical sweep. For the HRT category we widened to six metros to test whether the pattern travels: Boulder, Denver, Phoenix, Nashville, Tampa, and Austin.

When. Captured live on 2026-05-31 and 2026-06-01.

Tools. AI answers were captured directly from the live interfaces. For map-pack and rank-position work we use our own measurement tool, RankGridEngine, which pulls geo-gridded local rankings so we can see how a business surfaces across a city rather than from a single point. For this installment the headline findings come from the direct AI captures; the grid tool is how we will track movement over time in follow-ups.

The honest limits. This is a deliberately small, recent sample. We ran six AIs now, not the whole market. We ran two cities for most verticals and six for one. We captured a snapshot in time, and AI answers drift week to week. Google AI Overviews rendered on 55% of the queries we ran; the rest returned a local 3-pack instead, so AIO coverage is partial by design, not by omission. Gemini answered signed-out on the Flash model, and a signed-in Pro session could differ. We are not claiming statistical significance across the entire Front Range. We are reporting what these specific AIs actually returned for these specific high-intent queries on these specific dates. Where a number is not in our captures, we do not invent one. When we say "every AI we tested," we mean the AIs listed above, not the whole market.

One more limit worth naming, because it is the kind of thing that quietly skews local results. Our live AI queries were run from a Boulder IP address. For the Boulder-area verticals, that is exactly right: it matches what a real local searcher in Boulder would see. For the multi-metro HRT comparison, where we asked about Denver, Phoenix, Nashville, Tampa, and Austin, a Boulder IP can nudge which local businesses an AI chooses to show, so those out-of-town results should be confirmed with location-set queries before anyone leans on the exact named set. Setting the query's geographic location for each city, for example using Ahrefs location-set SERP data so the AI sees the market a local searcher there would see, is our standard going forward. We are flagging it not as a hole in the findings but as plain scientific honesty about what a single vantage point can and cannot tell you.

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Key findings

1. AI already names businesses by name, and the named set changes from one AI to the next

The single most useful thing we learned: there is no stable winner. Ask the AIs the same "best med spa in Boulder" question and you get a different lead name almost every time. Perplexity and ChatGPT led with one med spa. Grok led with a different one. Claude led with two others entirely. One Boulder med spa was named by Perplexity, Claude, and Grok but omitted entirely by ChatGPT's top block.

Adding Gemini this round made the picture more fragmented, not less. Gemini named a set of four med spas that barely overlapped any other AI, none of them the leader on Perplexity, Claude, Grok, or ChatGPT. On the Botox question it named yet another, almost entirely different set. Google AI Overviews, for its part, did not even render a block for these med-spa discovery questions. It fell back to the local 3-pack, where the leaders were the review-count front-runners.

That instability is the whole opportunity. If one business owned every AI's answer, there would be nothing to win. Because the named set is fragmented across six AIs now, a business can move from "named by one AI" to "named by all of them," and that movement is what we can engineer.

2. On pricing questions, one source often feeds every AI

This is the finding we keep coming back to. When we asked "dental implants Boulder cost," Perplexity, Claude, and Grok all anchored the single-implant number to the same source: a single Boulder dental practice's "$3,150 start to finish" pricing page. Not a directory or comparison site, one well-built pricing page. Every AI reached for it because it was the cleanest, most specific, most quotable number available.

Google AI Overviews then confirmed it from the inside. When we ran the same query on Google this round, the AI Overview block named that same practice's "$3,150 for a single tooth" bundled pricing alongside the broader range. So four AIs plus Google's own overview now converge on one well-built pricing page. That is the strongest version of this finding we have, and it is exactly the kind of convergence a business can earn its way into.

Gemini is the honest exception, and it points to where the work goes next. On both "dental implants Boulder cost" and "braces cost Boulder," Gemini returned generic national-style ranges and named no local practice and no comparison site at all. So owning the well-built pricing page wins Perplexity, Claude, Grok, and Google AI Overviews today, but it does not yet move Gemini, which is not pulling a single local cost source. Gemini looks like it is won by being a well-known, established business in Google's own index rather than by a cost page.

We found a related shape in orthodontics. Ask "braces cost Boulder" and the cost figures trace back almost entirely to a single directory or comparison site, though which one depends on the AI. Some AIs leaned on a local "dentists ranked" comparison site; Google AI Overviews leaned on national aligner-brand sites instead. Same mechanic, a different site in front per AI.

The lesson is blunt: pricing questions reward whoever publishes the clearest dated, number-dense page with the behind-the-scenes labels that help AIs read it, and most of the AIs will reach for it. Right now, in these categories, that is one source. There is room for exactly one more, and being that source means getting quoted by most of the AIs at once. Gemini is the reminder that no single page wins every AI, so a real program also works to make you a well-known, established name in Google's index.

3. Whether AI search is worth your time depends on your line of work

We checked each kind of business to see how the AIs answer for it. The question was simple: when someone asks an AI to recommend a business like yours, does it already have a fixed set of favorites it always names, or is there still room for a newcomer to become one of those names? The answer isn't the same from one industry to the next.

Your business	What the AIs do today	What it means for you
Dentist, "implant cost" (Boulder)	Every AI pulls the price from the same single website	The easiest win we found. Publish one clear, well-built pricing page and you can become a source the AIs quote.
Orthodontist, "braces cost" (Boulder)	One comparison site owns the price answer, and it's beatable	Almost the same easy opening as dental implants.
Med spa (Boulder)	The AIs don't agree on a favorite yet	Winnable by collecting more reviews and keeping your online listings accurate and complete.
Dentist, cosmetic / veneers (Boulder)	Each AI names a different handful of practices	Room to go from being mentioned by one AI to being mentioned by all of them.
Cosmetic / plastic surgery (Boulder)	Open, no clear favorite, but trust matters a lot here	A real opening, though slower, because the leaders already have hundreds of reviews.
LASIK / cataract eye surgery	One well-known clinic plus big national chains already hold the answer	Tougher. The early-mover advantage here is already taken, so it takes more work and more time. Not the first place to start.
Fertility / IVF	A couple of regional chains already hold the answer	Tougher. The early-mover advantage is gone, so winning here takes more work, more time, and more patience. Not the first place to start.
Personal injury law	Crowded with directories and heavy advertisers	Tougher. The ad and directory spend is entrenched, so it takes more work and a longer runway. Not the first place to start.

These three are specific verticals we tested and ruled out as the ones to chase first. That is not the same as "give up." It means the easy early-mover advantage in those categories has already been claimed, so they take more work, more time, and more patience to win. The smart play is to start where the ground is still open, build the wins and the proof there, and come back to the tougher categories with momentum.

The point for an owner is this: don't ask "can anyone win at AI search?" Ask "can I win in my industry, in my town, and where do I start?" For dentists and orthodontists asking about cost, the answer is yes, and cheaply, right now. For med spas, cosmetic dentists, and HRT clinics, the early-mover advantage is still sitting there for whoever grabs it first, and it will not sit there forever. For fertility clinics and injury firms, it is a longer climb, so we would not point your first dollar there. None of that means the door is shut. It means we would sequence the easy, open wins ahead of the hard, crowded ones.

4. The telehealth fear is unfounded, and the real competitor is the franchise

We expected AI to push national telehealth brands for "HRT near me" style queries. It did not. Across all six metros and every AI we tested, AI surfaced local, in-person clinics. Not one of the well-known national telehealth brands appeared in any local-intent answer. The answers are map-pack driven, so they pull brick-and-mortar.

Both of Google's products reinforced this directly this round. Google AI Overviews rendered an HRT block in five of the six metros (Austin returned a local 3-pack with no block), and every one named local in-person clinics by name. Gemini did the same for Boulder, naming local clinics only. No telehealth brand appeared on either Google product, in any metro. Interestingly, the two Google products named different local leaders for the same Boulder query, so even inside one company's two products the named set is not locked.

The real competition turned out to be multi-location franchises colonizing the local map pack. Where a TRT franchise had planted several locations (Austin, Phoenix), it crowded the answer. Where it had one or none (Nashville, Boulder), local independents owned the ground. The widest openings were Nashville and Boulder; the tightest were Austin (franchise lock) and Tampa (local giants with 500 to 1,000-plus review moats).

5. Different AIs reward different things

Grok visibly leaned on Reddit and forum chatter, surfacing individual practitioners by name because people recommended them in threads. Perplexity and Claude leaned on Google Maps review counts and directory and comparison-site listings. ChatGPT tracked closest to that Maps-and-directory layer and was the most likely to leave out a strong local business that had not built up those reviews and listings.

Adding Google's two AIs this round filled in the rest of the map. Google AI Overviews leans on top-ranking pages plus Google's own record of your business and the Maps layer, with a clear appetite for dated, number-dense pricing pages; it is the AI most likely to show up when there is a clean number or a settled set to summarize. Gemini, drawing on the same Google index, rewards how well-known and established your business is in that index, which is why its discovery answers favored long-established, highly-reviewed names and why it stayed generic on cost queries where it had no single local source to pull. Both Google products consistently named the Maps-prominent set and consistently left out the strong-but-quiet local names that Grok surfaces from Reddit.

This matters because "optimize for AI search" is not one job. Winning Grok is partly a show-up-genuinely-on-Reddit job. Winning Perplexity and ChatGPT is largely a reviews-and-listings job. Winning Claude rewards a clean, well-organized page. Winning Google AI Overviews rewards top-ranking content, the behind-the-scenes labels that help AIs read your page, and a dated number-dense page. Winning Gemini rewards being a well-known, established name in the Google index. Five different things to satisfy, one business to get cited.

6. Google AI Overviews shows up for cost questions, not for "best of" questions

This is the clearest new pattern from the Google data. We ran 20 queries through Google Search, and an AI Overview block rendered on 11 of them, about 55%. The split was not random. Every pricing question we ran triggered an overview: dental implant cost, braces cost, LASIK cost, and IVF cost. So did the discovery questions in consolidated, high-authority categories: fertility, and HRT in five of the six metros. But the fragmented Boulder discovery questions, "best med spa," "best cosmetic dentist," "best dentist for veneers," "best orthodontist," "best plastic surgeon," "best cosmetic surgeon," did not trigger an overview at all. Google fell back to the local 3-pack instead.

The read is simple. When there is a settled number or a settled set of names, Google summarizes it in an overview. When the field is genuinely fragmented, Google declines to summarize and hands you the map pack. For an owner, that is a tell about where the two games are. In categories where Google still shows the 3-pack, the fight is local SEO and reviews. In categories where the overview has already taken over, the fight is to be one of the few sources that overview is built from. Both are winnable, but they are not the same work, and the overview-vs-3-pack split tells you which one you are in.

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What this means for your business

We write these findings ROI-first, because the point is not to be interesting, it is to protect revenue.

1. When AI leaves you out, you slowly stop being considered, and nothing warns you. When AI names three businesses for "best [your category] near me" and you are not one of them, nothing breaks visibly. No alert fires. You simply stop being considered by the growing share of buyers who ask an AI before they ask a friend. The customers you lose this way never knew you existed, so you never see them in your "why didn't we close" review. The damage you cannot see is more dangerous than the kind you can.

2. The cheapest win is the pricing page. If you are in a category where one source currently feeds the cost answer, a single dated, number-dense pricing page, built with the behind-the-scenes labels that help AIs read it, can make you the second source the AIs pull from, across most of them at once. We watched Perplexity, Claude, Grok, and Google AI Overviews all reach for one practice's implant figure. This is the highest-probability, lowest-cost AI-citation win we found in the entire study. For a dentist or orthodontist, that is real traffic from people who are still deciding, captured for the cost of one well-built page. The one caveat: Gemini stayed generic on cost and did not pull a local source, so the pricing page wins four of the AIs we tested but not yet Gemini, which is a reason to pair it with the work in point 5 of becoming a well-known, established name.

3. Pick your battle by category, and sequence it. If you are a fertility clinic or a personal injury firm, AI search is a tougher, longer climb, because the early-mover advantage in those categories is already taken by entrenched regional names and heavy ad and directory spend. We would not point your first marketing dollar there. That is not "give up," it is "start somewhere easier and come back with momentum." If you are a med spa, dentist, orthodontist, or HRT clinic in a city without many big chain competitors, the ground is open right now and the early-mover advantage is still there for the taking. It will not stay open. The businesses that move first get cited; the ones that wait have to overcome the big stacks of reviews the early movers built. So grab the open verticals now, while you still can.

4. Your competitor is probably a chain, not a national app. For local health and wellness, the threat AI surfaces is the multi-location chain planting flags in your map pack, not a telehealth brand. The defense is local: a well-kept Google Business Profile, a steady stream of new reviews, and local content. That is winnable for an independent who moves before the chain does.

5. There is no single "AI optimization" lever. Because each AI rewards different things, a real program works on reviews and listings (Perplexity, ChatGPT), clear pages with the behind-the-scenes labels that help AIs read them (Claude and Google AI Overviews), a genuine presence on Reddit and forums (Grok), and becoming a well-known, established name in the Google index (Gemini) at the same time. We watched the same Boulder query return a different named set on five AIs, and even Google's own two products disagreed with each other. Anyone selling you a one-trick fix is selling you one AI's answer.

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Suggested visualizations

[CHART: AI agreement matrix. Rows = the businesses named for "best med spa in Boulder" (anonymized as Med Spa A, B, C and so on). Columns = Perplexity, Claude, Grok, ChatGPT, Gemini, Google AI Overviews. Filled cell = named by that AI. Visually shows how fragmented the named set is and how no business fills the whole row. Gemini's column is almost entirely its own names, and the Google AI Overviews column is empty because no overview rendered for this query.]

[CHART: "One source feeds the answer" diagram. Center node = a single pricing page. Arrows out to Perplexity, Claude, Grok, and Google AI Overviews all citing the same "$3,150" number. Gemini sits to the side with a dashed line and a "generic ranges only, no local source" label. A second, empty node labeled "the open seat" sits beside it to show the capturable opening.]

[CHART: AI Overview trigger rate. A simple split graphic: of 20 Google queries, 11 rendered an AI Overview, 9 returned a local 3-pack. Group the 11 by what triggered them (all four cost queries; fertility and five-of-six HRT metros) versus the 9 that did not (the fragmented Boulder "best of" discovery queries). Annotation: "Cost questions get an overview. Fragmented 'best of' questions get the map pack."]

[CHART: Vertical openness ladder. Horizontal bar per vertical, sorted from most open (dentistry implant cost) to toughest (fertility, personal injury), color-coded green at the open end to amber at the tougher end. The tougher categories are labeled "early-mover advantage already taken, longer climb" rather than "closed." One glance shows where to start first and where the longer game is.]

[CHART: HRT across six metros. Small map or bar set showing local-independent visibility vs chain dominance per metro, with Nashville and Boulder at the open end and Austin and Tampa at the locked end. Annotation: "Zero telehealth brands appeared in any local answer, on any of six AIs including both Google products."]

[CHART: Signal-by-AI table styled as a graphic. Columns: Reviews and listings, Clear pages with the behind-the-scenes labels that help AIs read them, Reddit and forum presence, Being a well-known name in the Google index. AI logos placed under the signal each one rewarded most: Perplexity and ChatGPT under reviews, Claude and Google AI Overviews under clear labeled pages, Grok under Reddit and forum presence, Gemini under well-known name.]

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A note on who ran this

This study was produced by the SEO Believer research team. Our work goes back to the early commercial internet: our founder, Annette Thompson, has been getting organizations found in search since 1995, and most recently led the SEO and operations that took a nonprofit from a Domain Rating of 0.9 to 62 with no paid advertising, alongside the placement of more than 4,000 dogs into adoptive homes.

Her background is in Medical Technology and Biochemistry, and it shows in how we work. We run campaigns with a scientific approach: form a hypothesis, query the AIs directly, capture the raw answers, and report what actually happened rather than what the trend pieces predict. We would rather show you a small, honest, reproducible result than a large, confident, unfalsifiable one. This is the first installment of an ongoing measurement, and we will update it as the AIs, and the answers, change.

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Want to know who AI search names in your category and city? That is the question we answer first on every engagement. The whitespace closes as more businesses wake up to it, so the read is most valuable now.