A Los Angeles med spa gets cited by ChatGPT and Google AI Overviews the same way it earns strong traditional rankings: specific, factual treatment content, complete schema markup, and a fast, crawler-friendly technical foundation — not a separate set of “AI search” tricks. Research on AI citation patterns backs this up directly: Digital Applied’s study of 1,000 Google AI Overviews found schema-marked pages are cited about 2.3 times more often than unstructured equivalents.
By the numbers: Google AI Overviews now appear on roughly a quarter of all searches, and ChatGPT reported 900 million weekly active users in early 2026 (Similarweb). Wellows identifies seven measurable factors behind AI Overview selection, including semantic completeness and structured data markup (Wellows), and schema-marked pages are cited about 2.3x more often than unstructured equivalents (Digital Applied).
Someone researching a med spa in Los Angeles is increasingly likely to start that research inside ChatGPT or see a Google AI Overview before they ever click through to a traditional list of search results. If your practice’s website isn’t structured in a way those systems can confidently read and cite, you’re invisible at exactly the moment a prospective patient is forming their shortlist — regardless of how well the site ranks in classic blue-link search.
AI search isn’t a rounding error anymore
The scale here has shifted quickly enough that it’s worth taking seriously even for a single-location practice. ChatGPT alone reported roughly 900 million weekly active users by early 2026, more than double the figure from a year earlier, and Google’s AI Overviews now appear on roughly a quarter of all searches (Similarweb, 2026 AI search data).
900M
ChatGPT weekly active users, early 2026 (Similarweb)
~25%
Of Google searches now show an AI Overview (Similarweb)
61%
Drop in organic click-through rate on searches that trigger an AI Overview (Wellows)
That last figure cuts both ways. When an AI Overview answers the query directly, far fewer users click through to any website (Wellows, 2026 AI Overview ranking factors guide) — which makes being the source that Overview or a chatbot actually cites far more valuable than ranking third or fourth in the traditional results below it.
What determines whether ChatGPT and AI Overviews cite a med spa?
Semantic completeness and structured data markup are two of the most actionable documented drivers of AI Overview selection, out of seven measurable factors identified in a large-scale ranking-factors analysis of 15,847 AI Overview results. For a med spa website, that means semantic completeness (the ability to answer a question fully without requiring an external reference, correlation of 0.87) and structured data markup, which the same analysis associates with a 73% lift in selection rate. It also reports a 156% higher selection rate for multi-modal content (text plus images) versus text-only, and an 89% higher selection probability for content with real-time factual verification (Wellows, 2026 AI Overview ranking factors guide).
| Item | Value |
|---|---|
| Multi-modal content (text + images vs. text only) | 156% |
| Real-time factual verification (citable sources) | 89% |
| Structured data / schema markup | 73% |
Source: Wellows, 2026 AI Overview ranking factors guide
The schema effect specifically has been measured more than one way with consistent direction: Digital Applied’s study of 1,000 Google AI Overviews found schema-marked pages are cited about 2.3x more often than unstructured equivalents (Digital Applied, 2026 citation pattern study of 1,000 AI Overviews). It isn’t enough to add schema at all, though; the schema needs to actually be complete and specific, with real service, price, and FAQ attributes filled in.
Includes specific service, price & FAQ attributes
- Attribute-rich schema
- Generic / minimal schema
Machine-readable business & review data
- Attribute-rich schema
- Generic / minimal schema
| Attribute-rich schema | Generic / minimal schema | |
|---|---|---|
| Includes specific service, price & FAQ attributes | ||
| Machine-readable business & review data |
Why don’t most med spa websites get cited by AI?
Most med spa websites don’t get cited by AI answer engines because they are built on heavy page-builder themes with vague service descriptions (“transform your look with our advanced treatments”) instead of specific, extractable facts. AI answer engines cite sources that give them something concrete to quote — a specific treatment name, a real price range, a clear answer to a specific question — not marketing copy that reads well to a human but says very little that’s structured or specific.
The practical gaps we see most often:
- Thin or vague treatment pages that don’t state what a treatment actually involves, typical session length, or a realistic price range.
- No FAQ content answering the actual questions patients ask — “how long does filler last,” “is Botox painful,” “how much does a HydraFacial cost in LA” — in a direct, quotable format. List-formatted content is easy for AI Overviews to extract, and plain FAQ-style Q&A pairs deliver it naturally.
- Missing or incomplete schema markup, which gives crawlers and AI systems structured signals about what a page actually is (a service, a business, a review) rather than making them infer it from unstructured text.
- JavaScript-heavy builds that are harder for some crawlers to render fully, meaning content that’s genuinely on the page never actually gets seen by every crawler pulling data for these answer engines.
The fixes that move both AEO and traditional SEO together
- Write specific, factual treatment content. Real session length, realistic price ranges, and honest descriptions of what a treatment does and doesn’t do — the same specificity that builds patient trust also gives AI systems something concrete to extract and cite.
- Build real FAQ sections around actual patient questions, not generic filler — structured as clear question-and-answer pairs, which both humans and AI systems parse easily.
- Implement full, attribute-rich schema markup: MedicalBusiness/LocalBusiness, Service, and FAQPage schema with specific attributes filled in — not just the schema type declared with minimal fields.
- Build on a fast, crawler-friendly technical foundation. A static-first framework like Astro — the basis of our Astro website design service — ships minimal JavaScript, which means content is present in the initial HTML rather than requiring a crawler to execute scripts to see it — directly relevant to how reliably AI crawlers can access your content at all.
- Structure content as lists and direct answers wherever it fits naturally — numbered steps for a treatment process, bullet summaries of what’s included, since list-formatted content is easy for AI systems to lift into a generated answer.
- Keep information current. An AI system citing outdated pricing or a discontinued treatment erodes trust fast — treat your service pages as living documents, not a one-time launch task.
Do E-E-A-T signals still matter for AI search?
Yes — authority is one of the seven AI Overview citation factors identified in the research above, and that analysis reports that 96% of AI Overview content comes from sources with strong E-E-A-T signals. For a med spa, that translates concretely — provider credentials, licensing information, and before/after documentation (where appropriate and compliant) aren’t just trust-building for patients, they’re a measurable input into whether an AI system treats your site as a credible source to quote at all, rather than skipping it in favor of a competitor with clearer authority signals.
We go deeper on the full AEO/GEO framework, including how it interacts with classic Google ranking factors, in our complete AEO/GEO explainer.
How AI crawlers actually access your site — a technical look
Understanding why technical foundation matters requires knowing, roughly, how these systems gather information in the first place. Most AI answer engines rely on a combination of their own crawlers (OpenAI’s GPTBot, Google’s crawlers powering AI Overviews) and, in some cases, real-time retrieval at the moment a user asks a question. Both paths share a common requirement: the content needs to be present and parseable without requiring complex JavaScript execution.
This is where a heavy, client-side-rendered WordPress page builder can quietly work against a med spa, even if the visual result looks identical to a lighter-weight build. If treatment details, pricing, or FAQ content are injected into the page via JavaScript after initial load — common with some page-builder plugins and single-page-application-style themes — a crawler that doesn’t fully execute that JavaScript may see a largely empty page, regardless of how complete the content looks to a human visitor with a fully loaded browser.
A static-first framework like Astro sidesteps this entirely: the meaningful content is present in the initial HTML response, before any JavaScript runs at all, which means every crawler — regardless of how sophisticated its rendering pipeline is — sees the same complete content a human visitor does. It’s the same architectural difference behind the mobile performance gap between Astro and WordPress.
A quick technical test: view your page’s source (not the rendered page, the raw HTML) and search for your actual treatment pricing or FAQ text. If it isn’t there in the raw source, some AI crawlers may not be seeing it either, regardless of how it renders in a browser.
A maintenance cadence for staying AI-citable over time
AEO isn’t a one-time technical project — content that was accurate and well-structured at launch drifts out of date as pricing, staff, and treatment offerings change, and an AI system citing stale information erodes trust faster than simply not being cited at all. A sustainable maintenance rhythm looks like:
- Monthly: spot-check that pricing and treatment details on your top three to five most-searched pages still match reality.
- Quarterly: re-run the ChatGPT and AI Overview citation checks described below, and compare against the previous quarter to see whether visibility is improving, stable, or slipping.
- Whenever a treatment or pricing structure changes: update the relevant schema and page content the same week, not as part of a periodic batch — an AI system has no way to know your Botox pricing is three months stale unless the page itself reflects the change.
AEO maintenance cadence for a med spa website
| When | What to check or update | Why it matters for AI citation |
|---|---|---|
| Monthly | Pricing and treatment details on the top three to five most-searched pages | Prevents AI answers from quoting stale prices or discontinued treatments |
| Quarterly | Re-run ChatGPT and AI Overview citation checks and compare with last quarter | Shows whether AI visibility is improving, stable, or slipping |
| Whenever a treatment or price changes | Relevant schema markup and page content, the same week | AI systems only know about a change once the page reflects it |
Common AEO misconceptions worth retiring
“AEO requires a completely separate website or content strategy.” In practice, the same specific, well-structured, schema-marked content that serves traditional SEO and genuinely informs patients also serves AI answer engines — treating AEO as a bolt-on side project usually produces worse results than simply doing the core content work well once.
“More schema is always better.” Volume matters less than accuracy and completeness — a smaller set of correctly implemented, attribute-rich schema types gives AI systems more usable facts than a large amount of generic, minimally filled-out schema.
“AI citation is purely a technical SEO problem.” Authority signals (credentials, licensing, genuine patient trust indicators) characterize the vast majority of AI Overview sources in the research covered earlier — a technically perfect but thin, low-authority site still underperforms a slightly less technical site backed by real expertise signals.
“Once you’re cited once, you’ll keep being cited.” AI answer engines re-evaluate sources continuously as their own crawling and training data updates — a practice that stops maintaining accurate, current content can lose citation visibility even after achieving it once, which is exactly why the maintenance cadence above matters as much as the initial build.
A note for multi-location med spa groups
Everything covered above applies per location, not just brand-wide — an AI system answering “best med spa near [specific LA neighborhood]” needs location-specific schema (address, hours, and location-specific service availability) to cite the correct branch confidently, not just a generic brand-level description. A multi-location group that consolidates all locations onto one generic “about us” style page, without distinct, complete schema and content per location, makes it genuinely harder for an AI system to know which specific branch to recommend for a geographically specific query — the same principle that drives local SEO map-pack visibility for service businesses applies directly to AI citation for multi-location practices.
Voice search and conversational AI queries
A related but distinct pattern worth planning for is voice-based and conversational search — someone asking a smart speaker or voice assistant “what’s a good med spa near me for microneedling” phrases the query very differently than a typed search. Voice queries tend to be longer, more conversational, and more likely to include a direct question format. Content written to directly answer full, natural questions (structured as clear question-and-answer pairs, as already recommended for AEO generally) tends to perform well for both voice search and chat-based AI answer engines simultaneously, since both are fundamentally trying to extract a direct, spoken-friendly answer from written content rather than a list of blue links to sift through manually.
A quick self-audit for AI citability
Before commissioning a rebuild, it’s worth checking where your current site actually stands:
- Ask ChatGPT and a Google AI Overview a realistic patient question — “best med spa for lip filler in LA,” for example — and see whether your practice shows up, and whether the details cited (pricing, services) are accurate.
- Open your top three treatment pages and count concrete facts. Real price range? Real session length? A specific description of what happens during the treatment? If the copy could apply to any med spa in any city, it’s too vague to cite confidently.
- Check whether your FAQ sections use FAQPage schema, not just visually formatted question-and-answer text — the schema is what makes the structure machine-readable, not just human-readable.
- Run your site through a JavaScript-disabled view (or a text-only browser extension) to see roughly what a crawler that doesn’t fully render JavaScript would see. If your key content disappears, that’s a real accessibility gap for some AI crawlers.
- Check your Google Business Profile and review the completeness of your listed services, since local business schema and GBP data both feed into how confidently an AI system can describe your practice.
Most med spa sites fail on the same point: treatment pages that read well but say almost nothing specific enough to quote. That single fix — adding real prices, real session lengths, and real specifics — tends to move both AI citation and traditional conversion rate at the same time, since specificity is exactly what builds patient trust too.
Bringing it together
Being cited by ChatGPT or an AI Overview isn’t a separate marketing channel to chase with special tricks — the research is fairly clear that it’s largely a byproduct of having specific, well-structured, technically accessible content with complete schema, which happens to also be exactly what ranks well in traditional search. A Los Angeles med spa that fixes vague copy, adds attribute-rich schema, and builds on a fast, crawler-friendly foundation is positioning itself for both forms of visibility at once, rather than treating AI search as a future problem to solve later. If you’d rather hand that build off, our website package pricing starts at $97 per month.
Written by the Hireaghlva GoHighLevel team
We build and run GoHighLevel accounts, automations, dashboards, and websites for agencies and local businesses across the US and Canada. This guide reflects that hands-on work, with sources cited inline where we reference outside data. Last reviewed September 13, 2026.
