Strong with patients and reviewers — but a legacy website that gives AI almost nothing structured to extract or cite.
Your future patients have stopped scrolling ten blue links. They open ChatGPT, Perplexity, or Google's AI Overview and ask one question — "What's the best med spa near Willow Bend for Botox?" — and act on the single answer the AI hands back.
You've spent nearly two decades building a 4.7-star reputation under a board-certified medical director. The risk now isn't that patients dislike Avanti — it's that the AI never brings you up, because a competitor's site is far easier for it to read. The clinics that get cited over the next few months will compound a lead that's hard to unseat. In your market, almost no one has claimed it yet.
Each category is weighted by how much answer engines rely on it when assembling and attributing a recommendation. Your points come almost entirely from real-world reputation; the machine-readable layers an AI actually reads are near-empty.
| Category | Score |
|---|---|
| Structured data (schema.org / JSON-LD) | 0 / 25 |
| Machine-readable FAQ / Q&A | 0 / 15 |
| Indexable pricing + treatment detail | 9 / 20 |
| llms.txt / AI-crawler signals | 1 / 10 |
| Reviews / authority | 11 / 15 |
| Presence in AI-cited listicles | 2 / 15 |
| Total AI Visibility Score | 23 / 100 |
The two highest-weighted machine-readable categories — structured data (25) and indexable specifics (20) — are where you have the most ground to gain. Your reviews score is genuinely strong; the problem is that almost none of it is presented in a form an AI can lift and attribute to you.
The fundamentals patients see are real: a 4.7-star rating with hundreds of reviews (≈455 on Chamber of Commerce, 61 on Yelp, 302 on Demandforce), BBB accreditation since 2012, and a board-certified medical director, Dr. Niti Randhawa (Family Medicine, ~20 years). But when an answer engine builds a recommendation, it doesn't read your reputation — it reads your structured facts: which services, where, by whom, at what price, with what proof. On that axis, avantiwillowbend.com — a legacy .htm template site — gives the AI very little it can lift and confidently attribute to you.
We reviewed the homepage and the injectables treatment page — neither carries any LocalBusiness, MedicalBusiness, Service, or MedicalProcedure markup, and no <script type="application/ld+json"> at all. This is the machine-readable layer AI engines trust most to extract who you are, what you treat, and where. Without it, your Frisco location and service menu are guesswork to a crawler. (0 / 25)
There's no FAQ content carrying FAQPage schema. Answer engines pull patient-question answers — recovery time, "does Botox hurt," candidacy, how long filler lasts, cost — straight from FAQ markup. You have none, so those high-intent answers get sourced from competitors who marked theirs up. (0 / 15)
This is your best foundation: services-injectables.htm names and describes ~11 injectables (Botox, Dysport, Juvéderm, Voluma, Restylane line, Kybella, PRP, sclerotherapy), and a facials page exists too. But depth is wildly uneven (Dysport gets ~15 words, sclerotherapy ~280), there's no pricing in indexable text, and no recovery/candidacy detail. An AI literally cannot answer "how much is Botox at Avanti?" — one of the most common buyer prompts — so it forfeits you. (9 / 20)
There's no llms.txt and no signal telling AI crawlers which pages and facts to prioritize. On a legacy .htm builder, even basic crawler hygiene is thin. It's the cheapest, fastest win in AEO and you don't have it. (1 / 10)
Your single best reason to be cited for a medical query — a board-certified medical director, Dr. Niti Randhawa — does not appear on the homepage or treatment pages in attributable text. AI weights expertise and trust signals heavily for health topics. That credential is documented in directories and your reviews, but it's absent from the pages a crawler reads as yours. (reviews/authority: 11 / 15)
For Plano/Frisco aesthetic queries, answer engines lean on what they can parse cleanly: structured clinic sites, high review counts, and the "best med spa" roundups. In our June 2026 searches for "best med spa Frisco TX" and "med spa Willow Bend," the names that surface first and repeatedly are U Med Spa (1,000+ Google reviews, "leading med spa since 2007"), Starwood Med Spa, Novuskin, and Geneva — pulled from listicles like ThreeBestRated, Rumi Aesthetics' "5 Best," and the Yelp top-10. Avanti did not appear in any of those curated lists. You exist in directory profiles (Yelp, BBB, Demandforce), which is why you score a 2 rather than a 0 — but you're being filed, not recommended. With no structured layer and no published specifics, even a clinic an AI could rank on quality and price gets left out of the answer entirely. (AI-cited listicles: 2 / 15)
The guarantee: every word we publish is grounded only in facts you verify — your real services, prices, and the actual credentials of your team. A verification step rejects anything unsupported before it ships. No invented claims, ever. That's the difference between AEO and the "compliance widget" vendors.
A 15-minute call. We'll show you the exact pages and questions to claim first — and where U Med Spa, Starwood, and Novuskin are already ahead in AI answers near Willow Bend.
Book a 15-minute AI visibility callservices-injectables.htm) — for the signals AI answer engines use to extract and cite a business: schema.org / JSON-LD structured data, FAQ markup, indexable pricing and treatment detail, llms.txt and crawler signals, and surfaced provider credentials. We cross-referenced this with how those engines currently assemble responses to "best med spa Frisco TX" and "med spa Willow Bend" queries — review counts, the board-certified medical director, and named competitors are drawn from live search results and public directories (Yelp, BBB, Chamber of Commerce, Demandforce), not assumptions. The 0–100 score is weighted: structured data 25, machine-readable FAQ/Q&A 15, indexable pricing + treatment detail 20, llms.txt/crawler signals 10, reviews/authority 15, presence in AI-cited listicles 15. Findings reflect what was publicly visible on that date; a full engagement includes live, repeated multi-engine probing for your specific treatment + location queries.