How it actually works. Step by step.Start to finish.
The Client Results page shows what happened. This page shows how it happened. Three detailed case studies documenting the full ARO process from initial audit through ongoing AI visibility, so you can see exactly what working with Yazeo looks like from the inside.
Am I on ChatGPT? Find out.Case study: Cosmetic surgery practice goes from zero AI mentions to ChatGPT's top recommendation in their market
Industry
Cosmetic surgery and med spa
Market
Major metro, Southern US
Timeline
6 months documented
Starting AI visibility
Zero across all platforms
1. The situation
2. The audit findings
3. What we built
4. The outcome
The situation
A well-established cosmetic surgery practice with three board-certified surgeons and a med spa division generating over $5 million in annual revenue. The practice had invested heavily in Google Ads ($15,000+/month), had a visually impressive website, and maintained a strong patient referral network.
The problem nobody had identified: when prospective patients asked ChatGPT "best cosmetic surgeon in [their city]," "rhinoplasty surgeon near me," or "top med spa for Botox," the practice didn't exist in the AI answer. Three competing practices appeared consistently, including one that had opened just two years earlier with half the surgical experience.
The managing partner discovered this by accident during a personal ChatGPT search and called us the next day.
The audit findings5 critical gaps
Our AI Visibility Audit revealed five specific gaps:
Gap 1: content
The practice's website was visually stunning but text thin. Beautiful photos. Minimal written content. The "Rhinoplasty" page had 120 words and a photo gallery. A competing practice's rhinoplasty page had 1,800 words answering every question a patient might ask AI: cost, recovery time, candidacy, technique options, and surgeon qualifications. AI had ten times more content to evaluate from the competitor.
Gap 2: reviews
The practice had 89 Google reviews (4.7 stars) but almost none mentioned specific procedures. The competing practice had 210 Google reviews plus 145 Real Self reviews, many describing specific procedures and outcomes in detail. AI uses this procedure-specific review text to match businesses with procedure-specific queries.
Gap 3: platform presence
The practice existed on Google and their own website. The competitor existed on Google, Real Self, Healthgrades, Vitals, Yelp, the American Society of Plastic Surgeons directory, their state medical board directory, and three local business directories. AI had eight sources confirming the competitor's existence and two confirming our client's.
Gap 4: schema
No structured data of any kind. The competitor had Medical Business, Physician, and Medical Procedure schema across their entire site.
Gap 5: third-party validation
Zero media mentions. Zero professional directory features beyond basic listings. The competitor had been quoted in a local health magazine, featured on a "Best Doctors" list, and mentioned in two local newspaper articles about cosmetic surgery trends.
What we built (month by month)
Month 1: foundation
Rewrote and expanded 14 procedure pages. Each page went from 100 to 200 words to 800 to 1,200 words answering the questions patients ask AI: "How much does rhinoplasty cost?", "What's the recovery time for a breast augmentation?", "Am I a good candidate for a facelift?", "What's the difference between Botox and fillers?" Cost guidance was directional (ranges, not exact prices) to remain compliant with medical advertising standards.
Built individual surgeon credential pages documenting board certifications, fellowship training, procedure specializations, and years of experience. Previously, the three surgeons shared a single "Our Team" page with headshots and one-sentence bios.
Implemented Medical Business, Physician, Medical Procedure, and FAQ schema across the entire website. Claimed and completed profiles on Real Self, Healthgrades, Vitals, the ASPS directory, and three local directories. Synchronized all business information across every platform.
Month 2: review acceleration
Launched a systematic review strategy. Every post-procedure patient received a follow-up communication with direct links to Google and Real Self. The prompt encouraged specificity: "If you're comfortable sharing, mentioning which procedure you had and what your experience was like helps future patients making similar decisions."
Within 30 days: 35 new Google reviews and 18 new Real Self reviews. Many mentioned specific procedures and outcomes. The review text gave AI the procedure-level data it was missing.
Month 3: authority building
Pitched the lead surgeon as an expert source to two local health publications. Result: one published quote in a "cosmetic surgery trends" article and one full feature on non-surgical facial rejuvenation.
Enrolled the practice in two professional association public directories they'd never claimed. Published an educational content piece: "Cosmetic Surgery vs Non-Surgical Treatments: How to Decide What's Right for You." This captured the comparison queries patients ask AI before they've decided on a specific procedure.
Month 4: first AI appearances
ChatGPT began recommending the practice for "rhinoplasty surgeon [city]" queries. The recommendation specifically cited the surgeon's fellowship training and patient review feedback, both signals we'd built. Google AI Overviews included the practice for "cosmetic surgeon [city]" for the first time.
Month 5 to 6: expansion and compounding
AI recommendations expanded to four procedure types: rhinoplasty, breast augmentation, Botox, and facial rejuvenation. Perplexity began citing the "surgical vs non-surgical" comparison page with a direct link.
New patients specifically mentioning AI as their discovery source became a monthly occurrence. The practice began tracking "How did you hear about us?" with AI as a distinct category.
The outcomeResults summarized
AI visibility
Zero mentions to consistent recommendations across ChatGPT, Google AI Overviews, and Perplexity for four procedure types within six months.
Review growth
89 Google reviews to 190+. Zero Real Self reviews to 65+. Procedure-specific review text increased from virtually none to many new reviews.
Platform presence
2 platforms to 12 platforms with consistent information.
Business impact
AI-referred patient consultations in the first six months represented a revenue potential that exceeded the full annual ARO investment multiple times over.
"We were spending $15,000 a month on Google Ads and getting good results. But we had no idea an entirely separate channel was sending patients to our competitors. The AI investment was a fraction of our ad spend and it opened a channel we didn't know existed. The patients who come through AI are different. They arrive educated. They've read about our surgeons. They know what procedure they want. The consultation is more productive, and the conversion rate is higher than any other channel."— Managing Partner
Case study: Personal injury law firm captures AI recommendations in one of the most competitive legal markets in the country
Industry
Personal injury law
Market
Top-10 US metro, highly competitive
Timeline
9 months documented
Starting AI visibility
Zero despite strong Google rankings
1. The situation
2. The audit findings
3. What we built
4. The outcome
The situation
A 12-attorney personal injury firm handling motor vehicle accidents, medical malpractice, workplace injuries, and wrongful death cases. The firm invested over $30,000 per month in Google Ads and SEO. They ranked on page one for their primary keywords. They had a recognizable TV advertising presence.
None of it mattered on ChatGPT. When prospective clients asked, "best personal injury lawyer in [city]" or "car accident attorney near me," two competing firms appeared, neither of which ranked as highly on Google or had as strong a trial record. The firm discovered this when a prospective client mentioned during a consultation that they'd initially contacted a competitor because "ChatGPT recommended them" but decided to seek a second opinion.
That single lost-then-recovered client interaction triggered the engagement.
The audit findings
Gap 1: content structure
The firm's website was designed for Google Ads conversion: short pages, big phone numbers, "Call Now" buttons. Effective for paid traffic. Invisible to AI. The pages had minimal text explaining the legal process, case types, or what distinguishes the firm. AI had nothing substantive to evaluate.
Gap 2: entity fragmentation
The firm had different information across Google, Avvo, Martindale-Hubbell, their website, and the state bar directory. Two name variations. Two phone numbers. An old address on one platform. AI couldn't confidently confirm basic facts about the firm.
Gap 3: attorney credentials undocumented
12 attorneys with strong credentials, but the website listed them with photos, titles, and one-line bios. Bar admissions, notable verdicts, published articles, speaking engagements, and professional association leadership were nowhere on the site. AI had no credential data to evaluate.
Gap 4: review quality
230 Google reviews (4.8 stars), but most said, "great lawyer, highly recommend." Few mentioned specific case types or outcomes. The competing firm had 180 reviews, but many described specific accident scenarios, settlement amounts (directionally), and the legal process experience.
Gap 5: no educational content
No blog. No FAQ. No "what to do after a car accident" guide. No "how much is my case worth" resource. These are the exact queries injury victims type into ChatGPT, and the firm had zero content addressing them.
What we built (month by month)
Months 1 to 2: complete content overhaul
Built 18 new pages. Case-type pages for motor vehicle accidents, truck accidents, motorcycle accidents, medical malpractice, workplace injuries, wrongful death, and premises liability. Each page explained the legal process specific to that case type, typical timelines, and what makes the firm qualified.
Built individual attorney pages with full credentials: bar admissions, notable case results, publications, speaking engagements, and professional leadership positions.
Created five educational guides including "What to Do After a Car Accident in [State]" and "How Much Is My Personal Injury Case Worth?" Implemented Legal Service, Attorney, and FAQ schema across the entire website.
Months 2 to 3: entity cleanup and platform expansion
Fixed every data inconsistency across Google, Avvo, Martindale-Hubbell, FindLaw, Justia, the state bar directory, Super Lawyers, Best Lawyers, and local directories. Synchronized one name, address, and phone number everywhere. Claimed and completed profiles on four new platforms.
Months 3 to 4: review strategy and authority
Launched review strategy encouraging case-type-specific feedback. Pitched lead trial attorney to local media outlets, resulting in expert quotes and contributed articles in regional legal publications.
Months 4 to 9: expansion and breakthrough
Perplexity began citing the firm's educational guides directly. ChatGPT followed with car accident attorney recommendations. By month 9, recommendations expanded to five separate case types including medical malpractice. Google AI Overviews featured the firm for the first time.
The outcome
AI visibility
Zero to consistent recommendations across ChatGPT, Google AI, and Perplexity for five case types within nine months.
Content growth
4 thin pages to 23 substantive pages plus 5 educational guides.
Platform presence
Inconsistent data across 6 platforms to consistent data across 14 platforms.
Review quality
Generic reviews to case-type-specific reviews with meaningful detail.
AI-referred case enquiries within the first nine months included one medical malpractice case and several motor vehicle accident cases. In personal injury, a single medical malpractice case can generate fees of $100,000 to $500,000+.
"The return on this investment is unlike anything else in our marketing budget. One case paid for years of this service."— Managing Partner
Case study: multi-location dental group builds AI visibility across five markets simultaneously
Industry
Dental (multi-location)
Market
Five suburban markets, Southeast US
Timeline
8 months documented
Starting AI visibility
Zero across all locations and all platforms
1. The situation
2. The audit findings
3. What we built
4. The outcome
The situation
A dental group operating five practices across suburban communities in a major Southeast metro. Services included general dentistry, cosmetic dentistry, Invisalign, dental implants, pediatric dentistry, and emergency dental care. Combined patient base of over 15,000 active patients.
The group had invested in a centralized website with location pages for each practice. Each location page had the address, phone number, a photo of the building, and a paragraph about the team. Functional for existing patients. Invisible to AI.
When prospective patients asked ChatGPT "dentist in [any of the five suburbs]," none of the five locations appeared. In each suburb, one or two competitors with smaller practices but stronger digital evidence dominated the AI recommendations.
The group's CEO discovered this when their marketing director ran a competitive AI audit as part of a broader marketing review.
The audit findings
Gap 1: Centralized website killed location-specific AI visibility
One website with thin location pages meant AI couldn't distinguish between the five practices. Each location needed to function as a distinct entity with its own content, its own reviews, and its own local signals. AI evaluates each location independently.
Gap 2: Reviews were aggregated, not location specific
The group had 320 Google reviews, but they were spread across five Google Business Profiles unevenly (one location had 140, another had 22). Most reviews didn't mention the specific location or dentist. AI needs location-specific review data to recommend a specific location for a specific area query.
Gap 3: No procedure-specific content
"Dental implants" was mentioned on a services dropdown menu. It wasn't a dedicated page explaining what implants involve, who's a candidate, what they cost, and what to expect. Same for Invisalign, cosmetic dentistry, and every other service. AI had no procedure-level content to evaluate.
Gap 4: Minimal local platform presence
Each location had a Google Business Profile and nothing else. No Healthgrades. No Vitals. No local chamber listing. No dental association directory. Each competitor appearing in AI had five to eight platform presences per location.
What we built (month by month)
Month 1 to 2: location-level content architecture
Built dedicated content hubs for each of the five locations. Each hub included: a comprehensive location page describing the specific practice, its team, its community, and its specializations. Procedure pages for every service offered at that location. FAQ content addressing the questions patients in that specific suburb ask. A "Meet Your Dentist" page for each provider at that location with credentials, specializations, and personal background.
Total new pages across five locations: 72.
Implemented Local Business and Dentist schema for each location independently, with location-specific service descriptions and geographic coordinates.
Month 2 to 3: location-specific review strategy
Built a review generation system that routed patients to the correct location-specific Google Business Profile (preventing reviews from landing on the wrong location). Added Healthgrades profiles for each location.
Review prompts encouraged location and procedure specificity: "If you could mention which office you visited and what treatment you received, that helps other patients in your area find the right care."
Within 60 days: the lowest-reviewed location went from 22 to 68 Google reviews. The highest went from 140 to 210.
Month 3 to 4: local authority building
Each location joined its local chamber of commerce (five separate chamber memberships). Two locations earned mentions in community newsletters. One dentist was featured in a local parenting magazine article about children's dental health. Built profiles on Healthgrades, Vitals, and the state dental association directory for each location.
Month 4: first AI appearances
The flagship location appeared in ChatGPT responses for "dentist in [suburb]" and "dental implants [suburb]." Google AI Overviews featured two locations for general dentistry queries.
Month 5 to 8: cascading results
By month 6, three of five locations appeared in at least one AI platform. By month 8, all five appeared. The procedure-specific content drove recommendations for high-value services: "Invisalign in [suburb]" and "dental implants [suburb]" queries began returning the relevant location.
The outcome
AI visibility
Zero across all five locations to all five appearing on at least one AI platform within eight months. Three locations appearing on multiple platforms for multiple query types.
Content
5 thin location pages to 72 substantive pages across the group.
Reviews
Uneven distribution (22 to 140 per location) to balanced, growing profiles (68 to 210+) with procedure-specific detail.
Platform presence
Google-only to Google, Healthgrades, Vitals, dental association, and local chamber directories across all five locations.
New patient registrations mentioning AI discovery became a measurable segment across all five locations by month six. The group's CEO noted that AI-referred patients skewed toward high-value services (implants and Invisalign) rather than routine cleanings, suggesting that AI captures patients at the treatment-research stage rather than the maintenance stage. This high-value patient profile made the per-location ROI clear within the first year.
What these three case studies reveal
Every case study started with the same discovery: a good business, invisible to AI, losing customers to competitors with weaker services but stronger digital evidence.
Every case study followed the same methodology: audit the gaps, build the evidence across all five signal categories, monitor the results, and adjust based on what AI shows monthly.
Every case study produced the same trajectory: invisible in month one, appearing in months two to four, consistently recommended by months four to eight, and compounding from there.
The industry doesn't matter. The city doesn't matter. The size doesn't matter. The evidence patterns AI evaluates are universal. The businesses that build those patterns get recommended. The businesses that don't, don't.
Your case study starts with the audit. Free. 60 secs.