Which Florida Businesses Does ChatGPT Recommend?

When we asked ChatGPT for the best plumber, roofer, HVAC company, dentist, personal injury lawyer, med spa, real estate agent and moving company in five Florida metros, it recommended the businesses at the top of its own map-style business list, and those businesses had roughly six times more reviews than the ones listed below them. ChatGPT backed its answers with directories and “best of” pages, never with the businesses’ own websites. The websites of the recommended businesses were mostly technically sound, which makes a crawlable site look like the entry ticket rather than the deciding factor.

This is an original study by Ambeltek, a Florida-registered AI SEO agency. We ran 40 prompts through ChatGPT on 24 September 2026, recorded every business and source it named, and ran our own 44-check audit on 59 of the recommended businesses’ websites. Below: the method, five findings, what the data does not show, and what a Florida business owner should do with it. If the terms are new, local AI visibility covers the basics.

On this page

• The five findings in brief
• How we ran the study
• Finding 1: the written picks come straight off the list
• Finding 2: reviews separate the named from the listed
• Finding 3: ChatGPT cited directories, not business websites
• Finding 4: the recommended websites were mostly sound
• Finding 5: structured data was the weakest layer
• What can this study not tell you?
• Which businesses did ChatGPT name first?
• What Florida businesses should do about ChatGPT recommendations

The five findings in brief

1. ChatGPT’s written recommendations mirrored the top of its local-business list. In 36 of the 39 answers that included a list, the businesses named in the text were exactly the first entries of that list. 172 of the 186 businesses named in both places (92%) sat in the list’s top five.
2. Reviews separated the recommended from the merely listed. Named businesses had a median of 125 reviews and a 4.7 average rating. Businesses that appeared only in the list had a median of 19 reviews and a 4.4 average.
3. ChatGPT cited directories and review roundups, never the businesses’ own sites. Of 113 citations, 56 went to vertical directories and 54 to national review or listicle sites. Business websites: 0. Reddit and Google Maps: 0. Yelp, the BBB and local media: 1 each.
4. The recommended businesses’ websites were mostly technically sound. Median audit score 89 out of 100, 48 of 59 sites at 75 or above. All 59 allowed OAI-SearchBot in robots.txt, and 50 of 57 served their main content without JavaScript.
5. Structured data was the weakest layer. Schema scored lowest of the six audit categories (average 73). Only 38 of 57 homepages (67%) declared a LocalBusiness-type entity, 28 (49%) linked their profiles with sameAs, and 9 (16%) named an author.

How we ran the study

We sent 40 prompts in the form “best [service] in [city], Florida” to ChatGPT through DataForSEO’s ChatGPT LLM Scraper and stored the full responses. Each prompt ran once. The question matters because this is now how a large share of customers shop: in BrightLocal’s 2026 survey of 1,002 US adults, 45% had used an AI tool to find a local business, up from 6% a year earlier, and ChatGPT was the most used tool for it.

How the 60 websites were chosen. For each prompt we took the first business ChatGPT named in its answer text, then a second one for 20 prompts spread evenly across metros and verticals. The website link came from ChatGPT’s own output, so no separate lookup was needed. We skipped links that were not the business’s own site (a map mirror, a link-in-bio page, brokerage profile pages) and never audited the same domain twice. Each vertical ended up with 6 to 8 audited sites.

The audit is the same engine behind our free AI SEO audit tool. It scores six weighted categories: AI crawler access (25%), content without JavaScript (20%), structured data and entity (20%), metadata (15%), GEO content signals (15%) and technical basics (5%). We counted a site as having LocalBusiness schema when its JSON-LD declared LocalBusiness or a schema.org subtype such as Dentist, Attorney, Plumber, RoofingContractor, HVACBusiness, MovingCompany, RealEstateAgent or MedicalClinic.

Limitations. Read the numbers with these in mind:

• ChatGPT’s answers vary run to run. One run per prompt is a snapshot, not a ranking. A second run on another day would name some different businesses.
• Location was set to the United States, not a device in Florida. The city came from the prompt. A logged-in user standing in Tampa with location sharing on may see different results.
• No control group. We audited recommended businesses only, so we cannot say their websites are better than those of businesses ChatGPT skipped.
• Homepages only. Service and location pages were not audited.
• Bot identity. Our audit requests identify as GPTBot but come from our servers, not OpenAI’s published IP ranges. A firewall that verifies bots by IP may refuse our request and still admit the real crawler. Four sites also rate-limited the audit’s burst of parallel requests while answering a single request normally; their access scores carry that penalty.
• Small cells. With 6 to 8 sites per vertical and 11 to 12 per metro, differences between verticals and cities are indicative only.
• Source categories are ours. We grouped cited domains into types by hand.

Finding 1: the written picks come straight off the list

For 39 of the 40 prompts, ChatGPT showed a local-business list: name, address, rating, review count and website, 12 businesses on average. It then wrote an answer naming about 5 of them. The written picks were not a fresh judgment. In 36 of those 39 answers, the named businesses that appeared in the list were exactly its first entries: name five, and they were list positions one to five. Of the 186 businesses that appeared in both the list and the text, 172 were in the list’s top five and only 2 came from ninth place or lower.

Only 11 businesses were named in the text without appearing in the list, usually a firm ChatGPT picked up from a directory such as Super Lawyers or HomeLight. One prompt, for Tampa movers, returned no list at all, and ChatGPT built its answer from moveBuddha and Yably instead.

The practical reading: for these prompts, getting recommended looks like getting to the top of whatever business data ChatGPT pulls its list from. About 31% of the list’s website links carried tracking tags that owners usually put on their Google Business Profile website button (for example utm_campaign=gmb or gbp), which suggests the list is built from the same listing data owners maintain, although ChatGPT does not say where it comes from. The Google Business Profile guide covers those fields one by one.

Finding 2: reviews separate the named from the listed

The businesses ChatGPT named had far stronger review profiles than those it only listed. That lines up with how Google describes its own local ranking, where prominence, including review count and rating, is one of three core factors alongside relevance and distance.

The list itself was not a quality filter. Across all 469 list entries, a quarter (25.6%) had fewer than 10 reviews and 17.9% were rated under 4.0 stars. ChatGPT’s text did most of the filtering, and it favored the entries at the top.

One more pattern matters for owners. In at least 11 of the 40 answers, the review count in ChatGPT’s text did not match the count on its own list for the same business, because the text quoted a directory’s figure (for example “4.9 from 500+ reviews” next to a list card showing 34). ChatGPT reads your reputation from several places at once, and they do not always agree.

Finding 3: ChatGPT cited directories, not business websites

Every one of the 40 answers cited at least one source. None of them cited a business’s own website.

The single most cited domain was Expertise.com (13 of 40 answers), followed by ConsumerAffairs (10), Birdeye (8), and BestProsInTown, HomeLight, moveBuddha and Yably (5 each). Nearly half of the cited page titles (47%) contained “2026”, which fits the idea that ChatGPT prefers roundups that look current. The places many owners assume matter most were almost absent: Yelp once (Jacksonville HVAC), the BBB once (Miami roofers), a local magazine once (Orlando Magazine’s Top Dentists list), and Reddit and Google Maps never. That Reddit result is a genuine null for this kind of prompt, not a gap in collection.

Each vertical leaned on its own set of sources:

One HVAC source deserves a note: a site that ranks HVAC companies by how often AI assistants recommend them was cited in 3 of 5 HVAC answers. AI answers are already citing pages about AI answers. For more on how engines pick what to cite, see how AI engines choose citations.

Finding 4: the recommended websites were mostly sound

The websites behind ChatGPT’s picks scored well on our audit. Across 59 homepages the mean was 84.1, the median 89, and the middle half fell between 80 and 94.

The basics were close to universal. All 59 served HTTPS. All 59 allowed OAI-SearchBot, the crawler OpenAI uses to surface pages in ChatGPT search, along with ChatGPT-User and Bingbot in robots.txt (11 had no robots.txt at all, which crawlers treat as permission), and none blocked GPTBot. 50 of the 57 analyzable homepages (88%) put at least 300 words of main content in the raw HTML, and only one was a client-side app that shows nothing without JavaScript. The median homepage carried 1,137 words. WordPress ran 36 of the 59 sites.

Firewalls were a small but real issue. Three sites served a normal browser but refused a single request identifying as an OpenAI bot, and two refused our requests entirely. Because our requests do not come from OpenAI’s IP ranges, this does not prove the real crawler is blocked, but it is worth checking in your CDN or security plugin settings. The robots.txt guide for AI crawlers and JavaScript rendering for AI crawlers explain both layers.

Average category scores for the 57 analyzable sites:

By vertical, real estate agents stood out at the bottom (mean 67 across 7 sites), partly because two agent sites redirected to a differently named property-search domain. The other seven verticals averaged between 80 and 90. With 6 to 8 sites each, treat that spread as a pattern to check, not a ranking.

Finding 5: structured data was the weakest layer

Structured data tells engines which business a page belongs to and links it to the same business elsewhere. For a local business that usually means a LocalBusiness node, or one of its subtypes such as Dentist or Plumber, with the address, phone and sameAs links to the business’s profiles. It was the category where the recommended businesses left the most on the table.

Some specifics from the schema layer:

• LocalBusiness-type entity: 38 of 57 (67%). One in three homepages of a recommended local business did not tell machines it is a local business. Only 2 of the 7 real estate agent sites did.
• sameAs links: 28 of 57 (49%). Half did not connect the website to their profiles elsewhere, even though Finding 3 shows those profiles are exactly where ChatGPT reads.
• FAQPage: 12 of 57 (21%). Named author: 9 of 57 (16%). These matter more for content pages than homepages, so a low rate is expected here.
• llms.txt: 20 of 59 (34%). Higher than we expected, and at least 7 of the 20 files said they were generated automatically by a WordPress SEO plugin (Yoast, Rank Math or All in One SEO). The owners may not know they have one. Engine adoption of the file is unconfirmed; see what llms.txt is.

The fix is small for most sites: one LocalBusiness subtype node with name, address, phone, geo coordinates, hours and sameAs links to the profiles that matter. Structured data for AI search and the sameAs schema guide show the markup.

What can this study not tell you?

Null and weak results are part of the finding:

• Website score did not track review count. Among the 59 audited businesses, the rank correlation between audit score and review count was -0.08, which is effectively zero. Strong reputations and strong websites were separate things in this sample.
• We cannot say a better website gets you recommended. Every audited business was already recommended. Without auditing businesses ChatGPT skipped, the most the data supports is that the recommended ones rarely had broken basics.
• Metro differences were small and noisy. Average scores ranged from 80 (Orlando) to 87 (Fort Lauderdale) on 11 or 12 sites each.
• Reddit, Google Maps and local news played almost no visible role for these prompts. That may differ for questions phrased as opinions (“is X any good?”) rather than “best X in Y”.
• The model version is unknown. DataForSEO did not report it, so we cannot tie these results to a specific ChatGPT model.

Which businesses did ChatGPT name first?

For reference, the first business ChatGPT named in each answer on 24 September 2026. A different run could lead with a different name. Scores for these businesses are not published; the audit results above are aggregates only.

What Florida businesses should do about ChatGPT recommendations

The data points to reputation and listings first, third-party coverage second, and a clean, well-marked-up website as the foundation that keeps you eligible.

1. Work the listing, not just the website. ChatGPT’s written picks came from the top of its business list, and the named businesses had a median of 125 reviews. Keep your Google Business Profile complete and accurate, add Bing Places and Apple Business Connect, and ask every satisfied customer for a review. Never buy them: the FTC’s rule on fake reviews bans it. Our local SEO checklist and Bing Webmaster Tools guide cover the setup.
2. Get onto the directories ChatGPT reads for your vertical. Use the table in Finding 3 as a starting list: Justia, Super Lawyers and Lawyers.com for injury lawyers; ADA Find-a-Dentist, Healthgrades and Birdeye for dentists; Zillow, Realtor.com and HomeLight for agents; moveBuddha for movers; Expertise.com, ConsumerAffairs and Angi for home services. Claim the free profiles, keep them consistent, and treat paid “best of” placements with skepticism.
3. Keep your numbers consistent everywhere. ChatGPT quoted directory review counts that disagreed with its own list in at least 11 answers. A business with 700 reviews on one platform and 35 on another sends a muddled signal.
4. Keep the website basics that every recommended business had. Allow OAI-SearchBot, ChatGPT-User and Bingbot, check that your CDN or security plugin does not refuse them, and serve your main content as HTML. These were near-universal among the recommended businesses, so missing one puts you behind the field.
5. Fix your structured data. Add a LocalBusiness subtype (Dentist, Attorney, Plumber, RoofingContractor, HVACBusiness, MovingCompany, RealEstateAgent) with name, address, phone, hours, geo coordinates and sameAs links to your Google, Yelp, BBB and directory profiles. A third of recommended businesses skipped this; doing it is a cheap edge.
6. Measure with repeated questions, not one screenshot. Ask ChatGPT the questions your customers ask, several times a month, and track how often you appear. Measuring brand visibility in ChatGPT explains a simple routine, and how to rank in ChatGPT search covers the wider picture.

Home services and law firm owners will find vertical-specific advice in home services SEO and SEO for personal injury lawyers.

Want to see where your own site stands? Run your homepage through our free AI SEO audit, the same 44 checks used in this study. If you would rather have the listings, reviews, directory profiles and schema handled for you, Ambeltek’s local SEO service does exactly that for Florida businesses.