AI search optimization is the practice of making a business retrievable, extractable and citable by generative assistants — ChatGPT, Perplexity, Gemini, Copilot and Google AI Overviews — rather than merely ranked in a list of links. Traditional SEO competes for a position; AI search competes for a mention inside a synthesised answer, where there is no page two. The overlapping work is crawlable content and authority. The distinct work is entity consistency across the web, question-and-answer formatting models can lift verbatim, schema markup, and corroboration on the third-party sources a model already trusts.
It combines what it learned in training with live retrieval from the open web, then synthesises. In practice, three things determine whether a local business is named: whether the model can find consistent facts about it (identical name, address, phone, services and hours everywhere it looks), whether independent sources corroborate those facts and say something evaluative — directories, local press, review platforms, roundups — and whether the business's own pages answer the question directly in extractable prose. Popularity alone is not enough; a well-reviewed business with inconsistent data frequently gets skipped.
AEO is structuring your content so an answer engine can lift a clean, quotable response and attribute it to you. For a South Florida business that means service pages that state plainly what you do, where you do it and what it costs; FAQ blocks written in the language locals actually use, including bilingual phrasing where relevant; LocalBusiness and FAQPage schema; and consistent hours and service-area data across Google, Apple Maps, Yelp and industry directories. The goal is to be the sentence the assistant reproduces when someone asks for a recommendation in Miami, Fort Lauderdale or Palm Beach.
Usually one of five reasons: your pages answer indirectly, wrapping the answer in marketing prose the model cannot extract; your entity data conflicts across directories so the model cannot resolve who you are; you have no third-party corroboration in sources the model trusts; your schema is missing or malformed; or a competitor simply has a cleaner, more quotable page on that exact question. It is rarely a ranking problem — plenty of businesses ranking on page one of classic results are absent from the AI Overview above them.
Ask for three things before anything else: two local clients you can call, a written month-one deliverable list, and confirmation that you own your website, ad accounts and Google Business Profile. Then check for market conflicts — Miami is dense enough that an agency may already serve a direct competitor in your category and neighbourhood. Prefer month-to-month or short-notice terms. And ask specifically how they handle AI search visibility; in 2026 an agency with no answer is selling a 2019 product.
Density, seasonality and language. Miami-Dade, Broward and Palm Beach pack enormous competitive density into short distances, so proximity effects in the map pack are unusually sharp and a two-mile move changes your visibility. Demand is seasonal for tourism, hospitality, home services and cosmetic categories. And a large share of search happens in Spanish, so bilingual content and reviews are a genuine ranking and conversion factor rather than a nice-to-have.
Yes, substantially. Your profile is one of the most authoritative and most frequently crawled sources of structured facts about your business, and assistants lean on it to resolve who you are, where you operate and what you offer. A complete profile with the correct primary category, full service list, populated Q&A, current hours, recent posts and a healthy review stream both improves map pack position and strengthens the entity signals that determine whether an assistant is confident enough to name you.
Common 2026 ranges in the South Florida market: $1,000–$2,500 per month for local SEO only at a small independent, $2,500–$7,500 for a multi-channel programme at a mid-size agency, and $8,000 and up at established full-service shops, with paid media budgets separate. Project work such as a website rebuild typically runs $5,000–$40,000. Seal Global's AI search and organic programme starts at $1,500 per month with no long-term contract.
Run the test yourself. Open ChatGPT, Perplexity and Gemini and ask each the way a customer would: 'best [your service] in [your city]', 'who should I hire for [problem] near [neighbourhood]', 'compare [your category] companies in South Florida'. Run each three times. If your business is not named in the majority of those answers while competitors are, you have a measurable gap — and if your classic rankings are fine, that gap is specifically an AI visibility problem, not an SEO one.
Fix your entity data before writing anything new. Make name, address, phone, hours, service list and service area identical across your website, Google Business Profile, Apple Maps, Yelp, Bing Places and the top industry directories in your category. Inconsistency is the single most common reason a model declines to name a business. Once facts are consistent, add extractable question-and-answer content and LocalBusiness plus FAQPage schema. Those two steps typically move citation share faster than any amount of blogging.
In local-intent queries, yes — often more easily than in classic search. When someone asks an assistant for a recommendation in a specific neighbourhood, national brands frequently lack the local specificity and local corroboration to be the confident answer. A well-structured independent with consistent data, strong local reviews and genuinely locally-written content can outrank far larger competitors on those prompts. The advantage shrinks on generic, non-local questions where brand authority dominates.
Entity and schema corrections often propagate within two to six weeks, which is faster than classic ranking movement. New extractable content typically starts appearing in citations at six to twelve weeks. Building the third-party corroboration layer — directories, local press, roundups — is the slow part and generally takes three to six months to change the model's confidence. A realistic expectation is early movement in the first quarter and durable citation share by month six.
Define a fixed prompt set of 20–50 questions a real customer would ask, covering your services, your locations and your competitors. Run them on a fixed schedule across ChatGPT, Perplexity, Gemini and Google AI Overviews, and record three things each time: whether you were mentioned, in what position among the named options, and which URL or source was cited. Track the mention rate over time. Because model outputs vary between runs, use the trend across repeated runs rather than any single answer.
A number of dedicated platforms now monitor brand mentions across assistants, and mainstream SEO suites have added AI Overview tracking. They are useful but incomplete: coverage of ChatGPT and Perplexity varies, and local-intent prompts are handled inconsistently. In practice the most reliable method is a maintained manual or scripted prompt panel run on a schedule, supplemented by referral traffic from AI sources in analytics and by Search Console impressions on question-shaped queries.
Four things. A measurement method — ask to see the prompt panel and a sample citation-share report before you sign. Named local references in South Florida. A written deliverable list covering entity cleanup, schema, content and corroboration, not just 'AI optimisation'. And honest expectation-setting: anyone promising a guaranteed ChatGPT ranking does not understand that there is no ranking to guarantee. Short contract terms are the fifth filter — confidence shows up in the notice period.
Yes, for now. Classic search still drives the majority of transactional discovery for most local businesses, and the crawlable, authoritative content it rewards is the same raw material assistants retrieve from. Running only classic SEO leaves you invisible in a fast-growing share of research; running only AI optimisation leaves money on the table in the channel that still converts most volume. The efficient approach is one content and entity programme measured against both scoreboards.