For two decades, aggregators owned page one — Yelp, Healthgrades, Angi, BBB. Now buyers ask ChatGPT for the best dentist in their ZIP and get three actual dentists, by name. Local has been structurally re-opened. We make sure you’re the name it surfaces.
Two decades of local search were aggregator-owned: Yelp, Healthgrades, Angi, BBB, Tripadvisor. Buyers landed on a directory, scrolled five listings, picked one. AI cuts the middleman. The shortlist is three actual local business names — given to the buyer with no clicking.
“Best HVAC near me”, “family dentist accepting Cigna in Plano”, “honest mechanic in zip code X” — these prompts are happening every day, in every category. The behavior is here, not coming. The local businesses showing up on those shortlists today are setting the default for the next year.
It used to be: rank in the local pack, get the click. Now it’s: get named in the AI shortlist, then convert that recommendation into a Maps lookup, a phone call, a walk-in. Two surfaces, both important — but the first one is brand-new and almost nobody is competing yet.
The local buyer journey now has a new first step. The buyer asks AI for a recommendation, gets three names, and then does what local buyers always did — opens Maps to see distance, scans reviews, and calls or walks in. The conversion moves where it always moved. The shortlist is what changed.
That means winning local now requires both layers — the AI shortlist (new) and the Maps + reviews + GBP stack (still load-bearing). Skip either layer and the funnel breaks at one end. We run both as one engagement.
AI doesn’t cite the page that says “family-friendly dentist serving North Texas” the same way every other practice in the metro does. It cites the page that knows the school district, names the insurance carriers it actually accepts, and answers the questions real local buyers are asking. Two moves we run that most local SEO programs skip.
The questions buyers ask the front desk on the phone and in the waiting room are the questions they’re now typing into ChatGPT. Insurance carriers, parking, kids welcome, weekend hours, specific procedures, language spoken — all of it.
We extract those question-and-answer pairs (from a few weeks of call notes, your existing GBP Q&A, and your review threads) and turn them into specific FAQ pages, neighborhood pages, and GBP posts — the long-tail factual answers AI lifts when it decomposes a local prompt.
Multi-location chains, single-location practices, and service-area businesses (HVAC, plumbing, roofing) need different programs. Different prompt sets, different content shapes, different review-acquisition motions, different GBP setups.
We tune the engagement to the business shape. The 12-location dental chain gets a templated location-page system + central review automation. The single-location law firm gets bespoke practice-area depth. The HVAC service area gets per-city pages and emergency-service prompts wired to ad spend.
We map your category against every prompt shape and audit the citation source for each. Different shape, different source — the on-site work bends per type.
Where you stand on the top 30 buyer prompts in your category and ZIP, scored across all five AI platforms. GBP health (categories, attributes, photos, Q&A, posts), citation parity across the 23 directories per industry, review velocity. Both layers, one report.
Re-pick primary and secondary categories against real conversion data, refresh service areas, fix NAP, ship a 90-day post calendar, photograph and tag the location, write the services list properly. Q&A seeded with the questions your front desk already answers daily.
Neighborhood pages, FAQ pages from your front-desk question log, constraint-specific pages (insurance, hours, accessibility), and a small but real blog of local content. The on-site source pages AI lifts from when it builds the shortlist.
Local subreddits, Nextdoor presence, neighborhood Facebook groups, and the local listicle ecosystem. AI cites these directly for local prompts. We do the work in a way that respects community rules and doesn’t look like vendor astroturfing.
AI citation share for your category and city, map-pack ranks, GBP impressions and direction requests, plus the booking number you actually live by. One dashboard, not three.
When AI answers a local prompt, it cross-references your GBP for category accuracy, hours, attributes, photos, Q&A, and recent posts — alongside Maps rank and reviews. Most local businesses picked their categories at signup, never refreshed them, and haven’t touched Q&A since.
We treat GBP as a first-class citation surface, not a sign-up chore. Categories re-picked against real conversion data, hours and special hours kept current, attributes ticked properly, service area refreshed, posts on a 90-day calendar, Q&A seeded from front-desk questions.
A single-location family dental practice with a healthy patient base, strong Google reviews, and zero AI presence. Aggregators and two larger nearby practices held every local AI shortlist for their core prompts. Walk-in traffic was steady; new-patient requests had plateaued.
We rebuilt the GBP, shipped neighborhood + insurance-specific pages from front-desk question logs, ran a small Reddit and Nextdoor presence in the four neighborhood communities the practice serves, and standardized review-acquisition off the chair. By week 9 they were in the AI top-3 on 18 of 24 prompts and new-patient bookings lifted 27% on a like-for-like comparison.
Read the full case study →Run a Live Audit. We pull your business against competitors on the local prompts that drive bookings — across ChatGPT, Perplexity, Gemini, Copilot, and Google AI Overviews.
