Local SEO for AI search comes down to one shift: assistants don't rank businesses, they describe them. Ask ChatGPT or Gemini for a plumber in Denver and it builds a shortlist from reviews, maps data, directories, and local pages, then names two or three businesses with reasons attached. Your job is to make that evidence easy to retrieve and impossible to confuse. Same raw materials as classic local SEO. Higher stakes for getting them consistent.
Here's the playbook, in build order.
Start with your Google Business Profile
Your profile is the densest block of structured local fact that exists about your business, and it feeds the answers whether an assistant reads it directly or through pages built on top of it. Gemini sits closest to Google's maps data. Everyone else retrieves local pages, and those pages, the roundups and directories and map packs, are themselves assembled from profile data and reviews.
So finish it. Every field: primary and secondary categories, services with descriptions, hours, service area, photos, and the business description written in plain language a machine can lift. An incomplete profile starves every assistant at once, because the gaps propagate into every page built downstream of it. This is the unglamorous half of what GEO is: making the record complete before worrying about anything clever.
Get reviews with volume, recency, and specificity
Reviews are the corroboration layer. When an assistant vets a shortlist, review content is what it quotes: "customers mention fast response times and fair pricing" is a sentence assembled from your reviews, not your website.
Three properties matter, in this order:
- Recency. A steady trickle beats an old pile. Reviews from this quarter tell the model you're alive and still good.
- Specificity. A review that says "replaced our water heater in Wash Park, same day" hands the assistant a service, a neighborhood, and an outcome it can repeat. "Great company!" hands it nothing.
- Volume. More signal, more confidence. But volume without the first two just makes vague evidence louder.
Build the habit: ask every happy customer, at the moment of the thank-you, and make it one tap. Nudge them toward specifics by asking "would you mention what we did and which part of town you're in?" Respond to every review, including the bad ones. And never fake a single one. Fabricated reviews are the one mistake that poisons the whole evidence chain, which is why clean hands are a hard line in our local SEO service.
Make your NAP boringly consistent
Name, address, phone. One canonical version everywhere: your site footer, your Google Business Profile, Yelp, Apple Maps, Bing Places, industry directories, your Facebook page. Machines cross-check these constantly, and every variant spends a little of their confidence in you.
Assistants confirm entities by cross-referencing sources, and mismatched records read as ambiguity. "Summit Plumbing LLC" at one address and "Summit Plumbing & Heating" at another might be one business or two, and a model that can't tell may hedge to a competitor it can verify. Entity confirmation is the same machinery covered in how LLMs pick brands, applied at city scale.
Block out an afternoon: list every place your business appears, fix mismatches, and kill duplicate listings. Nobody enjoys this work, which is exactly why doing it puts you ahead of the businesses that skipped it.
Build a real landing page for each city you serve
When an assistant searches "water heater repair denver," it retrieves pages that literally answer that phrase. Give it one. For each city or service area you genuinely cover, build a page with the service-plus-city query in the five placements: title, meta description, URL slug, H1, and the beginning of the first sentence.
Then earn the page's existence with local proof: projects you've done there, neighborhoods you cover, city-specific pricing or logistics, reviews from customers in that city. The five placements get you retrieved. The local proof gets you believed. What you must not do is stamp out twenty near-identical doorway pages with the city name swapped; thin duplicates get filtered by search engines and skipped by models, and they can drag down the pages that deserved to win.
One city, one genuine page. If you can't write 400 genuine words about your work in a city, you're not ready to target it.
Add LocalBusiness schema
Mark up your location pages with LocalBusiness structured data: exact name, address, phone matching your canonical NAP, hours, geo coordinates, service area, and sameAs links to your Google Business Profile and directory listings. It makes your local facts machine-legible and ties your site to the same entity your profile describes.
Keep expectations calibrated. Schema is hygiene rather than a lever, and the evidence on citations is genuinely mixed; we go through it in full in does schema markup help AI citations. For local businesses it's cheap insurance against ambiguity, which is reason enough. Validate it, match it to your visible content, and move on.
Get into the lists your city's answers are built from
One more evidence source punches above its weight: the local roundups. For "best [category] in [city]" questions, assistants lean on whatever listicles and directories already rank, so absence from those pages means absence from the answer they assemble. Search your own category plus city, note the three or four roundups that keep appearing, and check each for your business. Then work the list: claim your profiles on the directories, and pitch the editorial roundups with facts that make you easy to include, your specialty, your neighborhoods, a review pull-quote. Real coverage only; paying for inclusion on junk directories adds noise, not evidence.
Test what assistants say about your city tonight
You can't manage what you've never read. Run your own category through the assistants your customers use, ChatGPT with search, Gemini, and Perplexity, and log what comes back.
Paste this in, filled in with your details:
Act like a local customer in [city]. Answer each separately,
and list every source page you used:
1. Who are the best [category] in [city]? Give me three names
with your reasons.
2. Is [your business name] a good choice for [service] in [city]?
What do their reviews say?
3. Who would you recommend for [service] near [neighborhood]?
For each answer, tell me whether [your business name] came up,
and if not, what the recommended businesses had that it lacked.
Screenshot the answers and save the source lists. That third line of output is your gap analysis, written by the machine that does the recommending. If you never come up at all, run the deeper diagnostic in why is my business not showing up in ChatGPT to find which link in the chain is broken.
What does the maintenance loop look like?
Local AI visibility isn't a one-time build. The evidence decays: reviews age, hours change, competitors publish. The loop that keeps you recommended is small and monthly:
- Re-run the test prompt above in two or three assistants. Log who's named and which sources they used.
- Check the review trickle: did this month add fresh, specific reviews?
- Scan your NAP for drift after any change to hours, phone, or address.
- Refresh one city page with a recent project or review.
Thirty minutes a month once the foundation is set.
Start tonight with the test prompt. Run it in two assistants, screenshot what they say about your category in your city, and read the sources they pulled from. Whatever gap shows up first, profile, reviews, consistency, or pages, that's your next two weekends.