AEO for Local Business: How to Get Named When People Ask AI 'Near Me'

Picture the actual moment. Someone's water heater just failed on a Sunday night. They don't open Google and scroll a page of blue links and ten map pins. They ask their phone, "who's a good plumber near me open right now," and they call one of the two or three names that come back. If your business isn't one of those names, you never existed in that decision — and you'll never see it, because there was no click and no impression to miss.

That is what AEO is about for a local business. Not ranking in the map pack. Getting named in the answer itself when someone asks a machine who to call. I build this for a living, so everything below is mechanics I've watched decide real local recommendations, not theory. This is the service-business companion to the full answer engine optimization playbook and the tactical get-cited guide. Here's what actually works when the question is local.

Your Google Business Profile is the primary source — fill it completely

For local questions, the single richest thing an answer engine reads is your Google Business Profile. It's a structured, trusted record of who you are, what you do, where you do it, and when you're open. When someone asks AI for a local recommendation, that profile is doing more work than your homepage.

Most local businesses leave it half-built. The category is generic, the service list is empty, the service area is undefined, the hours are stale. To a machine, that's a business it can't confidently place, describe, or recommend. Fill in the real category, list every service in the service list, define the actual service area, keep hours accurate including holidays, and make sure the description says plainly what you do and where. Every empty field is a fact the engine has to guess at, and it would rather name a competitor it doesn't have to guess about.

Make "open now" answerable

A huge share of local intent is urgent. "Open now," "emergency," "tonight," "same day." To answer those, the engine needs live, accurate hours it can actually read — and that means your hours have to be right on your Business Profile and match what's on your site.

This is a boring detail that quietly costs real jobs. If your hours are wrong or missing, you get filtered straight out of the highest-intent question there is: someone ready to call this minute. Keep hours current, mark holidays, and if you genuinely are 24/7 for emergencies, say so in plain text where both the profile and the page can carry it.

Keep your name, address, and phone identical everywhere

Answer engines trust facts that corroborate each other. Your name, address, and phone number — NAP — are the anchor that ties your Business Profile, your website, and every directory into one business the machine is confident about. When they match everywhere, you're one trustworthy entity. When they don't, you're an ambiguous mess it's safer to skip.

The failures are mundane and common: an old suite number on one directory, a tracking phone number on the site that doesn't match the profile, "Ave" here and "Avenue" there, a former address that never got cleaned up. Pick one exact format and make it identical on your site, your Business Profile, and every listing you can reach. Consistency isn't busywork here — it's how the engine decides you're real.

Write a real page for each service and each area

People ask AI narrow local questions: "emergency AC repair in [town]," "grooming for large dogs near [neighborhood]," "commercial electrician [city]." The page that gets named is the one that answers that exact combination of service and place, in plain text. A single "Services" page that lists twelve things can't be cleanly quoted for any one of them, and a homepage that says "serving the greater metro area" answers no specific question at all.

Give each core service its own page, and where you genuinely work in distinct areas, give the important service-and-area combinations their own pages too. On each one, answer in text what people actually ask: what the service covers, what it costs or how pricing works, how fast you respond, what area you serve, and what makes you the right call. Lead with the answer, in the customer's words — the same answer-first discipline that wins everywhere in AI search, applied to local. It's the local version of FAQ pages that get cited.

Reviews are your consensus signal — make them specific and local

Answer engines are consensus machines. When Google, your site, and other sources agree you're the good plumber in this town, that agreement becomes the thing the model says. Your reviews are the richest consensus signal you own, and two businesses that look otherwise identical are often separated entirely by whose reviews are more numerous, more recent, and more specific.

Bare five stars barely help. "Fixed our burst pipe in Riverside on a Sunday, showed up in an hour" is worth ten "great service" reviews, because it names the service, the neighborhood, and the urgency — all things an engine can lift straight into a local answer. Ask satisfied customers to mention what you did and where, keep the flow of recent reviews steady, and respond to them, because a live, specific review body is far more useful to a machine than a static star average.

Mark up the facts so the machine trusts them

Structured data hands the engine clean facts instead of making it parse them out of your layout. For a local business that means LocalBusiness (or the specific subtype) schema carrying your NAP, hours, service area, and geo, plus FAQPage markup for the real questions on each service page. It won't rescue a thin page — the clear answer still has to be there in text — but when it is, schema tips you from "probably relevant" to "safe to name." The full mechanics are in schema and JSON-LD for AI search.

The hard part is doing this across every service and location

Everything above is straightforward for one service in one town. The problem is you don't have one — you have a real service list across a real coverage map, and people ask AI thousands of narrow questions that map to every combination of what you do and where you do it, not just your headline service in your home city. Winning means every one of those combinations has a page that answers it cleanly, with accurate NAP, live hours, real reviews, and complete schema, all consistent with each other.

Hand-building and maintaining that across a full service list and service area is the actual work, and it's why programmatic AEO at scale exists — generating genuinely useful, location-accurate, schema-complete answer pages for every service and area, not thin spun copy that gets you penalized. It's exactly the problem I built RunOctopus to solve: it reads your real business, services, and coverage and builds the extractable, cited-ready answer layer across all of it, so the machine has something of yours to name no matter how specific the local question gets.

Test what AI actually recommends near you

Don't guess whether this is working — measure it. Keep a fixed list of the real local questions in your trade ("emergency [service] in [town]," "best [service] near [area]," "[service] open now [city]"), ask each one in ChatGPT, Perplexity, and Google's AI answers, and record whether you get named. Re-run it monthly and track the share of local prompts that name you. That coverage rate, not raw traffic, is the number to move — see track your AI visibility for the full method.

The local businesses that win the next few years aren't the ones with the biggest ad spend. They're the ones whose service, area, hours, and reviews are the clearest, most consistent, most quotable record in town — so that when someone asks a machine who to call, you're simply the obvious name.