Local SEO and AI search, how to get recommended in your area

How do AI engines choose local businesses to recommend?

AI engines pick local businesses they can find, match to the place asked about, and trust. That draws on many of the same signals Google has used for local search for years.

When someone asks for the best kitchen fitter in Leeds, the engine needs three things. It needs to know which businesses serve Leeds. It needs to know what they offer. And it needs some reason to think one is better than another. Your Business Profile, website, directory listings and reviews all feed those three answers.

Engines with web search, such as ChatGPT search and Perplexity, often cite directories, review sites and local news when they answer. Engines that answer from model knowledge alone rely on what they learnt in training. In both cases, a business that is described clearly and consistently across many sources has the best chance.

Does local SEO still matter with AI search?

Yes, and most of it matters more. Google's local ranking help page says local results are based on relevance, distance and prominence. Relevance is how well a profile matches the search. Distance is how far the business is from the searcher. Prominence is how well known the business is, including links, reviews and ratings.

Google's guide to generative AI features goes a step further. It says Business Profile can help your products and services be visible in AI responses as well as other Google results. So the work you do on your profile counts twice.

For other engines, the link is less direct but still real. Directories and review sites often copy data that started on your profile or website. If those are accurate, the whole web tells a consistent story about you.

What local signals matter most for AI answers?

Five areas do most of the work. None of them is new, but AI raises the cost of getting them wrong.

NAP consistency is worth a special mention. Name, address and phone number should match exactly across your site, profile and directories. Small differences, such as "Ltd" on one site and not another, or an old phone number, make it harder for an engine to be sure two mentions are the same business.

How should you build location pages for AI search?

Give each branch a real page, not a template with the town name swapped. A good location page answers the questions someone in that town would ask.

• The full address, phone number and opening hours for that branch.
• The services offered there, especially if they differ between branches.
• Parking, access and how to find you.
• The names of the local team, where they are happy to be named.
• Photos of the branch and of real local jobs.
• A few answers to local questions, such as which areas you cover.

Mark each page up as a LocalBusiness on schema.org, with its address, phone and opening hours. Link it to the parent brand so engines see one business with several branches. The details must match the page and the Business Profile exactly.

For a multi-location business, this is the foundation. The multi-location brands page covers how to manage it across many branches.

What about businesses without a shopfront?

Service-area businesses, such as plumbers, roofers and mobile hairdressers, face a slightly different problem. They serve many towns but have one base, or none that customers visit.

The same principles apply with a different shape. Be clear on your site and profile about exactly which areas you cover. List the towns by name rather than saying "and surrounding areas". Where you do a lot of work in one town, a page about your work there can help. It needs real content, such as recent jobs, local issues and the questions people there ask.

Avoid creating dozens of near-identical town pages. They add little for people, and they give engines no clear reason to prefer you. A handful of strong pages for the towns that matter most is a better use of time. The local service businesses page covers this in more detail.

How much do reviews matter for AI recommendations?

A lot, as far as anyone can tell from the outside. Google names reviews as part of prominence and says more reviews and positive ratings can help local ranking.

AI engines also read what reviews say. When they explain why they recommend a business, they often echo review themes, such as "known for tidy fitters" or "good at explaining costs". That means the words in your reviews matter as well as the stars.

A steady flow of honest reviews is better than a burst. Ask every happy customer. Reply to every review, good or bad, calmly and helpfully. Never buy or fake reviews. Apart from breaking platform rules, fake reviews tend to read alike, and that can be spotted.

How do you track AI visibility for local searches?

Name the place in the prompt, run it in each market, and repeat it over time. That is the core of it.

Tracking "best dentist near me" through an AI engine's API does not work well, because the API has no idea where "me" is. Writing "best dentist in Harrogate" gives a clear, repeatable question. You can then run the same set of prompts for each town you serve.

A simple local prompt set might look like this for a dental group.

1. Best dentist in Harrogate for nervous patients.
2. Emergency dentist in Harrogate open on Saturday.
3. How much do dental implants cost in Harrogate?
4. Which dental practices in Harrogate offer Invisalign?
5. Is there an NHS dentist taking new patients in Harrogate?

Run the same five for each town, and compare. You will often find you are named in one town and missing in the next. The prompt research guide covers how to choose and grow a prompt set.

Why do local AI results differ from what customers see?

Because consumer apps can use more context than a tracking tool. The ChatGPT or Gemini app may know roughly where a user is, what they asked before and which model version they are on.

That does not make tracking useless. It makes it a sample. Running the same prompts on a schedule shows trends and competitor patterns that a one-off check in your own app cannot. Just be honest with yourself and your clients that a tracked answer is a close reading, not a copy of every customer's screen.

It is also worth checking the normal local results. The map pack in Google still drives a lot of calls, and it shares signals with Google's AI features. Tracking both side by side gives a fuller picture.

What if AI engines get your local details wrong?

Find the source and fix it there. Wrong opening hours, closed branches still being recommended and old phone numbers are common. They usually trace back to an old directory listing, a stale page or an outdated profile.

Start with your Business Profile and website. Then check the main directories and review sites in your sector. Once the sources agree, later answers tend to follow, though it can take time for engines to catch up.

How Axiom GEO helps

Local AI visibility runs the same prompts per country or region, using location-aware prompts for AI engines and a location setting for Google, then splits share of voice by market. Rank tracking checks daily Google positions down to town level and can filter for keywords showing a local pack. Google Business reviews finds every location for a brand, pulls reviews daily and drafts replies. Local citations tracks your directory listings and whether each one is live, claimed, pending or not claimed.