LLM SEO for Local Businesses: How to Show Up in ChatGPT, Gemini & AI Overviews

"LLM SEO" is a new name for an old local-business problem: showing up when someone asks a question instead of typing a search term. Ask ChatGPT "who's a good electrician in St. Paul," or ask Gemini the same thing inside Google, and a different set of rules decides whose name comes back. This guide covers what's actually known about how LLMs pick local businesses to cite, which local signals carry over from traditional SEO, and a step-by-step way to check where your business stands today.

People search for this under several names: llm seo, chatgpt seo, ai search seo, generative engine optimization, answer engine optimization. The label doesn't matter much. What matters is whether you rank in AI search the way you already rank in Google, and that depends on a smaller, more concrete set of signals than most "AI SEO" content admits.

Two things up front, because a lot of what gets published on this topic overstates what's verifiable. First, nobody outside OpenAI, Google, and Anthropic has the exact ranking formula for their own AI systems. Any post that hands you a precise weighting is guessing with more confidence than the evidence supports. Second, the fundamentals that already work for local SEO (structured data, consistent business information, real reviews) are also what shows up in the actual public research on this topic. This guide sticks to what's documented or reasonably inferred, and says which is which as it goes.

How LLMs Pick Local Businesses to Cite

Every consumer-facing LLM that answers a "best plumber near me" question does it through some form of grounding. Instead of answering purely from what it memorized during training, it runs a live web search (or a Google Search, in Gemini's case), reads a set of pages, and writes an answer that cites some subset of what it read. That's true of ChatGPT's search feature, Gemini and AI Mode inside Google Search, and Perplexity. The mechanism differs by product, and only one of the major players has published real detail on how source selection works.

Google's own documentation is the clearest public source here. Google's Search Central page on AI features in Search describes AI Overviews and AI Mode as using a "query fan-out" technique: the system breaks a question into several related searches, then draws on a wider set of pages than a standard results page would show. Google states plainly that there are "no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary" beyond standard SEO fundamentals. Take that at face value. Google isn't running a second, secret ranking system for AI answers. It's reusing the same index and largely the same signals, then writing a synthesized answer instead of, or alongside, a list of links.

ChatGPT and Gemini haven't published the same level of detail for their conversational answers. OpenAI has confirmed that ChatGPT's search feature pulls live results from the web and shows the sources it consulted, but the company hasn't published the specific criteria that decide which of those sources gets named in the written answer versus which gets read and discarded. Any post that gives you a precise weighting (say, "40% domain authority, 35% content quality") is reporting someone's reverse-engineered guess from watching citation patterns, not a documented fact. Treat numbers like that as a hypothesis, not a spec.

What is documented: a 2024 study out of Princeton, Georgia Tech, and the Allen Institute for AI, built specifically to measure this question, found that adding citations, statistics, and quotations from authoritative sources to a page's content could boost how often it got cited in a generative engine's answer by up to 40% in their tests. That study wasn't about local businesses specifically. It tested general informational content across a range of topics. But the direction is a reasonable one to extend to local pages: a page that states facts plainly, backs them with sources or data, and reads less like marketing copy and more like a reference gives a grounded LLM something easier to lift and cite.

Where Google Business Profile data likely fits in. Google's local pack and Maps results have drawn on Google Business Profile data for years, and there's no public evidence that AI Overviews or AI Mode's local answers pull from a separate data source when a question has local intent. That's a reasonable inference from how tightly GBP is already woven into Google's other local surfaces, not a confirmed detail about the AI layer specifically. For ChatGPT and Gemini's broader web-grounded answers, a business's own site, its reviews, and how it's discussed elsewhere online plausibly matter too, but the exact mix isn't public.

The Local Signals That Transfer

For a local business, three groups of signals show up again and again, whether you're talking about the local pack, a knowledge panel, or a line in a ChatGPT answer.

Reviews. LLMs that ground in web search read review content, not just star ratings, when they write an answer about "the best X near me." That's consistent with how Google's own local ranking already treats reviews: an average score plus signals of authenticity, recency, and detail woven through the text itself. Our guide to social signals and reviews covers what to actually change. The short version is that recent, specific reviews and a habit of responding to them transfer to AI answers about as directly as they transfer to Google's Map Pack, because both systems are reading the same review text.

NAP consistency. A large language model has no built-in way to verify that "ABC Plumbing" at 1234 Main St is the same business as "ABC Plumbing LLC" listed two blocks away on a directory, with a different phone number. Inconsistent name, address, and phone data doesn't just confuse Google's local ranking system. The same conflicting signal makes a grounded LLM less confident it's citing the right entity at all. Our citations and NAP guide walks through fixing this once, correctly, instead of chasing it listing by listing.

Structured data. This is the one VeloRank has tested directly. In our own crawl of 922 live Minnesota business websites, 87.7% had no LocalBusiness schema markup at all: the structured data that tells a search engine, or an AI model reading the page, what the business is, where it operates, and what it does. In a separate probe of eleven AI-generated answers to real local hiring questions, 68.3% of the citations went to a third-party directory instead of the business's own site. Those two numbers describe the same gap from opposite sides. Most local business pages give a grounded LLM nothing structured to read, so the model reaches for the directory that does. VeloRank's free schema generator builds LocalBusiness markup in a few minutes, and it's the single most direct fix on this list. Full findings and methodology are in our AI Overviews citation research.

Step-by-Step: Audit Your AI Visibility

This section is the practical answer to how to show up in ChatGPT: run the check, read the answer honestly, and fix the gap it reveals. Before changing anything, find out where you actually stand. VeloRank's free AI visibility checker runs one real ChatGPT query for your brand, service, and market, and shows you the answer it returns. No account or credit card required.

1. 1

   Open the AI visibility checker. Go to velorank.net/tools/ai-visibility-checker.

2. 2

   Enter your business, your core service, and your market. Use the same phrasing a customer would type: business name, the service you most want to be known for ("emergency plumbing," not "plumbing services"), and your city or metro area.

3. 3

   Run the check and read the full answer, not just the headline. The tool sends one live prompt to ChatGPT and returns the response. Read past whether you're named. Note which other businesses show up and whether the answer cites a directory, a competitor's site, or nothing specific at all.

4. 4

   Look at what's missing when you're absent. If a competitor is named and you're not, check whether their site has schema markup, a page for that specific service and city, and recent reviews, since those are the levers covered above.

5. 5

   Decide your next move. A single free check is a point-in-time snapshot of one engine (ChatGPT) and one prompt. If you're not showing up, the fixes below are the place to start. If you want to track this over time, across more prompts, that's what VeloRank's account tier extends into: ongoing monitoring instead of a one-off look.

Be honest about what the free check is and isn't. It's a real, live prompt result, not a simulation, but it's one engine and one moment in time. For broader monitoring across multiple AI platforms and prompts, our comparison of the paid GEO tracking platforms covers what those tools cost and who they're actually built for. Most of them are overkill for a single-location business just getting started.

What to Fix First

If the checker shows a gap, work in this order. It follows the same logic as the research above: fix the thing a machine can read before worrying about anything more subtle.

1. Add LocalBusiness schema markup, if you don't have it. This is the fix most directly tied to what an AI model can lift and quote, and per VeloRank's own crawl, most local business sites are missing it entirely. The schema generator handles this in minutes.
2. Lock down NAP consistency across your Google Business Profile, website, and top directories. See the citations guide for the audit process.
3. Write, or rewrite, the page for your highest-priority service and city, in the specific language a customer would use, not a category label. "24-hour water heater repair in Eagan" gives a grounded LLM something concrete to cite. "Plumbing services" doesn't.
4. Keep your Google Business Profile active and your reviews current. A profile that's gone quiet, or a review section with nothing recent, reads as a weaker signal to both the local pack and any LLM grounding an answer in the same data. See the Google Business Profile guide and the reviews guide.
5. Re-run the AI visibility checker after each fix, roughly monthly, to see whether anything changed. One check won't move the needle by itself. Watching the trend over a few months will tell you whether the fixes are working.

How to Measure Monthly

AI search SEO isn't a fire-and-forget project. Treat it the way you'd treat any other slow-moving ranking signal: check it on a schedule, not once and never again.

• Re-run the free AI visibility checker monthly for your top one or two service-and-city combinations. A single data point tells you little. Three or four months of the same prompt tells you whether the fixes above are working.
• Spot-check manually in ChatGPT and Gemini for a handful of other high-value queries the free tool doesn't cover. It's not glamorous, but logging the result in a spreadsheet each month is a legitimate, honest way to track something no free tool fully automates yet.
• Watch your Google Business Profile Insights, specifically calls, direction requests, and website clicks. AI Mode and AI Overviews both route a meaningful share of local recommendations through the same profile data, so a healthy, growing action count is a reasonable proxy signal even when you can't see exactly which result type drove it.
• Check Search Console for query and click changes on the pages you rewrote. It won't show AI citations directly, but a page that starts ranking better in ordinary search for its target phrase is also the page most likely to get pulled into a grounded answer.
• Move to a paid tracker once manual checking becomes the bottleneck, not before. If you're managing this across several markets or want daily, multi-engine tracking, our GEO tools comparison breaks down what Otterly, Profound, Peec AI, and the rest actually cost and who each one fits. Most single-location businesses aren't there yet, and that's fine.

Frequently Asked Questions

How do I get my business mentioned by ChatGPT?

Start with what the evidence actually supports: add LocalBusiness schema markup so a grounded model has structured facts to read, keep your name, address, and phone number identical everywhere online, write a specific page for each service and city you want to be found for, and keep reviews and your Google Business Profile active. None of this guarantees a citation on any single prompt, since ChatGPT hasn't published its exact source-selection criteria, but it's the same underlying work the public research on generative engine optimization points to, and it's also the work that already helps traditional local SEO. Run the free AI visibility checker to see where you stand today.

Is LLM SEO different from SEO?

Not as much as the new label suggests. LLM SEO describes optimizing for how AI systems like ChatGPT and Gemini ground and cite answers, but the underlying signals (structured data, consistent business information, specific and well-sourced content, real reviews) are the same signals traditional SEO already asks for. The practical difference is presentation: instead of ranking for a click, you're trying to be one of the facts a model lifts into a written answer. Treat LLM SEO as an extension of solid local SEO, not a separate discipline you start from scratch.

What is AEO vs SEO?

SEO optimizes a page to rank in a traditional results list: the blue links and the local map pack. AEO, answer engine optimization, optimizes content to be selected as the direct answer inside a result (a featured snippet, a voice-assistant reply, or an AI-generated summary) rather than just linked to from it. The line between AEO and the newer term GEO, generative engine optimization, is still genuinely blurry, and most practitioners in 2026 treat them as overlapping names for the same underlying goal rather than two separate playbooks. For a local business, the practical takeaway matters more than the label: the same specific, structured, trustworthy page tends to win at all three.

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Not sure where you stand right now? Run VeloRank's free AI visibility checker first, then compare notes against the GEO tools comparison once you're ready to track this on an ongoing basis.