AI answers at four business scales: local, multi-state, international and global SaaS

Local: the answer starts with the place

Six questions about businesses that serve one city: an emergency plumber and a managed IT company in Austin, a family dentist in Denver, commercial cleaning in Chicago, a CPA firm for small business in Charlotte and HVAC in Phoenix. Half of them are B2B purchases, and their answers were built the same way as the consumer ones.

Nine of the 11 local answers showed place data. Ask ChatGPT for a managed IT company in Austin and you get four company cards with a rating, a review count, an address, a phone number and a map. We opened the source panel on three of them. Each cited two things: a google.com record with the company’s name and street address, and a ranking of Austin IT providers from Jumpfactor, CloudSecureTech or Clutch. In Chicago, ChatGPT spelled out its own method: “I compared local business listings, customer ratings, and published service information.”

Google leaned on listings even harder. Its Denver answer was simply four Business Profile cards with 316 to 972 reviews each. For Austin plumbers it showed seven cards with 301 to about 18,100 reviews, plus a Reddit thread from r/Austin and the Austin American-Statesman’s list of plumbers that locals recommend. When Google cited a company’s own page, it was almost always a page about the city: GCS Technologies’ guide to choosing a managed IT provider in Austin, or the Austin pages of Roto-Rooter and Goettl. GCS came first in its answer. One run can’t tell us why, but the page Google cited answers exactly the question we asked.

The two systems rarely agreed. Across the five local questions both answered, they shared three names in total. For Phoenix, Google showed no AI Overview, and ChatGPT built its list from review sites such as RatingsNearMe, ContractorsRanked and Birdeye.

What to do

1. Keep your Google Business Profile complete and accurate, and keep the reviews coming. Google lists relevance, distance and prominence as its local ranking factors and says more reviews and positive ratings can help. ChatGPT cited google.com place records behind its Austin IT cards too.
2. Find the city rankings each system cites in your category. In our sample they ran from Clutch and IT-industry lists to Expertise.com, RatingsNearMe, Best Charlotte, My Best Denver, Yelp, Reddit and a local newspaper.
3. Publish one page that fully answers the local buyer’s question: who you serve in the city, how fast you respond, what’s included and how pricing works.
4. Check from the city, or name the city in the question. OpenAI says ChatGPT may use an approximate location based on your IP address to find local results.

Multi-state: each state gets its own shortlist

Three pairs of questions, each asked for two states: personal injury law in Texas and Florida, solar in Arizona and Texas, staffing in Ohio and Tennessee. Every category has firms that work in one state and firms that work across many.

Morgan & Morgan is the clearest case. ChatGPT put it first in Florida as the “best starting point for statewide coverage”, and Google’s Florida answer named it too. In Texas, neither system mentioned it. Sunrun followed the same pattern. ChatGPT listed it in Arizona and left it out in Texas, and Google’s Texas answer named four Texas installers and mentioned Sunrun only below the list, among national chains whose quotes, according to the EnergySage data it cited, tend to run higher than local contractors’.

We can’t say why from a single run. ChatGPT built its Texas law list partly from Dallas rankings and Chambers’ Texas guide, and those sources may simply not cover the firm. What we can see is what the national names on both state lists had in common. ChatGPT kept Express Employment Professionals, Aerotek and Adecco in both staffing answers and mostly backed them with proof from that state: “a 2026 Best of Staffing award winner in Tennessee”, “It has Ohio offices”, “Multiple Tennessee locations have strong local reviews”. Google went further: it split Ohio into regions and represented Express by its franchise office in Columbus.

The two systems built state answers differently. Google showed firm listings in five of the six state answers. ChatGPT did so once, for Texas solar, and leaned on state rankings and awards throughout: Chambers’ Texas guide, EnergySage’s Texas rankings, ClearlyRated’s Best of Staffing lists. Eleven of the 12 state answers ended by asking for the city or region.

One detail matters for B2B firms. In Ohio, ChatGPT read “best staffing agency” as a job search. In Tennessee, it asked whether we were looking for a job or hiring.

What to do

1. Give every office its own page, with the address, the people there, the services in that state and proof from that state, plus its own Business Profile. That’s the minimum: Morgan & Morgan has a Texas page and still wasn’t named.
2. Earn and publish proof at state level: state awards and rankings, reviews of each office, state credentials. That’s the proof the answers quoted when a national firm made a state list.
3. Measure state by state and for the main cities. A strong answer at home says little about the next state.
4. If you sell to businesses, say so plainly on the page and in the listing, so the answer doesn’t mistake your buyer for a job seeker.

International firms: a city abroad works like a local market

Three pairs of questions about international categories, each asked for two cities: commercial real estate in Dubai and London, accounting firms in Singapore and Toronto, freight forwarders in Dubai and Hong Kong. Each pair has international names that work in both cities.

With a city in every question, the answers looked local. Seven of the 10 showed place data. In ChatGPT, global brands came in mostly through their local presence: CBRE and JLL through cbre.ae and jll-mena.com in Dubai, PwC and RSM through their Singapore sections, Deloitte Canada and MNP with Toronto street addresses, DHL with a Hong Kong airport address and a pin on the map.

A local office was no guarantee, and some global names got in without any local detail. ChatGPT named Savills and Knight Frank in London but not in Dubai, although both list Dubai offices. Its Dubai freight answer, on the other hand, included DHL, Kuehne+Nagel and DSV with links to their global sites and nothing about Dubai, next to two local forwarders rated from their Dubai business listings. Google’s Singapore answer named the Big Four and the mid-tier firms in one line each, without local evidence.

How much the two cities overlapped depended on the category. ChatGPT named CBRE, JLL and Cushman & Wakefield in both Dubai and London, the last in Dubai as Cushman & Wakefield Core, the firm’s exclusive UAE affiliate. In accounting, it named PwC in Singapore and Deloitte in Toronto, with no firm on both lists. In freight, DHL was the only name both systems gave for both cities.

Two of ChatGPT’s international answers had no place data. In London it cited a commercial real estate brand survey, the trade publication Property Week and London rankings. In Singapore it cited a local review site, comparison blogs and the accounting regulator. Google showed no AI Overview at all for commercial real estate in Dubai or accounting firms in Toronto. ChatGPT answered both.

What to do

1. Give each country a real local presence online: a country site or section with the local office address, the local team and local transactions or cases.
2. Give each office its own place listing. In Dubai, every commercial real estate card ChatGPT showed traced back to a Dubai office record.
3. Find each market’s reference sources. In our sample they included lists of Dubai firms, a London brand survey and trade press, Singapore’s accounting regulator and a local review site, and the Hong Kong government’s directory of freight forwarders.
4. Check which AI answers appear for your questions in each market. Google showed no AI Overview for two of our six international questions. ChatGPT answered all six.

SaaS: no country versus a named country

Three pairs of questions about software sold worldwide, each asked once with no country and once for a specific one: CRM for small business (UK), project management for small teams (Germany) and payroll for small business (Canada).

None of the 12 software answers showed place data. They drew on comparison publishers, software directories, vendor pages, Reddit threads and YouTube reviews. The lists themselves changed from market to market.

The clearest sign of the connection’s location came from the CRM question with no country. Our ChatGPT test connection was in the Netherlands, and the answer added Teamleader Focus as “best for small businesses in the Netherlands and Belgium”, offered budget ranges in euros and cited a comparison of CRMs for small businesses in the Netherlands. Nothing in the question mentioned a country. OpenAI’s help center says ChatGPT may use an approximate location based on the IP address to provide relevant local results. The same connection didn’t change the project management or payroll answers. Both stayed in US dollars, and the payroll answer said it was “primarily for U.S. businesses” before asking which country we were in.

In Germany and Canada, ChatGPT replaced most of its no-country list. For project management in Germany, ChatGPT kept only Asana, noting that German data residency comes only with its Enterprise plans. The other four tools were pitched on German hosting, EU hosting, German small businesses or self-hosting. Payroll depends on local tax rules. ChatGPT’s Canadian answer was built around CPP and EI deductions, T4 forms and provincial rules, Google’s around T4 forms, CRA remittances and multi-province compliance. Both cited the global brands that stayed through their Canadian sites: QuickBooks Canada in both answers, adp.ca in ChatGPT’s.

For the UK CRM question, Google put Freshsales first and cited Startups.co.uk, which calls Freshsales the best CRM for small businesses. ChatGPT cited two other UK comparisons, left Freshsales out and quoted prices in pounds from those comparisons.

What to do

1. List the comparison publishers and software directories cited in each target market, and keep your product information there current and accurate.
2. If your category depends on local rules, such as payroll, tax or data residency, build a real local version: a country site or section, the local compliance details and local pricing. In both Canadian payroll answers, the global brands that stayed were cited through Canadian sites.
3. Make your use case obvious. Every software answer grouped products by use: best free option, best for sales pipelines, best for sole traders, best for German small businesses.
4. Keep local-currency prices current on each market’s comparison sites, and keep a pricing page that answers can link to.

What changes from one market to the next

Across the 45 answers, the evidence followed the place named in the question. One caution about the design: here the place and the kind of business change together. No software question named a city, and no service question named only a country. International categories asked for a city are the one cross-check, and their answers looked local.

Two more patterns held across the sample. Forty-four of the 45 answers ended with a question or an offer to narrow things down. And the systems treated company websites differently. ChatGPT mostly gave each company’s site as a “Website” link under its name, while Google cited company pages as sources more often. In local, state and international answers, those were almost always pages about that city, state or market.

Read the agreement figures with care. They come from one run per question and four to six questions per kind of business. The systems weren’t set up the same way: Google was set to each city, state or country, while ChatGPT ran from one connection outside the US. Overlap also depends on how long the lists are (Google named 14 firms for Singapore) and how crowded the market is. A city has many plumbers, while both London answers named the same five international firms. Our reading, which this sample can’t prove: local shortlists are the hardest to predict and to plan for.

How to run this check for your company

1. Write the buyer’s question for every market you sell in: your city, each state, each country and your category with no place at all.
2. Ask it in ChatGPT and in Google from your buyer’s location, or record where you asked from. A connection in another country can change the answer.
3. Record the date, the platform, whether you were logged in, the full answer and the sources behind each name.
4. Sort the sources by type: place records, rankings and lists, trade press, review and comparison sites, your own pages.
5. Strengthen the kind of evidence your market depends on before you write more pages.
6. Repeat the check under the same conditions and compare.

Our article on AI search optimization walks through recording the conditions and reading the sources step by step.