AI Answers on HubSpot Support Providers: Canada vs US vs Mexico

AI answers about HubSpot support providers can differ between Canada, the US and Mexico, but location is only one cause. One citation analysis found query language matters more than geography, varying by model, so a Spanish prompt from Mexico may shift cited sources more than an English prompt moved between Toronto and Chicago. This post compares one HubSpot support question across the three markets on brands named, domains cited and language, then gives you an audit template.

Key takeaways

• SparkToro data showed less than a 1-in-100 chance that ChatGPT or Google's AI returned the same brand list on repeated runs of the same prompt, so one screenshot proves nothing.
• Profound found that query language outweighed geography in shaping citations, an effect that varied by model, with Spanish prompts surfacing social content at roughly 1.4x the English rate in Google AI Overviews and roughly 0.5x in ChatGPT.
• Gemini Apps always collect location data to keep responses relevant, so the same question from Toronto, Chicago and Mexico City can return different providers.
• The three prompts in this post are worded differently on purpose, as real buyers write, so any gap between them mixes wording, language and location.
• Before trusting an AI shortlist, check support scope, response times against HubSpot's one working day email reply note, language coverage, time zones and integration depth.
• A three-market, two-engine audit with five runs per cell is 30 runs, about two hours a month by the worked estimate in this post.

Do ChatGPT, Gemini and other answer engines change their HubSpot support answers by location?

Yes, location can change the answer, but it is not the only cause. Gemini Apps, for example, collect location data to make responses relevant, and engines also return different brand lists on repeated runs of the same prompt, so one answer from one city tells you little.

Start with what the engines say about themselves. Google's Gemini Apps Privacy Hub states: "Location data is always collected if you use Gemini Apps so that they can provide you with a response that is relevant to your query." It adds that, with your permission, Gemini Apps also process precise location data from your device. A buyer in Toronto and a buyer in Mexico City asking about HubSpot support can receive answers shaped by where they are.

Location sits on top of a larger source of noise. In SparkToro's research on AI brand recommendations, 600 volunteers ran 12 different prompts through each of 3 tools a combined 2,961 times. Its conclusion: "The bottom line is: AIs do not give consistent lists of brand or product recommendations." Search Engine Journal's summary of the SparkToro data reports a less than 1-in-100 chance that ChatGPT or Google's AI in Search returned the same list of brands across repeated runs of the same prompt.

So the useful question is not "which providers does ChatGPT name in Canada?" but "how often does each provider appear across many runs in each market?" This post answers it in three steps:

1. Compare three buying prompts (Canada in English, the United States in English, Mexico in Spanish) on brands named, domains cited and language.
2. Separate what location explains from what wording and language explain.
3. Turn the comparison into an audit template your revenue team can run on its own category.

Answer engine optimization (AEO) is the work of getting your company named and cited in answers from tools like ChatGPT, Gemini and Google AI Overviews. The audit below is its measurement half.

How do the brands named and domains cited differ between Canada, the US and Mexico?

A hypothesis to test in your own runs: the US and Canada lists may overlap more than either overlaps with Mexico, because a Spanish prompt changes language as well as location. Because buyers phrase the question differently in each market, any gap mixes wording, language and location, so never credit it to location alone.

The tracking set uses one need, ongoing HubSpot support for a mid-size B2B company, written the way a buyer in each market might type it. The prompts are deliberately not identical. That mirrors real behavior: reviewing human-written prompts in SparkToro's prompt analysis, the author wrote, "Across 142 responses, there were barely two prompts that, if you squinted, looked similar to me at all."

No verified result set for these exact prompts is published here, so the brand and domain cells state what to log and what the cited research suggests you will see. Fill them with your own runs.

Read the rows in pairs. Canada against the US holds language constant, so gaps there come from wording, location and run-to-run noise. The US against Mexico also changes language, which the next section shows is a strong force. Keep engines apart too: Ahrefs' comparison of Google AI Mode and AI Overviews found that they cited the same URLs only 13.7% of the time, so mixing the two can look like a country gap when it is a feature gap.

How does asking in Spanish or Quebec French change the providers and sources AI shows?

Language can outweigh location. Profound's 2026 citation analysis found the query's language shaped AI citations more than the country's geography, with effects that varied by model.

Profound's analysis of how query language reshapes AI citations put it directly: "We found that a country's geography is secondary to a much more powerful force: the language of the query, but it varies by model." It reported that Spanish prompts surface social content at roughly 1.4x the English rate in Google AI Overviews and roughly 0.5x in ChatGPT, regardless of where the Spanish query originates. The same Spanish question can push one engine toward social posts and another away from them.

French matters if you sell into Quebec. Temso's analysis of over 7 million AI citations covered four major models, six non-English languages and 47 industry verticals. In its comparison of local-language sources, French performed strongest on Microsoft Copilot at 87.2%. Comparing the four models' local-language citation rates, Temso reports: "All pairwise differences are statistically significant (p < 0.0001 for every comparison)."

An academic audit points the same way in a different category. A 2026 paper on AI Overviews in health audited 1,920 health queries across 12 countries and 4 languages and found that "Issuing the same query in a country's official language rather than English raises the share of locally sourced citations approximately 3.5- to 13.5-fold." That is health, not HubSpot support, so treat it as a direction rather than a forecast.

What this means for your audit:

• Mexico in Spanish: log social and Spanish-language domains in their own columns, per engine.
• Quebec in French: add a French prompt from Montreal and include Copilot if your buyers use it.
• Control run: also ask the English version of the Mexico prompt from Mexico City. The gap between English and Spanish runs from the same place shows what language alone changes.

Why do a few HubSpot support providers appear again and again, and why can firms with local presence be missed?

In narrow categories, engines tend to repeat the same top names in most answers. Firms with strong local presence but little content the engine cites can stay out of those answers.

Repetition is normal in tight categories. In SparkToro's data, "when Google’s AI was asked to recommend digital marketing consultants with expertise in e-commerce, the Smartsites agency appeared in 85 out of the 95 responses." Search Engine Journal added: "In tight categories like cloud computing providers, top brands appeared in most responses." HubSpot support is arguably a narrow category, so expect a short set of recurring names in each market.

Two forces may work against firms with local presence but low AI visibility:

1. Source concentration. The health audit found that Google and Baidu "exhibit vertical integration, routing users toward their own company platforms in AI Overviews rather than a diverse set of primary sources." That finding covers health queries; if a similar pattern holds for HubSpot support, a provider absent from the platforms an engine favors may have fewer ways in.
2. Thin local content. The same paper found that "Smaller, lower-localization countries receive fewer domestically sourced references for health queries." A Mexican provider with little Spanish content to cite may lose ground to firms with more pages.

For the basics of why engines skip a company, read Why Doesn't AI Recommend Your Company? AEO Guide before you run the location audit.

What should you check before trusting an AI-generated shortlist of HubSpot support providers?

Treat the shortlist as names to verify, not a ranking. Check each provider on support scope, response times, language coverage, time zones and integration depth, then look it up in HubSpot's own directory.

HubSpot's own support gives you two benchmarks. Its Get help with HubSpot page states, "Currently, phone support is offered in English only," and says chat, callback and email support are available in a listed set of languages. For email support requests, it tells customers, "You should receive a reply within one working day." A provider you pay should at least match that and cover every language your teams work in.

Next, cross-check every name. HubSpot's guide to the Solutions Directory says it "applies to customers who want to hire a HubSpot Partner using the HubSpot Solutions Directory." Look each AI-named firm up there and interview at least two that pass your criteria.

Digitalegy is a HubSpot partner. If you need ongoing help and are comparing providers after reading what AI engines suggest, review Digitalegy's HubSpot support service against the same criteria you apply to everyone else.

How do you run the same location comparison for your own category, and how long does it take?

Pick one buyer question, write an equivalent version per market and language, run each from its location in at least two engines, log brands, cited domains and language, and repeat monthly. A three-market, two-engine audit takes about two hours by the worked estimate in this post.

Log one row per run in this template:

1. Pick one buyer question your customers actually ask in sales calls.
2. Write equivalent versions per market and language. Have a native speaker check the Spanish and French, and keep a literal translation as a control.
3. Run each version from its location in at least two engines. Log AI Overviews and AI Mode separately: Ahrefs notes that "87% of the time, these two systems are pulling from completely different sources to answer the same query."
4. Run each prompt five times per engine and market, each in a fresh session.
5. Log brands, cited domains, answer language and settings.
6. Calculate percent visibility per brand per market.
7. Repeat monthly and compare trends, not single runs.
8. Score the providers that appear against fair selection criteria (scope, response times, language, time zones, integration depth), never against how often AI names them.

Percent visibility is the share of runs in which a brand appears. SparkToro wrote: "Measuring that percent visibility is (probably) a reasonable way to know how prominent or invisible your entity is within the AI’s consideration set." Hypothetical example: Provider A appears in 4 of 5 ChatGPT runs from Toronto (80%), 3 of 5 from Chicago (60%) and 0 of 5 from Mexico City (0%), so 7 of 15 overall (47%).

Time, as a planning estimate: 3 markets x 2 engines x 5 runs = 30 runs. At about 2 minutes to run and paste each, that is 60 minutes; coding brands and domains at about 1 minute per run adds 30; comparison and scoring add 20 to 30. Total: roughly two hours a month. Each extra market prompt run in two engines adds 10 runs (about 40 minutes); logging AI Mode separately across three markets adds 15 runs (about an hour).

What are the limits and risks of tracking AI answers by country?

Small samples, answers that change between runs, personalization and different wording per market can all create gaps that look like country effects. Treat results as directional and report the sample size with every figure.

Compare your sample with the research. SparkToro's data came from 2,961 runs. In a 5-run cell, one answer moves a provider's visibility by 20 points. Larger studies have limits too. Search Engine Journal notes, "The report is not peer-reviewed academic research," and that "Survey respondents used their normal AI tool settings without standardization, which the authors said was intentional to capture real-world variation." Ahrefs states: "This analysis compares single generations of AI Mode and AI Overview responses."

• Small samples: show n next to every percentage and ignore single-month swings.
• Changing answers: judge trends over three or more months.
• Personalization: Gemini Apps process precise device location with permission, so log settings and use logged-out sessions where you can.
• Different wording per market: keep the literal translation control to separate wording from location.
• Engine updates: note the date of any visible model or feature change.

What do these country differences mean for a company that sells in more than one market?

Treat each market and language as its own search environment: track it separately, publish content in the buyer's language, and choose providers who cover the languages and hours of every market you sell in.

One English report will not cover Canada, the US and Mexico. Profound's conclusion is blunt: "The most critical operational shift: tracking prompts in local languages is not optional." Because Spanish prompts drew social content at roughly 0.5x the English rate in ChatGPT but 1.4x in Google AI Overviews, your Spanish content plan depends on which engine Mexican buyers use. Temso agrees: "Choosing where to invest in non-English content depends not only on your audience, but on which AI platforms your audience uses."

• Buyers choosing a provider: require support in each market's language and hours, since HubSpot says phone support is currently offered in English only, while chat, callback and email cover a listed set of languages.
• Content teams: write Spanish and Quebec French pages for the questions buyers ask there, not literal copies of English pages.
• RevOps: report visibility by market next to pipeline by market, so content spend follows revenue.

Digitalegy serves companies in Canada, the United States and Latin America. Digitalegy is a HubSpot partner. Canadian companies can see Digitalegy's page for companies in Canada, and US companies can see Digitalegy's page for the United States. For help acting on what the audit shows, see Digitalegy's AEO service, and for the content side read How to rank in AI search results in 2026.

Frequently asked questions

Can I use a VPN to see what buyers in Mexico see?

Only partly. Google's Gemini privacy page says Gemini Apps also process precise location data from your device with your permission, so a VPN may not change every signal. Log how location was set, or ask a colleague in the market to run the prompts.

How many runs per prompt are enough?

Five runs per engine per market is a practical monthly minimum. That is our recommendation, not a research threshold. Fewer runs make each answer too large a share of the result.

Should I use logged-in or logged-out sessions?

Use logged-out sessions where you can, and log the setting for every run either way. In the SparkToro study, respondents used their normal AI tool settings without standardization, and Gemini Apps process precise device location with your permission, so settings are part of what you measure.

Which engine should I add first for Quebec buyers?

Start with the engines your Quebec buyers actually use, and include Microsoft Copilot if they use it. Temso found that French performed strongest on Copilot at 87.2% in its comparison of local-language sources.

Should I log the answer's language when it differs from the prompt's?

Yes. The template's Language field records both the prompt language and the answer language, so a Spanish prompt that returns an English answer stays visible instead of being mixed into your Spanish results.

Can I use this template for categories other than HubSpot support?

Yes. Swap in your own buyer question and selection criteria; the fields, run counts and monthly rhythm stay the same.

Sources

1. Gemini Apps Privacy Hub - Gemini Apps Help, Google (October 26, 2026)
2. NEW Research: AIs are highly inconsistent when recommending brands or products; marketers should take care when tracking AI visibility, SparkToro (January 28, 2026)
3. AI Recommendations Change With Nearly Every Query: Sparktoro, Search Engine Journal (January 30, 2026)
4. How query language reshapes AI citations, Profound (April 21, 2026)
5. Are AI Mode and AI Overviews Just Different Versions of the Same Answer? (730K Responses Studied), Ahrefs (December 15, 2025)
6. 7 million AEO source citations analyzed: how AI Models Differ on Local-Language Sources, Temso AI (April 20, 2026)
7. Who Anchors AI Overviews in Health? Baidu, Google, and the Geography of Authority, arXiv (Mingyue Zha, Ho-Chun Herbert Chang) (September 6, 2026)
8. Get help with HubSpot, HubSpot (November 4, 2026)
9. Use the HubSpot Solutions Directory, HubSpot (February 23, 2026)