Do assistants recommend different suppliers in different Canadian sectors, and who you should hire because of it

In the Canadian data the engines reach for software in almost every sector and for Canadian agencies in exactly one: marketing services itself. Across 64 blind readings of ChatGPT and Gemini on 22 October 2026, software products took 69 namings and six Canadian marketing agencies took 21. No Canadian professional practice, no local trade business and no individual practitioner was named anywhere. The engines also declined to name anybody at all in 33 of the 64 readings. The hiring consequence is specific: if you want to be the thing an engine names in your Canadian sector, you need somebody who can produce the artefacts software companies produce, and almost no Canadian sector hires for that today.

What the readings show, by question shape

Question type in the bank	Supplier type named	Readings naming nobody
Choosing and vetting a supplier	Canadian agencies and software	Moderate
Pricing and contracts	Mostly nothing, with ranges instead of names	High
Channel and platform questions	Software, consistently	Lower
Sector specific buyer questions	Software, with Canadian sector context around it	Moderate
Procurement and how buyers buy	Mostly nothing, with Canadian federal sources cited instead	High

The bank was 198 Canadian buying questions in 33 batches, put to both engines through a data provider with Canada named inside the text of every question, because the provider accepts gl, hl and location parameters and ignores all three. Batch 33 names us in the question and is a ceiling measurement, excluded throughout. Sixty four blind readings, 32 per engine, 2,992 citation URLs across 974 distinct hosts.

The namings, in full

• Software: Semrush 16 readings, HubSpot 12, Ahrefs 9, Profound 2, Peec 2, Otterly 2, Brandwatch 1, SE Ranking 1, BrightLocal 1, Moz 1, Rankscale 1. Sixty nine namings.
• Canadian marketing agencies: Major Tom 8, Konstruct 6, Jelly Digital 2, Seota 2, Pilot Digital 2, Edkent 1. Twenty one namings.
• Canadian professional practices, local trade businesses and individual practitioners: zero.
• Readings naming nobody at all: 33 of 64.

Why it happens, mechanically

The explanation is in the citation list rather than in any preference. The hosts the engines leaned on were documentation and official reference material: developers.google.com in 17 readings, support.google.com in 14, torontoseo.com in 13, clutch.co in 12, canadacreate.com in 11, canada.ca in 10, wisemedia.io in 9, semrush.com in 9, competition-bureau.canada.ca in 9, ised-isde.canada.ca in 9, blog.google in 8, www150.statcan.gc.ca in 8 and ottawaseo.com in 7. One entry, ccn.web.tr, is the data provider's own fetch infrastructure rather than a publisher and is excluded from source analysis.

An engine composing an answer needs something attributable. Software companies publish prices, specifications, documentation and changelogs, dated and checkable. The six Canadian agencies that got named had attributable published material about them too. Canadian professional practices and trade businesses publish approaches and self written case studies. Given that choice the engine quotes the first two.

The hiring consequence

• Hire or train somebody who can write a specification. What arrives, how many, in what format, by when, what is excluded. A documentation skill rather than a copywriting one, and most Canadian marketing teams do not have it.
• Put a published fee position in somebody's objectives. Getting a dated, defensible figure published in Canadian dollars with its tax treatment is usually an internal negotiation, so it needs an owner with standing.
• Make one person responsible for counting something. A counted figure with a denominator, built on Canadian official data where that helps, is the most quotable asset a Canadian services business can produce and nobody owns it by default.
• If Quebec is a market, hire the French writing rather than adding it to a job description. The Charter of the French Language makes French a requirement for selling pages, and a French specification is a separate asset from the English one.
• Do not open a role for assistant optimisation. The experienced pool is tiny in Canada. Hire for judgement and documentation skill and develop the specifics with a real measurement.

The engine split, and why a single score would mislead

Gemini named a rival in 18 of its 32 Canadian readings and ChatGPT in 13. ChatGPT named nobody at all in 19 readings against Gemini's 14. ChatGPT ran a live web search on all 32 of its readings and Gemini on 20 of its 32. So the two surfaces differ on both willingness to name and recency of reading, and a blended Canadian score would hide both. Any supplier reporting one number should be asked to split it.

How to check your own Canadian sector

• Write twelve buying questions a buyer in your sector would ask, with Canada or the province named in each, and a French set if Quebec applies.
• Put each to both engines, save the answers and source lists, and record the date, engine and language.
• Classify every named supplier as software, agency, practice, practitioner or local firm. The mix is your answer.
• Count the readings that named nobody. Those are the openings and they are cheapest to take.
• Report the engines separately with their own denominators.

If you want the gates an engine applies before naming anybody, they are set out in the five gates. If the question behind yours is why a competitor gets named and you do not, that is worked through in why your competitor is recommended and you are not.

Questions about supplier types in Canadian assistant answers

Does my Canadian sector get different treatment?

Software dominated every question shape in our bank, with Canadian marketing agencies the one exception. Check your own sector with twelve questions rather than assuming, because our bank is broad rather than deep in any vertical.

Why were Canadian agencies the only firms named?

Because enough attributable published material exists about them. Twenty one namings across six firms is thin and it is not zero, which sets a reachable standard for other Canadian sectors.

Should a Canadian practice publish like a software company?

In what it publishes, yes. Fee positions, specifications and dated counted figures are the artefacts the engines quoted. That is a publishing choice rather than a change of business model.

Why do so many readings name nobody?

Because the engine had nothing specific enough to attribute. Thirty eight of 64 is the largest single outcome and the cheapest opening in the Canadian set.

Which asset moves it fastest?

A dated fee position in Canadian dollars, because it is short, almost always missing, and directly quotable.

Classifying your own Canadian namings rather than guessing

The question of what kind of supplier an engine names in a Canadian sector is answerable only by reading the answers and classifying them, which is how the 69 and 21 figures above were produced. AI Knows Us reads ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews with Canada named inside the question, records who was named and what each answer cited, reports the engines separately with their denominators, and keeps the raw answer text so a classification can be checked. Outside the United States and the United Kingdom the cheapest plan that reads all five engines is Growth at 199 dollars a month with 100 buyer questions watched, read on aiknowsus.com/pricing and aiknowsus.com/llms.txt on 22 October 2026.