Ask an assistant who to hire and you get two or three names. Whoever's on that list wins the shortlist without competing. This is a genuinely new distribution channel, and almost nobody in Canada is working on it yet.
How these answers get made
Roughly: the model retrieves sources relevant to the question, synthesises across them, and names the businesses that appear consistently and unambiguously. That last part is the key insight. Unlike classic search, where one strong page can rank, AI answers reward consensus — the same facts about you, repeated across many independent sources.
Which means what other sites say about you often matters more than what your own site says.
The five things that move the needle
1. Make your entity unambiguous
The model needs to know exactly what your business is, where it operates, and what it does. That means identical name, address, phone and description everywhere; complete Organization and LocalBusiness schema; a properly filled Google Business Profile; and consistent LinkedIn, Crunchbase and directory profiles. Conflicting information doesn't just fail to help — it actively suppresses you, because the model can't resolve which version is true.
2. Write content a model can quote cleanly
Lead with the direct answer, then explain. Use real headings phrased as the questions people ask. Include specifics — numbers, prices, timelines, specifications — because vague marketing prose has nothing extractable in it. Comparison tables and clear definitions get lifted constantly.
3. Get named on third-party pages
"Best [service] in [city]" listicles, industry directories, association member lists, local news, and yes — Reddit and Quora, which several engines lean on heavily. You cannot manufacture these credibly at scale, but you can earn a steady handful, and a handful is often enough in a Canadian city.
4. Let the right crawlers in
Check your robots.txt actually permits the AI crawlers you want reading you. Plenty of sites block them by accident through a security plugin or an overzealous CDN rule. An llms.txt summary file is a low-cost addition worth having while conventions settle.
5. Reviews, with text
Detailed reviews mentioning specific services and locations are quotable evidence. Star ratings alone are not.
How to measure it
Write 20–30 prompts your buyers would realistically use — service plus city, "best X in Y", "who should I hire for Z". Run them against ChatGPT, Gemini, Perplexity, Copilot and Google's AI Overviews on a schedule, and log every business named. That gives you a share-of-voice percentage that moves over time, which is far more useful than a screenshot.
Is it worth doing yet?
If your buyers research before they call — professional services, healthcare, high-ticket home improvement, B2B — yes, and the entry cost today is a fraction of what SEO costs in an established market. If you sell impulse purchases or emergency call-outs, keep your focus on the map pack for now.
The honest caveat: nobody can guarantee an AI recommendation. These systems are probabilistic and change without notice. What you can do is measure your citation rate, improve the signals that drive it, and watch the trend — which is exactly what our AEO service does.
Quick answers
Does good SEO automatically get me cited?
It helps but isn't sufficient. Answer engines weigh cross-source consensus and structured facts far more heavily than ranking position, so businesses that rank well can still be invisible in AI answers.
How often should I check my AI visibility?
Monthly. These systems change frequently enough that quarterly checks miss the movement, and daily checks are just noise.