AI
LLM SEO: How to Optimize Your Site So AI Models Actually Find You
By Kyle Senger
15+ years in local marketing; Google Ads certified; Shopify Partner.
Here's a question worth sitting with: when someone asks ChatGPT to recommend a plumber in Saskatoon, a family lawyer in Calgary, or a dental clinic in Toronto, does your business come up?
Most Canadian SMB owners have no idea. And that's the gap LLM SEO is trying to close.
This isn't about chasing a trend. It's about understanding that search behaviour has genuinely shifted. ChatGPT hit 800 million weekly active users in 2026. Perplexity is growing fast as an AI-native research tool. Google's own AI Overviews now appear on roughly 39% of informational queries. People are asking AI tools questions, and those tools are pulling from the web to answer them. If your site isn't structured in a way that makes it easy for large language models to read, understand, and cite, you're invisible to a growing chunk of your potential customers.
LLM SEO is the practice of making your website and content legible to large language models, so they can accurately represent your business when someone asks a relevant question. It overlaps with traditional SEO in some ways, and diverges from it in others. This article covers the divergence. For the broader picture of how AI is changing marketing for Canadian businesses, our complete guide to AI for marketing is a good starting point.
What LLMs Actually Do With Your Website
Traditional SEO is built around getting Google's crawler to understand your pages and rank them for specific queries. You optimize title tags, build backlinks, earn authority. The mechanism is: search query → ranked list → user clicks → your site.
LLM SEO is different. The mechanism is: user question → AI model generates an answer → model cites sources or names businesses → user may or may not click through.
That's a meaningful shift. You're no longer just competing for a click. You're competing to be named.
Here's how large language models actually process your site. They don't crawl it in real time the way Google does. Most LLMs are trained on large datasets of web content collected up to a specific cutoff date. When a model like GPT-4o or Claude is asked a question, it draws on patterns from that training data, plus, in some cases, live retrieval from the web if the model has that capability enabled.
So there are two pathways your site can influence an LLM's answer:
Training data inclusion. If your content was on the web and publicly crawlable before the model's training cutoff, it may have been included in the training corpus. Quality, clarity, and citation-worthiness all affect whether your content made it in and whether it was weighted positively.
Retrieval-augmented generation (RAG). Models like Perplexity and ChatGPT with browsing enabled actively retrieve live web content when answering queries. They pull pages, read them, and synthesize answers. Here, your current site structure, page speed, and content clarity matter a lot.
Both pathways reward the same underlying thing: content that is clear, specific, well-structured, and credible. Which sounds like good SEO advice from 2015. And it kind of is. But the execution is different enough that it's worth walking through in detail.
For a deeper look at how AI is changing search specifically, the AI SEO playbook covers the broader strategic territory. And if you want to understand how Google's own AI features fit into this, Google AI Mode and how to rank in AI Overviews are worth reading alongside this article.
Why Traditional SEO Signals Don't Fully Translate
If you've done SEO before, some of what you've built will carry over. A fast site, clear page structure, strong content, inbound links from reputable sources. Those things still matter.
But some traditional SEO moves are close to irrelevant for LLM visibility.
Keyword density. LLMs don't count keyword instances. They understand meaning. A page that says "plumber in Regina" seventeen times isn't going to be cited more than a page that clearly explains what the plumber does, what areas they serve, and what makes them worth calling.
Meta descriptions. Useful for Google click-through rates. Largely ignored by LLMs pulling content.
Exact-match anchor text. The link-building playbook around exact-match anchors has almost no bearing on whether an LLM cites your content. What matters is whether authoritative sites mention your business by name in context.
Thin pages with strong backlink profiles. An LLM reading your site is essentially asking: "Does this page actually answer the question?" A 300-word page with 40 backlinks doesn't answer much. A 1,200-word page that walks through a real process, with specific details, does.
Here's the thing. The shift from keyword-matching to meaning-matching is genuinely good news for small businesses that have real expertise. A plumber in Moose Jaw who writes a detailed, honest explanation of how to diagnose a leaking water main has a better shot at being cited by an LLM than a national directory page stuffed with location keywords.
That's the piece that most people miss. LLM SEO rewards genuine knowledge, expressed clearly.
The Technical Side: What LLMs Need to Read Your Site
This is where LLM SEO gets specific. There are a handful of technical moves that make your site more legible to AI models, and most of them are straightforward.
llms.txt
This is a newer convention, similar in concept to robots.txt. An llms.txt file sits in your site's root directory and tells AI crawlers what content is available, what's most important, and how to understand your site's structure. It's not a ranking factor in the traditional sense. It's more like a map you're handing to the model. We have a full setup guide at llms.txt: Setup Guide for SMB Websites.
Structured data and schema markup
Schema markup is code you add to your pages that explicitly tells machines what type of content is on the page. A LocalBusiness schema tells a model your name, address, phone number, hours, and service area. An FAQ schema presents questions and answers in a format that's easy to extract. A Review schema signals credibility.
Per DataForSEO data, "ai seo" has a Canadian search volume of 1,000 queries per month at a CPC of CA$21.33. That's a competitive space. Schema is one of the cleaner ways to differentiate. Our schema markup for AI search guide covers which schema types actually move the needle.
Crawlability for AI bots
Two bots you need to know: GPTBot (OpenAI's crawler) and ClaudeBot (Anthropic's crawler). By default, most sites allow them. But if you've been aggressive with your robots.txt file, you may have accidentally blocked them. Worth checking. We cover both in detail: GPTBot explained and ClaudeBot in robots.txt.
Page structure
LLMs parse content hierarchically. Clear H1, H2, H3 structure helps a model understand what a page is about and what its sub-topics are. Short paragraphs, direct answers near the top of each section, and minimal jargon all help. If your page buries the answer in paragraph six, the model may not weight it as the answer.
What Your Content Actually Needs to Say
Technical setup gets you in the door. Content is what keeps you there.
I think the clearest mental model for LLM-friendly content is: write for someone who is going to read your page and then summarize it to a third party. What would they say? Would they have enough specific, credible information to recommend you confidently?
Here's what that looks like in practice.
Named specifics. Vague content gets vague citations, or none. "We serve the Regina area" is less useful to an LLM than "We serve Regina, White City, Balgonie, and Emerald Park, with same-day appointments available for emergency calls." The second version gives the model something to work with.
Direct answers to common questions. FAQ sections aren't just good UX. They're structured in exactly the format LLMs use to answer questions. A question, followed immediately by a clear answer, is easy to extract and cite. Our answer engine optimization guide goes deeper on this.
Credibility signals woven into the content. Certifications, years in business, professional associations, specific project outcomes (without fabricating numbers), media mentions. LLMs weight these signals when deciding whether a source is worth citing.
First-person expertise. A page written by someone who clearly knows the topic reads differently to an LLM than a page assembled from scraped content. This is one reason the trend toward AI content writing requires care. AI-generated content that lacks genuine expertise can actually hurt your LLM visibility, because models are getting better at recognizing it.
In my experience, businesses that have been producing genuine, specific content for a few years, even if their traditional SEO isn't polished, tend to get cited more readily by LLMs than businesses with technically strong SEO but thin content. The model is looking for a trustworthy source. Be that.
A Week-by-Week LLM SEO Audit You Can Actually Run
This is the operational piece. If you want to know where you stand and what to fix, here's how I'd approach it over about four weeks.
Week 1: Baseline your current AI visibility.
Ask ChatGPT, Perplexity, and Claude variations of the questions your customers actually ask. "Who are the best [your service type] in [your city]?" "What should I look for in a [your profession] in Canada?" "Can you recommend a [your service] near [your neighbourhood]?"
Note whether your business is mentioned. Note who is mentioned instead of you. That's your competitive set for LLM SEO purposes. Our AI search visibility guide has a more structured framework for tracking this over time.
Week 2: Technical audit.
Check your robots.txt file for GPTBot and ClaudeBot blocks. Verify your schema markup using Google's Rich Results Test. Look at your llms.txt file , if you don't have one, add it. Run a crawl with Screaming Frog or Ahrefs to identify pages with thin content (under 400 words), missing H1s, or broken internal links.
Week 3: Content gap analysis.
Compare the questions you found in Week 1 to your existing content. For each question an LLM answered without citing you, ask: do we have a page that answers this? If yes, is it specific enough? If no, add it to your content calendar.
Typically, businesses I've audited are missing content on two or three categories: their process (how they actually do the work), their service area specifics, and their credentials. Those three gaps are usually the highest-priority fixes.
Week 4: Build the missing pieces.
Write or rewrite the pages you identified. Each page should: answer the primary question directly in the first two paragraphs, include named specifics (location, credentials, process steps), have proper schema markup, and link to related pages on your site.
Then re-run your Week 1 tests. It won't happen overnight, but you should see movement within 60-90 days as AI crawlers re-index your content.
For a more structured version of this process, our AI SEO audit guide walks through it step by step.
Citations: The Currency of LLM SEO
In traditional SEO, backlinks are the primary trust signal. In LLM SEO, citations are.
A citation in this context means: another credible source on the web mentions your business by name, in a relevant context. This could be a local news article, an industry directory, a professional association listing, a review platform, or a guest article you wrote for a trade publication.
When an LLM is trained on or retrieves content that includes these mentions, it learns to associate your business name with the topic and location. That's how you get named when someone asks a relevant question.
Per 2025 Microsoft research, 71% of Canadian SMBs are now using AI tools in some capacity. But most of them haven't thought about how to be visible to AI tools. That's the gap you can close right now, before this becomes as competitive as traditional SEO.
The practical moves here: get listed in credible Canadian directories (Clutch.ca, UpCity Canada, your provincial chamber of commerce). Earn reviews on Google Business Profile. Write for industry publications. Get quoted in local media. These aren't new ideas. But their value has compounded now that LLMs are reading and weighting them.
Our full guide on earning AI citations from ChatGPT and Perplexity covers the specific tactics in more detail.
3 Takeaways to Finish
LLM SEO is a real discipline, not a rebrand of the same old advice. Here's what I'd want you to remember.
First, the technical foundation is straightforward. Check your AI bot crawlability, add an llms.txt file, implement schema markup, and make sure your site structure is clean. Most Canadian SMBs haven't done this yet. It's not complicated work, and it's worth doing now.
Second, content quality is the actual differentiator. Specific, credible, clearly structured content written by someone who knows the topic is what LLMs cite. Thin pages optimized for keyword density don't cut it. If you've been producing genuine expertise content, you're further ahead than you think.
Third, citations from other credible sources matter. Being mentioned by name in relevant contexts, on authoritative sites, is how LLMs learn to recommend you. Traditional PR, directory listings, and review platforms all feed into this.
If you want to go deeper on the broader AI search picture, generative engine optimization and how to optimize for AI search are the next logical reads.
