Buyers stopped scrolling — they ask ChatGPT, Claude, Gemini and Perplexity and trust the answer. We make Toronto businesses the one large language models name first, and keep you there.
What's included
Getting recommended by AI comes from the whole stack working together — a clean, well-structured entity the models can parse, citations across the sources they trust, and fresh, machine-readable content. We build and run all of it as one managed program.
A clean, unambiguous identity — consistent name, NAP and facts everywhere — so models know exactly who you are and quote you confidently.
Optimized to be named first when buyers ask ChatGPT, Claude, Gemini or Perplexity for the best in your Toronto market.
Earned references across the trusted sources LLMs retrieve and train on — so the models keep seeing you cited as the answer.
A fast, crawlable site with clear answer-first pages and an llms.txt — structured exactly the way models extract and quote.
Crisp, direct answers to the exact questions buyers ask AI — the shape models lift verbatim into a recommendation.
Organization, FAQPage and sameAs markup so models can read, attribute and trust your facts without guessing.
We keep your facts current and track which LLMs recommend you — so you stay the default answer, not last year's data.
The goal of every lever: when AI is asked who to hire in your market, your brand is the one name it gives.
Being invisible to a model wins you nothing. Being one of five names is barely better. We climb the ladder — from mentioned, to listed, to the linked source, to the single business AI recommends when a Toronto buyer asks who to hire.
The Patrick OS dashboard tracks whether each model recommends you — ChatGPT, Claude, Gemini, Perplexity and Copilot — refreshed every 24 hours. Not a monthly PDF. A live file you log into the day you sign.
Toronto coverage
Buyers ask models location-aware questions — "best in Yorkville", "near me", "in the GTA". We make Toronto businesses the name large language models give for every neighbourhood and market they actually serve, not just one generic city query.
What gets you recommended in Toronto
A clear entity, earned citations and fresh content compound on each other — so we work them in sequence, not all at once.
The first lever: an unambiguous identity models can read without guessing — clean facts, consistent NAP and brand, and Organization schema — so AI knows exactly who you are.
Models recommend who the trusted web vouches for. We earn the citations and mentions across the sources LLMs retrieve and train on — so they keep seeing you as the answer.
Models favour current, machine-readable facts. We keep your llms.txt, FAQs and answers fresh — so AI reaches for you, not a competitor working from last year's data.
How we work
No mystery, no 12-month lock-in. Here's exactly what happens after you reach out.
We test what ChatGPT, Claude, Gemini and Perplexity say today when buyers ask who to hire in your market, audit your entity, schema and content, and show you who AI recommends instead of you.
A clean, parseable entity: consistent NAP and brand facts, Organization and FAQPage schema, an llms.txt and answer-first pages — so models can read, attribute and quote you with confidence.
The compounding work: earning citations and mentions across the sources LLMs retrieve and train on, and the entity signals that make AI treat you as the business worth recommending.
A live dashboard of which models recommend you, refreshed daily — not a monthly PDF. We double down on the prompts and surfaces winning you the most recommendations.
LLM SEO, done differently
Here's the pattern we see when businesses come to us from other agencies — and how we work instead.
Patterns drawn from businesses that have migrated to us from other agencies. Read the case studies →
LLM SEO
LLM SEO — also called large language model SEO — is the work of making a business the one AI tools like ChatGPT, Claude, Gemini and Perplexity name when someone asks them for a recommendation. Instead of fighting for a blue link, you're being optimized to be the answer the model gives.
A growing share of buyers now ask an AI assistant "who should I hire in Toronto for this?" and act on the reply without ever opening Google. LLM SEO makes sure the model's reply is you.
Models recommend the business the wider web most clearly and consistently describes as the best fit. That comes down to three things: a clean, unambiguous entity they can parse (consistent name, facts and schema), citations and mentions across the sources they retrieve and train on, and fresh, machine-readable content that answers the question directly.
It's not about stuffing keywords. It's about being the clearest, best-attested answer so the model reaches for you with confidence.
Yes. We track and optimize for the models your buyers actually use — ChatGPT, Claude, Gemini, Perplexity and Copilot. They retrieve and weight sources slightly differently, so we tune the entity, schema and citation work to win across all of them rather than betting on one.
Your Patrick OS dashboard shows, model by model, whether each one currently recommends you and where you're climbing.
Nobody controls a model's output directly — and you should be wary of anyone who claims they can flip a switch. What we do control is the input: the entity, schema, citations and content the models read when forming an answer. Improve those and the recommendations follow.
Think of it the way reviews and reputation shape word of mouth. We can't dictate the sentence the model produces, but we can make you the obvious, best-supported business for it to name.
Schema and a clean entity are the technical foundation. Organization, FAQPage and sameAs markup spell out your facts in a structured form models can read without guessing, and a consistent entity — same name, NAP and brand everywhere — tells AI all those mentions are the same business.
We also publish an llms.txt and answer-first content so the models have a clear, machine-readable source to quote. Get this right and you stop being a fuzzy guess and become a confident recommendation.
We run the prompts your buyers would actually type — "best in Toronto for X", "who should I hire" — across ChatGPT, Claude, Gemini and Perplexity, and track whether you're recommended, mentioned, or absent, refreshed every 24 hours in your Patrick OS dashboard.
That's the real metric: your share of recommendations across the models, not a vanity ranking. You see exactly which prompts and surfaces are winning and where a competitor is still being named instead.
Entity and schema fixes can be reflected within weeks for models that retrieve live sources, like Perplexity and the search-grounded modes of ChatGPT and Gemini. Recommendations that depend on training data and earned citations build more slowly — expect meaningful movement in 3–6 months and compounding results by 6–12.
Anyone promising you'll be ChatGPT's top pick in 30 days is overpromising. During the free audit we'll tell you honestly what timeline to expect for your market.
It's a fair question. The shift is real but early — a meaningful and fast-growing slice of buyers now start with an AI assistant instead of a search bar, and they tend to act on the single name it gives. That's a winner-take-most surface, and the businesses building a clean entity now are the ones models will keep recommending later.
You don't have to bet the whole budget on it. The free audit shows you exactly what AI says about you today, so you can decide with real data rather than hype.
Traditional SEO competes for a ranking — a position in a list of ten links a human then chooses between. LLM SEO competes to be the answer itself: the one business a model names, often with no list to scroll. The foundations overlap (clean site, schema, authority), but LLM SEO leans far harder on entity clarity, citations across trusted sources, and machine-readable, answer-first content.
If you're a Toronto business focused on being recommended by AI, this page is your starting point. For ranking in Google's local results and Map Pack, see the general Local SEO page →