How buyers research before they reach you
A prospect no longer types a category query into Google and clicks the top result. They ask ChatGPT, Claude, Gemini or Perplexity for a recommendation, read the synthesised answer, and shortlist two or three names before they ever visit a website. If your brand is not in that synthesised answer, you are not in the consideration set, even if you rank first for the equivalent blue-link query. LLM SEO exists to put you into the answer the model gives back.
This is a different surface from the search results page. A language model does not return a ranked list of ten links. It composes a paragraph from its training data and the pages it retrieves live, then reads that paragraph aloud to the user. The work, therefore, is not about position one. It is about being present, correctly described, and quotable inside the sources the model trusts.
LLM SEO versus traditional SEO
The two disciplines share plumbing. Both reward schema markup, clean entity signals, fast crawlable pages and authoritative inbound links. But the optimisation target is different, and that changes what you measure.
- Traditional SEO optimises for a position in a list of blue links, measured by rankings and organic sessions.
- LLM SEO optimises for presence and accuracy inside a generated answer, measured by whether ChatGPT, Claude, Gemini and Perplexity name you, cite you, and get your details right.
A page can rank well and still lose. If a model summarises the topic from a competitor’s extractable content, the click never happens. Our job is to make sure the model’s summary is built from your brand, or at minimum that you appear inside it alongside the competitor.
The two layers models actually read
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Every answer a model gives is pulled from two places, and an LLM SEO programme has to address both.
- Training data, the static corpus the model learned from. Presence here comes from being mentioned consistently, over time, on domains that were crawled for training. It is slow to change but durable.
- Live retrieval, the pages the model fetches at prompt time when browsing is enabled. Perplexity, ChatGPT with search, Gemini and Copilot all retrieve live sources to ground their answers. This layer is responsive. Fix the right source pages and the answer can shift within retrieval cycles.
WeProms maps which domains feed retrieval for your category and targets presence there, while strengthening the consistent, long-term signals that shape how your brand sits in training data.
Entity clarity is the foundation
Language models reason about entities, the named things and the relationships between them. Before any content work matters, the model has to resolve which entity your brand refers to. Weak or inconsistent entity signals produce the most common LLM SEO failure there is: a model that describes you vaguely, mixes you up with a namesake, or fabricates your product details because it is guessing.
We tighten the signals that resolve your identity. Wikidata entries and the knowledge-graph records they feed pin down who you are. schema.org Organisation, Person, Product and LocalBusiness markup names your entities explicitly so a parser does not have to infer them. And we make your brand attributes, the founding city, the specialties, the key people, consistent across your own site, Google Business Profile and the directories models read. When every source agrees on who you are, models stop guessing and start quoting.
Content the models can actually quote
Models prefer to lift a clean sentence. Long, narrative intros full of adjectives get paraphrased, and paraphrasing is exactly where your brand leaks out of the answer. We rebuild priority pages around 40 to 60 word, self-contained answer blocks: a concise factual statement, often preceded by a question-led heading, that a model can reproduce almost verbatim without needing to reword you away.
We also pin citation-worthy assets to key pages, original data, benchmarks, definitions, comparison tables, because retrieval crawlers favour sources that hand them something concrete to repeat. A page that states a verifiable number gets re-cited. A page that only asserts industry-leading quality does not.
Alongside the content we handle technical access. Opening the AI-crawler user-agents in robots.txt, GPTBot, ClaudeBot, PerplexityBot, Google-Extended and CCBot, and publishing a maintained llms.txt index of the pages you want models to read. If the crawl layer is closed, none of the content work ever reaches the model.
Measuring what the models say
How we helped a Pakistani business achieve measurable results.
Rankings do not apply here, so we measure model behaviour directly. We build a fixed prompt set from your real buyer questions and competitor names, run it across ChatGPT, Claude, Gemini and Perplexity on a schedule, and score every response: does the model name you, cite a source, describe you accurately, and where do competitors sit inside the same answer.
That produces a monthly AI share-of-voice read in a Looker Studio dashboard, alongside GA4 referral traffic from answer engines and any CRM feedback on AI-sourced leads. It is the only honest way to know whether the work is moving the needle, because in LLM SEO, if you are not measuring the model’s output, you are guessing at it.
If your category is shifting into AI answers faster than your current SEO reflects, start with a strategy call and a model-behaviour baseline, or compare the related disciplines in our services library.
