01 Two channels decide what a model says
A language model can talk about your brand through two channels. The first is live retrieval: the model searches, reads a handful of pages, and answers from them. The second is trained knowledge: the statistical residue of every mention of your brand in the data the model was trained on. Retrieval you can influence in weeks. Trained knowledge moves on the provider's schedule, one model release at a time, and no one outside the lab controls what gets sampled.
LLM SEO plans for both. Fast work targets what retrieval finds today. Patient work builds the mention footprint the next model generation will absorb. Ask an assistant about your category with browsing disabled and you are reading the trained channel raw: that answer is the baseline we test first. Confusing the two timelines is the most common failure in this field.
02 Your training-data footprint
Training corpora lean on the durable, public web: news archives, Wikipedia and its citation trail, books, forums, and high-authority sites that survive deduplication. Marketing pages are a weak signal there. Journalism is a strong one. A brand mentioned across years of press coverage enters the next training run described the way those journalists described it.
You cannot edit history, so the footprint gets built forward: steady earned coverage with backlinks, mentions in reference-grade sources, and public documentation of what your company actually does. This is the same lever described on backlinks for AI trust: press placements are the one part of the training data you get to influence. The practical test is simple: if a careful stranger researched you using only third-party sources, would they get the story right? Models are that stranger, at scale.
03 Consistency is how machines decide what is true
Models and the systems around them estimate truth partly through agreement. When your founding year, category label, and product claims match across dozens of independent sources, the machine treats those facts as settled. When sources disagree, everything about you becomes lower confidence, and the model hedges or omits you.
So we enforce one version of the brand record: the same facts on your site, your profiles, the databases that describe you, and the coverage we earn. The audit usually finds drift nobody noticed: an acquired product still listed, an old tagline, three different employee counts. Entity consistency sounds like housekeeping. In LLM SEO it is closer to load-bearing infrastructure.
04 Structured data and machine-readable pages
Schema.org markup does not rewrite a model's weights, but it sharpens the retrieval layer that feeds live answers: Organization, Product, and FAQPage markup help systems resolve who you are and lift accurate snippets. We pair markup with page structures machines parse cleanly: short claims, dated statistics, comparison tables. Machines reward the same structure skimming humans do, so none of this fights your conversion goals.
We also watch llms.txt, a proposed file that tells AI crawlers what matters on a site. As of mid-2026 it is an emerging idea rather than a standard, so we implement it cheaply and expect nothing from it yet. On-page work supports the program. The engine that drives it is still earned coverage.
05 What an LLM SEO retainer covers
Month to month the retainer combines earned press placements, entity corrections, structured data work, and page rebuilds, tracked with a fixed prompt panel run across ChatGPT, Claude, Perplexity, Gemini, AI Overviews, and Microsoft Copilot. Where the interface allows it we note whether an answer came from search or from model memory, because the distinction tells us which channel is moving. The wider measurement method is described in our guide to measuring AI visibility.
LLM SEO is one lens on the same underlying program described on our AI SEO services page. Retainers start at $3,500 per month with a minimum of five monthly placements, detailed on pricing, with no setup fees and no guaranteed answers, because no honest vendor can guarantee those.