LLM SEO Services for Brands
Models Should Already Know

LLM SEO is the practice of optimising a brand so large language models find, understand and cite it accurately. It covers two separate pathways: live retrieval, where the model searches the web and fetches your pages, and model memory, the impression formed from everything written about you before the training cutoff.

It is sometimes written LLMO. The practical work spans crawler access, rendering, entity resolution, structured data, answer-shaped content and third-party corroboration.

They overlap heavily and the industry uses all three loosely. LLM SEO is generally the umbrella term. GEO (Generative Engine Optimization) emphasises being cited as a source inside a generated answer. AEO (Answer Engine Optimization) emphasises being the extracted direct answer to a specific question.

Roughly 80% of the underlying work is shared across all three. Be sceptical of any agency selling them as three separate retainers — the difference is emphasis, not toolkit.

No, and be wary of anyone claiming it does. Traditional SEO is the foundation: if your site is not crawlable, fast and credible, nothing retrieves it — ranking algorithm or language model.

What LLM SEO adds on top is entity resolution, schema depth, answer-shaped structure and third-party corroboration. Most of that improves classic organic performance at the same time.

Retrieval fixes move fastest. If a crawler is blocked or your pages render empty, unblocking that can change visibility within two to six weeks on the next crawl cycle.

Entity and schema work typically registers in four to eight weeks. Corroboration and citation building is the slow part — two to four months — because it depends on third parties publishing and then being re-crawled. Model memory shifts over quarters, not weeks.

Through a fixed, benchmarked prompt set: typically 40 to 60 questions your category genuinely gets asked, run across every major platform on a schedule, recording whether your brand is named, how prominently, and against which competitors.

That produces a citation share figure tracked month over month. It is noisier than rank tracking, so each prompt is run multiple times per cycle and variance is reported rather than hidden. A single favourable screenshot is not evidence.

No. Model outputs are probabilistic, vary by phrasing, and change with every model update. Any agency guaranteeing specific AI citations is either misunderstanding how these systems work or misrepresenting it.

What is achievable is measurable progress: a documented baseline, a defined prompt set, monthly reporting, and an honest account of what moved and what did not.

Most often it is technical rather than editorial. The two most common causes we find are a blocked AI crawler in robots.txt and a client-side rendered site that serves an empty HTML shell to crawlers that do not execute JavaScript.

Googlebot renders JavaScript reasonably well, so teams check Google, see everything indexed, and assume all crawlers behave the same way. Several major AI crawlers do not.

Not directly, and nobody can. Training datasets are not something an agency submits to. What you can influence is what exists to be trained on — the volume and consistency of accurate information about your brand across the open web.

That is a slow compounding game measured in quarters. Anyone selling training-data placement is selling something that does not exist.

ChatGPT, Perplexity, Google Gemini, Claude, Microsoft Copilot, Google AI Overviews and DeepSeek as standard, with classic Google and Bing organic underneath as the retrieval foundation.

The core work — crawler access, rendering, entity clarity, corroboration — helps across all of them simultaneously. Platform-specific tactics matter at the margin and are covered on our individual platform pages.

Usually one of three causes: outdated information that was accurate when it was written, inconsistent descriptions across your own properties that taught the model to be uncertain, or confusion with a similarly-named entity.

All three are fixable, but they need diagnosing first — the remedy differs completely depending on which one it is. Our description audit traces specific errors back to their probable sources.

It is an emerging convention with genuinely uncertain adoption. We implement it because it takes fifteen minutes and is harmless, but it currently does far less than robots.txt directives and rendering strategy.

Treat it as a cheap hedge, not a strategy. An agency positioning llms.txt as a core service line is dressing up a trivial task.

Standalone audits typically run in the low five figures as a fixed fee. Ongoing retainers generally start around $4,000–$10,000 per month for focused engagements, rising to $10,000–$25,000+ for competitive categories or enterprise scope.

Cost is driven by technical debt, site and catalogue scale, competitive intensity, number of properties and markets, and whether we implement or specify. Full detail is on our pricing page.

Often yes, and sometimes more so. Smaller brands cannot outspend incumbents on links, but entity clarity and retrieval readiness are cheap to fix and most competitors have not bothered. It is one of the few places where structure beats budget.

Where it is not worth it: if your category simply is not being asked about conversationally yet. We will tell you if benchmarking shows that rather than selling you a retainer for a surface with no demand.

Yes, and it is a common arrangement. We frequently handle the AI visibility layer while an internal team or incumbent agency continues content production and classic SEO.

It works best when responsibilities are explicit. We define who owns technical implementation, who owns publishing, and where handoffs happen, in writing, before work starts.