LLM SEO aligns your entity signals, content architecture, and source footprint with how language models actually find and attribute information — both through live retrieval and through what the model already carries.
There are two routes into a language model's answer. The first is live retrieval — the model searches, fetches pages, and cites what it found. The second is what the model already carries from training: a compressed, imperfect impression of who you are, formed from everything written about you before the cutoff.
These call for different work. Retrieval rewards crawlability, structure, and freshness. Model memory rewards consistency and volume of corroborating references over time — you can't edit it directly, only influence what gets written about you going forward.
Most brands optimize for neither, because most SEO work targets a ranking algorithm that behaves like neither. LLM SEO is the deliberate practice of building both: a site that's cleanly retrievable right now, and an entity footprint consistent enough that future models form an accurate impression of you.
Which pathway matters more depends on your category and how established your brand already is. This is one of the first things we determine in an engagement.
Best for: Newer brands, fast-changing categories, and anywhere the assistant reliably searches before answering.
Best for: Established brands defending category position, and anyone playing a multi-year positioning game.
Four workstreams, run in parallel. The first two are technical and fast; the second two are slower and compound.
Crawler access, rendering, response handling, and freshness signals — the mechanics that determine whether a model fetching your page gets usable content or an empty shell.
Schema, consistent naming, defined attributes, and sameAs corroboration so a model can resolve your brand to a specific entity rather than a fuzzy string.
The independent sources describing you consistently. Volume matters less than consistency — three sources agreeing beats thirty contradicting each other.
Auditing what models currently say about you, finding where they're wrong, and tracing the sources causing it so the underlying error can be addressed.
LLM SEO spans technical retrieval work, entity engineering, external corroboration, and the ongoing testing that catches when a model starts describing you incorrectly.
Ensuring every model-feeding crawler can reach, render, and parse your priority pages, verified through actual user-agent testing rather than assumption.
Building the schema, naming consistency, and attribute definitions that let models resolve your brand precisely rather than approximately.
Systematically testing how each model describes your brand, catalogues your products, and positions you against competitors — then tracing errors to their source.
Earning consistent third-party references that reinforce the same facts, because agreement across independent sources is what models weight most heavily.
Keeping high-value pages current and clearly dated, since staleness signals push retrieval toward more recently updated competitors.
Monthly tracking across model families, because behaviour differs between them and updates can change source selection without warning.
How much weight goes to retrieval versus memory depends on how established you are and how fast your category moves.
Models carry little or no impression of you, so live retrieval is essentially your only route in. The upside is that retrieval is the pathway you control most directly.
Models already have an impression — the question is whether it's accurate and current. Often the highest-value work is correcting what they get wrong.
Models routinely garble technical specifications, pricing tiers, and capability boundaries. Errors here cost deals directly when a buyer is told you don't do something you do.
The classic enterprise failure is internal inconsistency — different units describing the same entity differently, which teaches models to be uncertain about all of it.
The RON Loop applies here with heavy emphasis on the Orient phase — understanding what models currently believe about you is the work that shapes everything else.
We verify what each model-feeding crawler actually receives, and establish whether live retrieval is even available to you today.
We test systematically how each model describes your brand, products, and competitive position, then trace every error back to its likely source.
Schema and naming consistency go in on-site, while external work targets the sources that will reinforce the correct description over time.
Monthly re-testing across model families, tracking description accuracy and citation share, with priorities reset by what actually shifted.
Everything that makes a site retrievable by a language model — clean rendering, accurate schema, consistent entity signals, credible external references — is something classic search has rewarded for years. The difference is what happens after retrieval.
The questions clients ask us most before starting. If yours isn't here, ask us directly on a consultation call.
LLM SEO is the practice of optimizing a brand for how large language models find, understand, and attribute information. It covers two pathways: live retrieval, where the model searches and fetches your pages, and model memory, the impression formed during training.
It's sometimes called LLMO. The practical work spans technical retrieval readiness, entity definition, external corroboration, and ongoing testing of how models actually describe you.
Not directly, and nobody can. Training datasets aren't something an agency submits to. What you can influence is what exists to be trained on — the volume and consistency of accurate, credible information about your brand across the open web.
That's a slow, compounding game measured in quarters. Anyone claiming they can insert your brand into training data is selling something that doesn't exist.
Usually one of three reasons: outdated information that was accurate when written, inconsistent descriptions across your own properties that taught the model to be uncertain, or confusion with a similarly-named entity.
All three are addressable, but they need diagnosing first. Our description audit traces specific errors back to their probable sources so the underlying cause gets fixed rather than just noted.
The terms overlap heavily. LLM SEO is generally used as the umbrella covering optimization toward language models overall. GEO emphasizes earning citations in generated answers. AEO emphasizes being the extracted direct answer.
We don't treat them as three separate products, because roughly 80% of the underlying work is shared. What differs is where the emphasis goes, which we determine from your baseline rather than deciding in advance.
Retrieval work can move in weeks. Entity and schema work typically shows in four to eight weeks. Description correction and corroboration building are the slow parts — realistically one to two quarters before models consistently describe you differently.
We report monthly throughout so you can see which pathway is moving rather than waiting on a single distant outcome.
The major families behind ChatGPT, Perplexity, Gemini, Claude, and Copilot, plus Google's AI Overviews. Behaviour differs between them, particularly in how readily each one searches versus answering from memory.
The core work — retrievability, entity clarity, corroboration — helps across all of them. Platform-specific tactics are a smaller share of the effort than most agencies imply.
It's an emerging convention with genuinely uncertain adoption, and we'll be straight with you about that rather than overselling it. We implement it because it's cheap, harmless, and may become meaningful.
We don't treat it as a strategy. Anyone positioning llms.txt as the core of an AI SEO offering is dressing up a fifteen-minute task as a service line.
Through repeated structured testing. We run a consistent set of prompts across model families on a fixed schedule and record how each one describes your brand, category, products, and competitors.
That produces a trackable accuracy score over time. It's genuinely noisier than rank tracking — outputs vary by phrasing and change with updates — so we report trends and ranges rather than pretending to precision we don't have.
Often yes, and sometimes more so. Smaller brands can't outspend incumbents on links, but entity clarity and retrieval readiness are cheap to fix and most competitors haven't bothered. It's one of the few places where structure beats budget.
Where it isn't worth it: if your category simply isn't being asked about conversationally yet. We'll tell you if benchmarking shows that, rather than selling you a retainer for a surface with no demand.
Search Console and analytics access, developer or CMS access for technical implementation, and someone internally who can approve and ship changes. For technical products, accurate current specification documentation matters a lot.
The most common blocker isn't strategy — it's implementation throughput. If shipping changes is slow internally, we'll scope implementation support rather than hand over recommendations you can't action.
We'll run your brand through every major model family and show you exactly what they say about your company, your products, and your competitors — including what they get wrong.
No commitment · 45 minutes · Immediate value
Two fields. We’ll crawl your site as GPTBot, ClaudeBot and PerplexityBot, benchmark a sample of your category’s prompts, and send you what we find.
No commitment, no sales sequence. If we’re not a fit, we’ll say so.