LLM SEO consultancy: what it is and how to hire one

TL;DR: An LLM SEO consultancy helps your brand show up inside AI answers from ChatGPT, Gemini, Claude, and Perplexity, more than in Google's blue links. The work covers content structure, schema, citation-worthy sourcing, and entity authority. It's a separate discipline from traditional SEO, and demand for it has outrun the supply of people who actually know how it works.

What does an LLM SEO consultancy actually do?

Traditional SEO gets your page ranked. LLM SEO gets your brand named when an AI assistant answers the question your customer just typed. Two different problems. Two different fixes.

A competent LLM SEO consultancy (sometimes called a GEO or AEO consultancy, short for generative engine optimization and answer engine optimization) does a handful of specific things. It audits how often and how accurately AI systems mention your brand. It finds the gaps: the categories where competitors get named and you don't. It restructures content so language models can pull clean, quotable facts from your pages. And it builds the third-party citation footprint that makes a model treat your brand as a known quantity instead of a guess.

The work is messier than it sounds. Google's PageRank is at least conceptually documented. The retrieval logic inside ChatGPT's browsing layer, Perplexity's index, or Google's AI Overviews is not. Consultants work from published research on retrieval-augmented generation (RAG), from observation, and from the small stack of academic studies that have tested what makes a page more likely to be cited.

One study from researchers at Princeton, Georgia Tech, IIT Delhi, and Allen AI found that AI-generated answers contain verifiable factual errors at rates that vary by domain and query type, which tells you something about how models weight sources [1]. A brand that gets cited often and correctly has almost always done the structural work that makes a precise fact easy to extract.

See generative engine optimization for the full breakdown of the underlying discipline.

How is LLM SEO different from traditional SEO?

The differences are real enough that most traditional SEO agencies can't do this work yet.

Traditional SEO optimizes for a ranked list. The user sees ten blue links and clicks one. You measure rankings and organic traffic. LLM SEO optimizes for a synthesized answer. The user asks a question and gets prose back. Your brand is in that prose or it isn't. There is no page two.

The signals differ too. Traditional SEO leans on backlink authority, keyword coverage, and technical crawlability. Those still matter to AI search indirectly, since models train on and retrieve from the wider web, but they aren't the direct cause of a citation. Research published by Ahrefs in 2024 found that pages cited in AI Overviews had a median Domain Rating of 76, versus 66 for pages ranking in the top 10 but not cited [2]. Strong domain authority helps. It isn't enough on its own.

What drives a citation is closer to this: does the page hold a clean, self-contained answer to a specific question? Is the entity (your brand, product, or claim) named in enough independent sources that a model trusts it's real? Is the content chunked in a way a RAG system can actually use?

Keyword targeting still exists here, but the unit of optimization moves from a keyword phrase to a question plus its likely follow-ups. AI engines retrieve by semantic match to the user's full intent, not by exact-match keywords. Cited pages average 0.60 title-question semantic similarity versus 0.48 for pages that get passed over, according to research on answer engine citation patterns [3].

See ai seo for a fuller look at how the discipline has changed.

What signals do AI engines actually use to decide what to cite?

Nobody outside the model providers knows for certain. What we have is observational research, published RAG papers, and inference from controlled tests. Here's the shortlist that keeps showing up.

Entity authority comes first: how consistently and accurately your brand is described across independent sources. Models are built to reduce uncertainty. If your brand appears in 40 independent sources that all describe you the same way, a model can quote you with confidence. Appear in 3 sources that disagree, and the model skips you or hedges.

Second is answer completeness. Pages that answer the question in their first 100 to 150 words get cited more than pages that bury the answer. It's partly a RAG artifact: when a system chunks a page, the opening chunk carries the most weight. A vague or promotional lede is a low-value chunk.

Third is structured data. Schema markup (FAQ, HowTo, Article, Organization) gives models a clean extraction path. Google's structured data documentation states that schema helps their systems understand page content [4]. That understanding feeds AI Overviews and other generative features.

Fourth is freshness. Perplexity weights recency hard. A page updated last month beats one last touched in 2021 for most informational queries, all else equal.

Fifth is citation density inside the content. Pages that cite primary sources (studies, government data, official docs) read as more trustworthy to retrieval systems. This mirrors how E-E-A-T works for Google, applied to generative retrieval.

The table below compares which signals matter most across the major AI answer surfaces, based on published research and disclosed system behavior.

| Signal | Google AI Overviews | Perplexity | ChatGPT (browsing) | Claude | |---|---|---|---|---| | Domain authority | High | Medium | Medium | Low (training-weighted) | | Structured data / schema | High | Low | Low | Low | | Answer-in-lede | High | High | High | High | | Freshness | Medium | High | Medium | Low | | Entity co-citation count | Medium | High | High | High | | Internal link structure | Medium | Low | Low | Low |

Median domain authority: AI Overview-cited pages vs. top-10 ranked pages

| | | |---|---| | Cited in AI Overviews (median DR) | 76 | | Ranked top 10, not cited (median DR) | 66 |

Source: Ahrefs, Google AI Overviews Study, 2024

What does LLM SEO consultancy cost?

Pricing is all over the map because the field is young and the quality range is enormous. Here's what the market looks like as of mid-2025.

Freelance LLM SEO specialists charge anywhere from $100 to $400 per hour. The spread reflects real skill differences. Someone who can show you before-and-after citation tracking, explain RAG chunking, and write content that actually gets cited is worth $300 an hour. Someone who learned the term last month and renamed their old SEO deck is not worth $100.

Boutique consultancies that specialize in AI visibility (usually 3 to 15 people) price in monthly retainers. Expect $3,000 to $12,000 a month for a focused engagement on one brand in one or two product categories. Larger brands with multiple product lines or international markets will see proposals north of $15,000 a month.

Full-service agencies that bolted on an LLM SEO practice charge more and often deliver less depth, because the AI work is a small team inside a big generalist shop. Not a universal rule. Common enough to watch for.

Project work (a one-time AI visibility audit plus a content restructuring plan) typically runs $8,000 to $25,000 depending on scope. That's often the right starting point for brands that don't yet know how big their problem is.

No one has published a rigorous salary or pricing survey specific to LLM SEO consultancy as of this writing. These ranges come from job postings, public agency pricing pages, and practitioner conversations, not a formal study. Treat them as directional.

For the tooling costs that layer on top of consultancy fees, see ai seo tools.

How do you evaluate whether an LLM SEO consultant knows what they're doing?

This is the most useful question in the article, and the answer is blunter than most agencies would like.

Ask for a live citation-tracking demo. A serious consultant can show you, right now, how often a brand (a current or past client, anonymized is fine) appears in AI answers for a defined query set. If they can't, they're running on theory.

Ask them to explain why a specific page gets cited and a competitor's page doesn't. This tests whether they understand the real mechanisms (content structure, entity signals, schema, RAG chunking) or whether they're guessing.

Ask what they measure. Organic traffic from Google is not an LLM SEO metric. Citation rate across a tracked query set, share of AI answer mentions in a category, and accuracy of brand representation are LLM SEO metrics. If their whole framework is traffic-based, they haven't made the conceptual shift.

Ask for a writing sample that later got cited. Not every consultant produces content, but the ones who do should have examples. Read it. Does it answer a specific question in the first paragraph? Does it carry a concrete fact with a named source? Those are the markers of citation-worthy content.

Ask their position on schema. A good consultant gives a nuanced answer: schema helps Google's generative features, it matters less for Perplexity and ChatGPT browsing, and the priority depends on your platform mix. Anyone who says schema is everything, or schema is worthless, is oversimplifying.

Last, ask how they handle the fact that AI citation is partly random. Honest consultants will tell you citation rates improve with structural work but no one can guarantee a specific citation in a specific answer. If someone guarantees it, walk.

What deliverables should you expect from a consultancy engagement?

A serious LLM SEO engagement follows a clear sequence.

Phase one is an AI visibility audit. It documents your current citation rate across a defined set of queries and platforms, shows where competitors get cited instead of you, and diagnoses why. A good audit produces a prioritized list of content gaps and structural problems, more than a score.

Phase two is a content and entity strategy. This covers which questions your brand should own, how to structure new content to answer them directly, what third-party coverage you need to establish entity authority, and how to brief your writers or agency partners.

Phase three is implementation: rewriting existing pages, creating new ones, building schema markup, and chasing the external citation footprint (earned media, directory listings, industry and academic citations).

Phase four is ongoing measurement. Citation rates move as AI systems retrain, as competitors optimize, and as query patterns shift. A monthly check on citation rate across your tracked query set is the floor.

Tools like Spawned handle the measurement layer: tracking how often and how accurately your brand appears across ChatGPT, Gemini, Perplexity, and other platforms. The audit itself can seed phase one without a manual research sprint.

See ai search visibility metrics kpis for the full list of metrics worth tracking.

What does good content for LLM citation look like?

A few structural properties show up again and again in content that gets cited.

The answer comes first. Not after a paragraph of throat-clearing, not after a definition, not after a disclaimer. First. This holds for Google's featured snippets and it holds harder for generative retrieval, because the opening chunk of a page is the one a RAG system is most likely to grab.

Each section answers exactly one question. Not two. Not a question and its cousin. One question, answered directly, with detail after. This maps to how AI systems chunk pages and how they build answers: they pull one chunk per source, not a blend of three chunks from the same page.

The content carries concrete, extractable facts. Not "our product is highly effective" but "in a 2024 randomized trial of 400 users, 67% reported X within 30 days [source]." A model can quote a specific number from a named study. It can't quote a marketing claim.

The writing names entities plainly and consistently. If your brand is "Acme Corp," don't switch to "we" in the body, "Acme" in one paragraph, and "the company" in the next. Consistent naming helps a model tie your entity to the claims you're making.

The page links to primary sources. A page that cites a government database, a peer-reviewed study, or an official industry body signals that its claims are checkable. That signal counts.

Headings are full questions, not clever labels. "How much does X cost?" beats "Pricing" as a heading for AI retrieval, because the semantic match to the user's query is direct.

How do AI Overviews, Perplexity, and ChatGPT differ in what they cite?

Treating every AI answer surface as the same is a common and expensive mistake. They pull from different places.

Google AI Overviews leans on pages already in Google's index with strong traditional signals: domain authority, structured data, E-E-A-T, and freshness. Google's own documentation states that AI Overviews use the same index and quality signals as Search [5]. Your existing SEO foundation matters more here than anywhere else. A brand with weak authority and thin content won't appear in AI Overviews no matter how cleverly the content is structured.

Perplexity is a real-time retrieval engine. It crawls the web, pulls current pages, and synthesizes. Freshness matters more here than anywhere. Perplexity also shows its sources to the user, so citation is transparent and easy to track by hand. It favors pages with clear attributable facts and named sources, partly because its users expect cited answers.

ChatGPT with browsing (in GPT-4o and similar models) works much like Perplexity: it fetches live pages and synthesizes. Without browsing on, ChatGPT draws from training data, which has a knowledge cutoff and skews toward content that was widely reproduced and linked before that cutoff. For most commercial brands, the browsing version is the one that matters for ongoing visibility.

Claude runs mostly on trained knowledge in its default mode, with optional retrieval. It has the least transparent citation behavior of the major models. Entity authority matters most here: if your brand is widely and consistently described in the training corpus, you'll appear. If you're newer or niche, you may not, regardless of content quality.

See ai search and google ai search for deeper coverage of each platform.

What's the role of third-party citations and earned media in LLM SEO?

Entity authority is probably the most underrated lever in LLM SEO.

A model's confidence that your brand is real and trustworthy in a category comes mostly from how many independent sources describe you consistently. It's conceptually similar to how PageRank worked, but the mechanism differs. For AI citation, what counts is co-occurrence: your brand name showing up alongside category-relevant terms in sources the model trusts.

So earned media in industry publications, academic citations where relevant, inclusion in authoritative directories and databases, and Wikipedia presence (when notability warrants it) all lift your citation rate. A study published in PLOS ONE found that Wikipedia is disproportionately represented in large language model training data, accounting for a large share of tokens in common training corpora [6]. Being accurately described on Wikipedia is a real signal.

Press coverage in outlets that are well-represented in training data (major newspapers, trade publications, established high-authority blogs) has outsized value. Not because those outlets send traffic. Because the language they use to describe you becomes part of the model's picture of your entity.

This is why some of the most effective LLM SEO isn't website work at all. It's PR, thought leadership, and data studies built to be cited. A brand that publishes original research picked up by three industry publications has done more for its citation rate than a brand that rewrote its homepage with question headings but has no external footprint.

How do you measure whether LLM SEO is working?

Measurement trips up most early practitioners. The temptation is to use traffic as a proxy, but traffic from AI search is often not attributable, and plenty of AI-influenced purchases never produce a tracked click at all.

The core metric is citation rate: the share of times your brand appears in AI answers for a defined query set. You track it by defining a query set (usually 50 to 200 queries that represent your category), running those queries on your target platforms, and recording whether and how your brand shows up.

Secondary metrics matter too. Accuracy of representation: is what the AI says about you correct? Share of answer: when you're cited, are you the top recommendation or a passing mention? Sentiment: is the surrounding context positive, neutral, or negative? Competitive share: what percentage of citation slots in your category does your brand hold versus rivals?

None of these is easy to measure by hand at scale. Query sets need to run regularly (weekly at minimum, more often ideally) across multiple platforms. That's why dedicated AI visibility tools exist. The manual version is a full-time job.

A reasonable benchmark: brands making significant content and entity changes typically see measurable citation rate improvement in 60 to 120 days for Perplexity and ChatGPT browsing, and 90 to 180 days for Google AI Overviews and training-data-weighted models. No one has published a rigorous study on this timeline. These come from practitioner observation and are rough guides.

See ai search visibility metrics kpis for the full framework.

Should you hire a consultancy, build in-house, or use a tool?

The honest answer depends on where you're starting.

If you have a content team and an SEO manager, the smart move is usually to upskill that team with a short consultancy engagement, buy a measurement tool, and build the competency in-house. The content work (restructuring pages, writing articles, earning citations) is ongoing and is better owned inside your org than outsourced forever.

If you have no content team, or a small marketing operation, a retained consultancy makes more sense in the near term. You need someone to do the work, more than advise on it.

If you're in a category with heavy AI search competition (financial services, health, software, travel, consumer electronics), you probably need both: a specialist consultancy for the strategy and structure, and a measurement tool to track what's happening week to week.

The worst outcome is hiring a generalist SEO agency that added "LLM SEO" to its service page without doing the conceptual work to understand why it's different. You'll pay for traditional SEO wearing an AI badge. The questions in the evaluation section above are built to filter those out.

Spawned is one option for the measurement layer: it tracks brand citation rates across the major AI platforms and surfaces the content gaps a consultancy would need to fix [learn more at spawned.com]. The tool doesn't replace the strategy and content work, though. No tool does.

For a view of the current tool landscape, see ai seo tools.

What should you watch out for when hiring an LLM SEO consultancy?

A few patterns are reliable red flags. Learn them before you sign anything.

Guaranteed citation placement. No one can promise this. AI systems are probabilistic and updated constantly. Any consultancy that guarantees a specific citation in a specific AI answer is misrepresenting how these systems work.

No real measurement method. Good consultancies have a systematic way to track citation rates. If the method is "I searched ChatGPT and you came up," that's not a method.

Over-reliance on schema. Schema is useful, especially for Google AI Overviews, but it's not the whole job. A consultancy that leads with schema and never mentions entity authority, content structure, or external citations is working from an incomplete model.

Confusing AI Overviews with LLM SEO. AI Overviews are one piece. A consultancy that only optimizes for them and ignores Perplexity, ChatGPT, and Claude leaves most of the opportunity on the table. By some estimates, ChatGPT and Perplexity together handle well over a billion queries a month [7], and that number is climbing faster than AI Overviews adoption.

No content production. The structural work here means rewriting and creating content. A consultancy that only advises and never produces or reviews actual content is selling you a strategy deck, not a visibility gain.

Stale methodology. Watch for anyone who hasn't updated their approach in the last six months. The field moves fast. A good practitioner runs experiments, reads new research, and adjusts. Stale methodology is a genuine risk in a space still being defined.

Sources

1. arXiv: 'FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation' (Min et al., Princeton, Georgia Tech, IIT Delhi, Allen AI)
2. Ahrefs: Google AI Overviews study, 2024
3. arXiv: 'GEO: Generative Engine Optimization' (Aggarwal et al., 2023)
4. Google Search Central: Structured Data documentation
5. Google Search Central Blog: update on AI Overviews
6. PLOS ONE (journals.plos.org): studies on LLM training data composition and Wikipedia representation
7. Similarweb: AI search query volume estimates
8. Google Search Central: creating helpful, reliable, people-first content (E-E-A-T)
9. arXiv: 'RARR: Researching and Revising What Language Models Say, Using Language Models' (Gao et al., 2022)
10. Perplexity AI: how Perplexity works