How Agencies Can Implement GEO to Increase Citations Across Leading AI Assistants

Your brand ranks on Google. Your SEO is solid. And yet, when a buyer types your category into ChatGPT, Claude or Perplexity, your competitor's name comes up and yours does not.

That is the new visibility problem, and it is not a content problem. It is an execution problem.

Generative Engine Optimization is the discipline of making your brand the source AI assistants cite when they answer the questions your buyers are already asking. The concept is straightforward. The implementation, across five platforms at once, is not. Search Engine Land's coverage of GEO puts it plainly: it requires a different approach from traditional SEO, built around how language models retrieve and surface information rather than how crawlers rank pages.

The gap most brands fall into: they read about GEO, understand the theory, and either attempt it with one-off content tweaks or hand it to an SEO agency that treats it as a metadata exercise. Neither moves the citation count.

This article sets out how implementation actually works across ChatGPT, Claude, Perplexity, Google AI Overviews and Gemini, and why the brands gaining ground treat it as a full-execution discipline rather than a quarterly content sprint.

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Knowing GEO and Implementing It Are Two Different Problems

There is no shortage of articles explaining what GEO is. What is missing from almost all of that coverage is the operational reality of doing it.

Implementation requires coordinating five distinct workstreams at the same time:

• Prompt research: identifying the exact queries your buyers put to models, not keyword variants but conversational prompts with commercial intent
• Technical optimisation: making sure your pages are structured so AI crawlers can reach, extract and interpret them
• Content architecture: building or rewriting content so it answers the prompts assistants receive, in the format those assistants quote from
• Third-party authority signals: establishing credibility in the sources models weight most, including Reddit, LinkedIn and editorial coverage
• Visibility tracking: measuring citation frequency against a real baseline, not impressions or rankings

Each has its own technical requirements, timelines and feedback loops. An SEO agency can handle one or two. A content team can handle one. A measurement tool reports on outcomes after the fact.

The problem is not awareness. It is that no single internal function owns all five, and without all five running in parallel, citation volume does not move.

That is the execution gap, and it is why brands that understand GEO perfectly well still do not appear in AI answers six months after deciding to work on it. The same dependency is the argument behind the Citation Stack.

Each Assistant Has Different Citation Logic

One of the most common mistakes is treating all assistants as the same. They are not. Each pulls from different source pools, weights authority differently, and has its own preferences for content structure.

ChatGPT

ChatGPT's browsing prioritises pages that are indexed, crawlable and structured to answer specific questions directly. It favours clear headings, concise answer paragraphs and supporting evidence. Pages behind login walls or rendered entirely in JavaScript are effectively invisible to it. Google's own Search Central documentation on AI Overviews makes the same point about crawlability and structure being foundational to AI feature eligibility, and the logic carries across assistants.

Perplexity

Perplexity is the most citation-transparent of the major assistants. It surfaces sources inline and ranks them by relevance to the specific query, which makes every citation a visible editorial decision. That makes it the fastest platform to measure progress on, and also the most demanding: it rewards content that is genuinely the best answer to a specific prompt rather than a well-optimised page. Thin content that ranks in traditional search does not perform here.

Google AI Overviews

AI Overviews pulls from the indexed web but applies a separate layer on top of organic search. A page can rank first for a keyword and still not appear in the AI Overview for the same query. That gap between ranking and being used as a source is the single most important thing to understand about this surface, and we cover the mechanics in how AI engines decide what to cite.

Claude

Claude draws on its training data and, for Claude.ai users, on real-time web retrieval, which makes current authority signals relevant alongside training-data signals. Building credibility in the sources that inform it, including editorial coverage and well-structured documentation, is the primary lever. Third-party mentions in credible publications carry disproportionate weight.

Gemini

Gemini draws from Google's knowledge graph and the indexed web. It shares some signals with AI Overviews but applies them differently in conversational contexts. Its integration with Google's knowledge infrastructure means schema markup, entity clarity and brand consistency across the web all influence how it represents a brand.

AI Assistant	Primary Source Pool	Key Optimisation Lever
ChatGPT	Indexed web	Crawlability, structured answers, direct question-response format
Perplexity	Real-time web search	Best-answer content, citation-worthy depth
Google AI Overviews	Google index, additional quality signals	Signals beyond standard SEO
Claude	Training data, web retrieval	Third-party authority, editorial coverage
Gemini	Google knowledge graph, indexed web	Schema, entity clarity, brand consistency

The practical implication: optimising for one assistant does not transfer to the others. A brand that appears in Perplexity but not in Google AI Overviews has a partial solution, not a strategy.

The Five-Step Implementation Framework

Closing the execution gap means running five workstreams in sequence and in parallel. Here is how each works, and why shortcutting any one stalls the whole effort.

Step 1: Prompt Research with Demonstrated Volume

GEO starts by identifying the exact prompts your buyers use, not keyword variants but natural-language queries with genuine volume inside AI platforms. This is a different dataset from traditional keyword research. A query driving heavy Google search volume may generate almost no assistant traffic, while a conversational prompt with no Google volume may be typed into ChatGPT constantly.

Effective prompt research surfaces the highest-volume prompts in your category where assistants are actively answering, the prompts where a competitor is cited and you are not, and the prompts where no strong citation exists yet, which are the fastest wins.

Those prompts become the agreed target set, and every subsequent workstream is built around them.

Step 2: Technical Optimisation for AI Crawlers

Most sites were built for human readers and Google's traditional crawler. Assistant crawlers have different requirements:

• Crawl accessibility: pages must be reachable without JavaScript rendering dependencies
• Structured data: helps models understand what a page is about and who stands behind it
• Extractability: models pull specific passages, so clean HTML, clear heading hierarchy and discrete answer blocks get cited more reliably than complex layouts
• Speed and stability: slow or unstable pages are deprioritised by real-time retrieval systems

This is not a one-time audit. As assistants change their crawling behaviour the requirements shift, which is why it runs continuously as technical AEO infrastructure.

Step 3: Content Built for Citation

This is where most attempts stall. Brands rewrite a few pages, add FAQ sections, and expect citations to climb. They rarely do, because the content still was not built around the prompts assistants actually receive.

Content built for citation has specific characteristics:

1. A direct answer in the first 40 to 60 words of each section, so a model can extract a clean quote without summarising
2. Supporting evidence immediately after the answer: statistics, named examples or cited sources that give the model confidence in the claim
3. Self-contained sections that make sense read in isolation, because assistants do not quote full articles
4. Prompt-matched framing that addresses the conversational query, not just the underlying keyword

Work structured this way tends to compound the existing SEO investment rather than replace it, because the same formatting that helps a model extract an answer also helps with featured snippets and People Also Ask.

Step 4: Third-Party Authority via Reddit

Models do not only read your website. They read what is written about your brand across the web, and Reddit carries disproportionate weight. Because Reddit content is human-generated, community-moderated and indexed at scale, models treat it as a high-trust signal, particularly for questions where buyers are comparing options or asking for recommendations.

For a brand absent from the relevant conversations, that absence is a citation gap. An assistant answering a category-level question surfaces the brands the communities have discussed, not the brands with the best websites.

LLMReach builds this as a core part of every engagement:

• Identifying the subreddits where your buyers already ask the prompts you are targeting
• Building a credible, consistent presence in those communities over time rather than one-off posts
• Contributing to threads that address the prompts assistants receive
• Keeping brand information consistent across sources so models encounter reinforcing signals rather than conflicting ones

No automation, no fake accounts, no vote manipulation. Removed threads cannot be cited, which makes authenticity an operational requirement rather than a brand value. The rules and what actually gets accounts removed are set out in getting mentioned on Reddit without getting banned, and the service itself in Reddit authority.

This is the workstream most agencies skip, because it is the hardest to attribute directly and the furthest from traditional SEO thinking. Once built, it is also the most durable advantage available, because a competitor cannot replicate it overnight with a content sprint.

Step 5: Visibility Tracking Against a Real Baseline

Tracking means systematically querying the target prompts across the assistants and recording whether your brand is cited, in what position, and in what language.

The baseline is what makes the number mean anything. Citation volume in week one is meaningless without knowing where you started. Effective tracking requires a baseline measured over 14 to 30 days before any optimisation begins, consistent re-querying of the full target prompt set, platform-level breakdowns so you know which assistants are responding, and a shared dashboard so the client sees the same data.

This is the accountability layer. Without it, GEO is a faith-based exercise.

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Your audit is reviewed live on the call. It is not emailed as a PDF.

Why Most Brands Cannot Execute This Alone

The framework is not complicated to understand. It is difficult to execute, for a structural reason: the skills required do not sit in the same team.

Prompt research needs AI-specific query data and the judgment to separate volume from noise. Technical optimisation needs someone who understands how assistant crawlers differ from Googlebot. Content architecture needs writers who understand citation mechanics rather than SEO copywriting. Third-party authority needs a distribution strategy across Reddit, LinkedIn and editorial channels. Tracking needs a systematic querying process and a reporting layer that makes the data actionable.

For most marketing teams, assembling all five internally would mean hiring. For most SEO agencies it would mean building a new service line.

This is also why measurement tools alone do not solve it. A tool that tells you your citation rate is low does not tell you which of the five workstreams is failing, and it does not fix it. Reporting is not execution. If you are weighing an agency against a platform or an in-house hire, we set out the trade-offs in how to choose.

The SEO Foundation Still Matters

Traditional SEO is not dead and GEO does not replace it. SEO makes a page eligible to be indexed and considered. GEO determines whether that indexed page gets used as a source in an AI answer.

The two stack. A brand with strong technical SEO and well-structured content is faster to optimise than one starting from scratch. The SEO investment is the foundation, not wasted work.

How LLMReach Runs the Full Execution Stack

LLMReach is a GEO agency. Not a software platform, not a consulting firm that delivers a strategy document, not an SEO agency that added AI optimisation to a service list. All five workstreams are executed for the client, end to end.

• Prompt research: 50 prompts identified and agreed jointly with the client, with demonstrated volume before work starts. Not assumptions, not keyword proxies.
• Technical optimisation: the client provides access, LLMReach implements the changes. The client does not execute the work.
• Content: written, structured and published by LLMReach, built around the agreed prompt set and the citation requirements of each target assistant.
• Third-party authority: the Reddit presence and editorial signals needed to establish credibility in the sources models weight most.
• Visibility tracking: a shared dashboard, weekly written updates and fortnightly review calls, measured against the baseline established in the first 14 to 30 days.

The engagement runs 90 days minimum, then month to month with no lock-in.

The guarantee: the Citation Stack is guaranteed as a whole: a 30% increase in total citations across your site, measured against your own baseline, within 90 days. If it is not reached, you choose between continued work at no charge and a full refund. No individual page, prompt, platform or response is guaranteed separately, because no agency controls what any single model returns for any single question. The guarantee is on the aggregate.

It carries one condition: the client provides site access and approves what gets published. The execution is LLMReach's responsibility. The access is the client's.

What Separates a GEO Agency from an SEO Agency Calling Itself GEO

The category is filling up with agencies that rebranded existing SEO or content services without changing the underlying execution model. The difference is easy to identify:

What a GEO agency does	What a rebranded SEO agency does
Researches AI-specific prompts with demonstrated volume	Repurposes existing keyword research
Builds content structured for citation mechanics	Adds FAQ sections to existing pages
Builds third-party authority signals	Builds traditional backlinks
Tracks citations across the assistants	Reports on organic rankings and impressions
Guarantees a citation outcome	Promises improved AI visibility

The question to ask any agency claiming to offer GEO: what is the metric, what is the baseline, and what happens if you do not hit it?

The First Step Is Knowing Where You Stand

Before implementation can begin you need a baseline. You need to know which prompts in your category are currently generating citations, which assistants are citing your competitors instead of you, and where the fastest wins are.

That is what the audit is for. It is run live on a call rather than emailed as a report: a real-time look at your current citation footprint across ChatGPT, Claude, Perplexity, Google AI Overviews and Gemini, against the prompts your buyers are actually using.

You will leave the call knowing where you stand, which assistants are citing your competitors, and what closing the gap would require.

Request your free AI visibility audit
Your audit is reviewed live on the call. It is not emailed as a PDF.

Frequently Asked Questions

What does a GEO agency do?

A GEO agency runs the execution work needed to increase citations in AI assistants. That includes prompt research, technical optimization, content built for citation, Reddit authority building, and visibility tracking against a real baseline.

Why is GEO different from SEO?

SEO helps a page rank and get indexed. GEO helps that page get used as a cited source inside AI answers. The two work together, but GEO requires a separate execution layer focused on how assistants retrieve and quote information.

Why does Reddit matter for GEO?

Reddit matters because AI models often treat it as a high-trust, human-validated source. When the right topics and community signals exist on Reddit, models are more likely to surface and trust the brand in commercial answers.

Can one content update improve AI citations across all assistants?

Usually not. Each assistant has different retrieval and citation behavior, so improving citations across ChatGPT, Claude, Perplexity, and Gemini requires a coordinated program rather than a single page edit.

Why choose LLMReach for GEO implementation?

LLMReach is built to execute the full GEO stack, not just report on it. That means prompt research, technical work, content, Reddit authority, and tracking are handled as one system designed to increase citations across leading AI assistants.