GEO Agency: Generative Engine Optimization for B2B Tech

What is a GEO agency and why you need one

A GEO agency is a specialist marketing firm that helps brands get cited by AI search engines — ChatGPT, Gemini, Perplexity, Claude, Copilot, and Google AI Overviews. Where a traditional SEO agency focuses on ranking your pages in the 10 blue links of a Google results page, a GEO agency focuses on getting your brand mentioned by name inside an AI-generated answer. Those are very different goals, and they require very different work.

The rise of generative AI search has been the most disruptive shift in organic visibility since Google launched. In the space of two years, AI Overviews have appeared on over half of all Google searches, ChatGPT has become a primary research tool for millions of B2B buyers, and Perplexity has carved out a significant slice of high-intent commercial queries. These platforms do not show 10 blue links. They synthesise answers from a handful of cited sources. If you are not one of those sources, you are invisible — even if you rank #1 in traditional Google for the same query.

That is the gap a GEO agency exists to fill. We help you understand which AI engines are answering questions about your category, who they are citing, what content they prefer to pull from, and how to engineer your own content, schema, and entity signals so that you become one of the cited sources. It is not a tweak to traditional SEO — it is a separate discipline with its own playbooks, its own measurement, and its own success criteria. Most B2B tech companies do not yet have the in-house expertise to do it well, which is why specialist GEO agencies are rapidly becoming essential partners.

GEO vs SEO: the differences that matter

The fundamental difference between GEO and SEO comes down to how the underlying systems retrieve and present information. Traditional search engines build an index of the web, match user queries to keywords in that index, score the matched documents using ranking signals (links, content quality, technical health, user signals), and present a ranked list of 10 results. Your job as an SEO is to be one of those 10 results.

AI engines work differently. They take a user query, decompose it into 8-12 sub-queries (this is called query fan-out), retrieve candidate content for each sub-query, evaluate that content for authority and structural clarity, then synthesise a single answer that draws from the highest-scoring sources and cites them by name. Your job as a GEO practitioner is to be one of the cited sources across as many of those sub-queries as possible. Read our deep-dive on SEO vs GEO for the complete breakdown.

Several practical differences flow from that. SEO rewards keyword targeting; GEO rewards comprehensive topical coverage. SEO rewards backlinks; GEO rewards entity authority and structured data. SEO rewards page-level optimisation; GEO rewards sentence-level and section-level optimisation, because AI engines extract chunks of content rather than returning whole pages. SEO measures rankings and traffic; GEO measures citations, share of voice in AI responses, and AI-driven referral traffic. The two disciplines share a foundation — strong technical health and high-quality content help both — but the optimisation tactics diverge significantly.

How AI search engines decide who to cite

Understanding how AI engines select sources is the foundation of every GEO programme. The process has five stages: query decomposition, retrieval, evaluation, synthesis, and citation. At each stage, content is filtered out — and only a handful of sources survive to be cited in the final answer.

Query decomposition is where the model breaks the user's question into sub-queries. For a query like “best CRM for B2B SaaS,” the model might generate sub-queries about pricing, integrations, ease of use, scalability, customer support, deployment options, security, and use case fit. The exact sub-queries depend on the model and the context, but the pattern is consistent: one user query becomes many.

Retrieval happens for each sub-query independently. The model fetches candidate content from its training data, its index, or a live web search depending on the platform. Content that does not match any sub-query never enters the candidate pool — which is why content covering only narrow keyword targets often fails in AI search.

Evaluation scores each candidate for relevance, authority, structural clarity, recency, and trustworthiness. Models prefer content that delivers clear answers in the first few hundred words, contains specific data points and statistics, uses semantic HTML and schema markup, comes from authoritative sources, and has consistent entity associations. Content that buries the answer behind long intros, marketing fluff, or unstructured prose typically loses at this stage.

Synthesis combines information from the top-scoring sources into a coherent answer. The model selects the most useful passages from each source and weaves them into a response that answers the original query.

Citation is the final step. Sources that contributed materially to the answer get named — usually as inline citations or a sources panel. Our guide on how to get cited by ChatGPT walks through the specific tactics that move you up the citation rankings.

Query fan-out explained

Query fan-out is the most important concept in GEO and the one most marketers misunderstand. The traditional SEO mental model says “target a keyword, write a page that matches it, build links, rank for it.” That model does not work in AI search because AI engines do not match keywords. They decompose, retrieve, and synthesise.

Consider a real example. A buyer asks Perplexity “what is the best ABM platform for mid-market B2B SaaS.” The model generates sub-queries that might include: what is account-based marketing, what features does an ABM platform need, what are the leading ABM platforms, how do ABM platforms price their products, what integrations matter for mid-market SaaS, how does ABM differ from inbound marketing, what does a typical implementation look like, and case studies from similar companies. For each sub-query, the model retrieves candidate sources independently. The final answer pulls from whichever sources scored best across those sub-queries.

The implication for GEO is huge. To win the citation, your content has to cover multiple sub-queries — not just the original query. A page that says “we are the best ABM platform for mid-market SaaS” will lose to a page that explains what ABM is, what features matter, how to evaluate vendors, how pricing works, and which integrations are essential. The latter has surface area against the sub-queries; the former does not.

Mapping query fan-out for your priority topics is the single most valuable exercise a GEO agency can run. We do this systematically — testing target queries across multiple AI engines, capturing the sub-queries each one generates, and using that map to architect content that covers the full surface area.

How to get cited by ChatGPT, Gemini, and Perplexity

Each AI engine has its own quirks, but the underlying principles are consistent. To improve your citation rates across all the major platforms, your content needs to satisfy six conditions.

1. Answer-first formatting. The key insight should appear in the first 100 words of every page. Models often only retrieve the opening chunk of a document, so burying the answer behind a long intro means it never gets surfaced. Lead with the answer; explain the context after.

2. Stat density. AI engines disproportionately cite content with specific numbers and data points. Statistics signal authority and provide quotable material the model can weave into its answer. Wherever you make a claim, back it with a number — and cite the source so the model can verify it.

3. Semantic chunking. Models retrieve content in chunks, not in whole documents. Use clear headings, short paragraphs, bullet lists, and explicit section labels so each chunk is self-contained and retrievable. Avoid long unbroken passages — they make it harder for the model to extract a usable snippet.

4. Schema and structured data. JSON-LD schema is the language AI engines use to understand your content. FAQPage, Article, HowTo, Organization, and Product schema all help models parse and trust your pages. Most B2B sites have weak or missing schema — fixing this is one of the highest-leverage GEO interventions.

5. Entity signals. Models build associations between brands and topics from consistent mentions across the web — third-party publications, knowledge graph entries, structured data, and authoritative directories. Strong entity signals make the model more likely to surface your brand when the topic comes up. See our guide to AI Overview optimization for more on entity engineering.

6. Coverage breadth. As we covered in the query fan-out section, your content needs to address multiple sub-queries within each priority topic. Pages that cover only the headline question lose to pages that cover the full surface area.

GEO measurement and ROI

Measuring GEO is harder than measuring SEO because AI platforms do not (yet) provide a clean equivalent of Google Search Console. There is no “ChatGPT impressions” report, no “Perplexity citations” dashboard, and no ranking tool that tracks every AI engine. That makes ROI conversations harder — and it is one reason GEO is still under-invested by B2B brands.

The metrics we track fall into three buckets. Visibility metrics measure how often your brand appears in AI responses for priority queries: citation frequency, citation share vs. competitors, and number of unique queries where you are cited. We test these by querying the AI engines directly on a recurring schedule and parsing the responses. Traffic metrics measure the downstream impact: referral traffic from ChatGPT, Perplexity, Gemini, and Copilot in your analytics tool, plus AI Overview impression and click data from Google Search Console where available. Brand metrics measure how AI engines describe you: sentiment in AI responses, accuracy of brand descriptions, and the topics models associate with your brand.

ROI calculation comes from connecting these visibility and traffic metrics to pipeline. AI-driven referral traffic typically converts at higher rates than generic organic because users have already received a vetted answer and are clicking through to validate it — they are pre-qualified. Most clients see 2-5x conversion rates from AI referral traffic vs. traditional organic. As AI search continues to grow, that pipeline impact compounds quickly.

GEO for B2B tech companies

B2B tech is one of the highest-leverage verticals for GEO, for three reasons. First, B2B buyers are early adopters of AI tools — engineers, product leaders, and enterprise sales operators all use ChatGPT and Perplexity heavily for research. Second, B2B tech queries tend to be complex and comparative, exactly the kind of queries where AI engines provide the most value (and where they generate the longest, most citation-heavy answers). Third, B2B tech sales cycles are long and consideration-driven — getting cited early in the research phase shapes how buyers think about your category for months.

The companies that win GEO in B2B tech are not necessarily the biggest. They are the ones who publish comprehensive, structured, data-rich content about their category, who maintain strong entity signals across third-party sources, and who treat AI search as a primary distribution channel rather than an afterthought. We help our clients get there faster by porting the playbooks we have developed across SaaS, AI, cybersecurity, fintech, and developer tools. See our B2B content strategy guide for how this fits into the wider go-to-market motion.

Common GEO mistakes B2B brands make

Five mistakes account for most failed GEO programmes. The first is treating GEO as a tweak to existing SEO — running the same playbook with a few schema additions and expecting different results. GEO is a separate discipline with its own measurement and its own tactics. Treat it that way.

The second is keyword-thinking. Brands that map content to single keywords miss the query fan-out reality. Every priority topic needs comprehensive coverage of the sub-queries the AI engines actually generate, not coverage of the headline keyword.

The third is burying the answer. Long intros, marketing fluff, and hero sections push the substantive content below the chunk window that AI engines retrieve. Lead with the answer.

The fourth is ignoring schema. JSON-LD structured data is the easiest, highest-leverage GEO intervention available, and most B2B sites either have no schema or have malformed schema that does not parse cleanly. Fix this first — it has the fastest payoff.

The fifth is not measuring. If you cannot tell whether your brand is being cited more this month than last month, you cannot manage the programme. Set up citation tracking on day one — even crude tracking is better than none.

GEO agency vs DIY GEO

The case for hiring a GEO agency comes down to speed, expertise, and tooling. GEO is a fast-moving discipline — AI platforms update their retrieval and ranking systems on weekly cycles, new platforms launch, and best practices shift constantly. An agency that does GEO every day across multiple clients is constantly seeing what works, what stops working, and what is new. An in-house team trying to learn on the fly is always six months behind.

Tooling is another factor. Tracking AI citations across ChatGPT, Gemini, Perplexity, Claude, Copilot, and Google AI Overviews requires either custom infrastructure or paid platforms — neither of which is cheap or easy to set up. Agencies have already paid the upfront cost and amortise it across clients.

The case for DIY GEO exists too. If you have a strong in-house content team and a technical SEO who can learn quickly, you can absolutely build GEO capability internally. The trick is to be honest about the learning curve. Most B2B tech companies underestimate it by 6-12 months, ship low-quality work in the meantime, and end up worse off than if they had brought in a specialist from the start.

We frequently work alongside in-house teams in a hybrid model: we run the audit, build the strategy, train the team, and stay on as a strategic advisor while the in-house team handles execution. This is often the most cost-effective option for companies that want to build capability long-term but need to move quickly now.