generative engine optimization (geo) for b2b: get cited

Generative engine optimization structures content, entities, and crawler access. The goal is a citation inside an AI answer.

That is the whole job. Not ranking. Citation. A generative engine reads a question and retrieves a handful of sources. It writes a synthesized answer and names the sources it leaned on. If your page is not in that retrieved set, you do not exist in the answer. Your position in the ten blue links underneath is irrelevant.

I run this on my own site. On 2026-09-09 I ran an answer-engine test across Perplexity, Claude, and Google AI Mode. The result was blunt. Ascendgtm.net got cited only on queries that already contained my name. Category prompts told a different story. Take the "best fractional CMO" style of question a buyer actually asks. The cited domains were listicles and directories. Not operator sites. Across 603 SERPs I track, 81% had an AI Overview present.

That gap between name-query citation and category-query citation is the real GEO problem. Name queries are easy. If someone already knows you, every engine finds you. Category queries are the ones that produce demand, and those are the ones I was losing. This article is the mechanics I use to close that gap.

What is generative engine optimization

The term comes from a 2023 paper by Aggarwal and co-authors. It is published on arXiv as 2311.09735. They formalized generative engines as systems that answer a query from multiple sources. A language model synthesizes and summarizes them. They introduced GEO as a black-box optimization framework, plus GEO-bench for evaluation. Their headline finding: GEO can lift visibility by up to 40 percent in generative answers.

"Up to 40%" is the honest number. It is a ceiling from a benchmark, not a promise for your domain. I quote it because it is the only peer-reviewed anchor the field has. Most vendor decks quote it without the "up to".

GEO, SEO, and AEO are not synonyms. Here is how I keep them separate.

AEO is the closest sibling. I wrote it up separately in what is answer engine optimization. The short version: AEO wins one box. GEO wins a seat in a paragraph that cites three or four sources. Both need clean definitions. Only GEO needs an entity graph.

How generative engines choose sources

Four mechanisms decide whether you get cited. Optimize for all four or you optimize for none.

Retrieval. Most answer engines run a live search, pull the top results, and read them. Some read a cached index instead. Either way the constraint is the same. Your text has to be there in plain form when the retriever arrives. Your page must be fetchable by their crawler. It must render its content without JavaScript execution. The substance must sit above the fold of the raw HTML. A page that needs client-side hydration to show its argument reads as empty.

Entity resolution. The engine has to decide that "Ascend GTM" and "Mishaal Murawala" are resolvable things. It also has to match them to the same names on LinkedIn and in a directory. That resolution runs on identifiers. Stable JSON-LD @id values and a sameAs array hand the engine those identifiers. Without them it guesses.

Corroboration across sources. This is the mechanic that catches most B2B sites. An engine that finds a claim on one domain treats it as an assertion. The same claim on three independent domains becomes a fact. The cleanest source gets cited. Your own site is a single source. A claim that appears nowhere else is structurally hard to cite.

Freshness. Answer engines weight recency heavily on anything that moves. Pricing, tooling, benchmarks, best-of lists. A visible dateModified and a real update history are retrieval signals, not decoration.

The on-site GEO checklist for B2B

This is the order I execute in. Each item is a mechanic, not a tactic.

1. Open your crawler allowances deliberately. GPTBot, ClaudeBot, PerplexityBot, and Google-Extended each need an explicit Allow. Put it in robots.txt. Without one you are not retrievable. Google-Extended is the one most teams get wrong. Per Google Search Central's AI features docs, it controls Gemini and grounded generation. It does not control whether you appear in AI Overviews. Blocking it removes you from other Google generative surfaces instead. That is usually the opposite of what the team intended.

2. Decide your Content-Signal header on purpose. The Content-Signal response header states three preferences separately. Search, AI input, and AI training. It is a declaration, not an enforcement mechanism. Set it because you have a position. Know that it blocks nothing by itself.

3. Build an entity graph with stable JSON-LD @ids. Give your Organization, Person, WebSite, and Article nodes permanent @id URIs. Never change them. Link them to each other by @id, not by repeating the name. Then attach a sameAs array. Point it at your LinkedIn, Crunchbase, GitHub, and any Wikidata item. This is the highest-leverage on-site GEO change. It converts a naming problem into an identifier lookup.

4. Ship llms.txt and llms-full.txt. Per llmstxt.org, /llms.txt is a markdown file at your root. It gives an LLM a curated map. A title, a short summary, and annotated links to the pages that matter. llms-full.txt extends that to the full content in one file. Treat it as a table of contents you control, not a robots file. It changes what a model reads first.

5. Serve markdown twins. Publish a plain markdown version of every article at <url>.md. Then honor Accept: text/markdown on the canonical URL. A client asking for markdown gets markdown. This removes the parsing failure modes between your HTML and the retriever. It is a small change with an outsized effect on what reaches the model.

6. Write definition-first paragraphs. The first sentence under every H2 answers the H2 as a standalone claim. Under 40 words, no throat-clearing. A retriever that grabs one paragraph should grab something quotable. Look at the first line of this article. That is the pattern.

7. Make your statistics quotable and sourced. A number with a named source and a date is citable. A number without one is a liability. A model that cannot corroborate it drops the whole passage. Every statistic in this article names its source. Do that on every page.

8. Expose updated dates in the markup. Put dateModified in your Article schema. Render the updated date visibly. Then actually update the page. A stale dateModified on unchanged content is worse than none. The corroboration check catches it.

9. Keep sentences short and claims separable. One idea per sentence. A model extracting a citation needs a span it can lift without breaking meaning. Long compound sentences with three clauses do not survive extraction. The test is simple. Can any single sentence be quoted alone and still be true?

10. Answer "what is this and who is it for" in plain language on every page. Not a positioning statement. A description a stranger could repeat back correctly.

The off-site GEO checklist

Here is the finding from my own answer-engine test that changed how I spend time. For category prompts, the domains getting cited were listicles and directories. Not the operator sites the listicles were about.

That is not a bug in the engines. It is the corroboration mechanic doing exactly what it should. A directory entry is a third-party statement about you. A listicle is a comparison that puts you in a set. Both are independent sources. Your own about page is not.

So the off-site work is entity footprint work.

1. Get into the listicles the engines already cite. Run your category prompts and note which domains come back. Treat that list as a target account list. These pages get retrieved for the questions your buyers ask.

2. Claim and complete the directory entries. Every profile is a sameAs target. Every profile is also an independent corroboration source. Incomplete profiles resolve badly.

3. Create or correct a Wikidata item if you are notable enough. Wikidata is a structured entity source many pipelines consume directly. If an item exists and is wrong, that error propagates.

4. Keep the facts identical everywhere. Same role string, same company name, same city. An engine reconciling three profiles with three job titles picks the most frequent one. Not the one you prefer.

5. Publish where a third party owns the byline decision. A guest post, a podcast transcript, a conference listing. The value is not the backlink. It is that someone else asserted your existence.

How to measure GEO

Rank tracking does not measure this. You need three instruments.

A prompt panel. Write 20 to 40 prompts a real buyer would type. Use their words, not your keywords. Split them into name queries and category queries. Run them monthly across the engines your buyer uses. ChatGPT, Perplexity, Claude, Google AI Mode. Record the full answer and every cited domain. This is the whole measurement system. Everything else is derived from it.

Citation rate. Per engine, the share of panel prompts where your domain appears in the citations. Track name queries and category queries separately, always. Mine were far apart. Averaging them would have hidden the entire problem.

Share of voice. Of all citations your panel returns, what share are yours versus each competitor. This is the number that tells you whether GEO work moved anything. It is comparative. A rising citation rate in a rising category is not progress.

One warning on measurement. Answer engines are non-deterministic. The same prompt returns different citations across runs. Run each prompt at least three times and record frequency, not a single result. A one-shot test is an anecdote. This is also why vendor screenshots of a single lucky citation prove nothing. Ask for the frequency across runs, or ignore the claim.

GEO services vs doing it in-house

A generative engine optimization agency sells a retainer. It typically covers a prompt panel, monthly reporting, and schema implementation. Then content production and digital PR. Reporting and digital PR carry genuine ongoing cost. They are labor that does not compress.

The technical layer does compress. Look back at the on-site checklist. Crawler allowances. The Content-Signal header. A JSON-LD entity graph with stable @id values. Then llms.txt, markdown twins, and definition-first rewrites of your top pages. That is a week for someone who can edit templates and write schema. It is not a retainer. It is a project with an end date.

So the split I would run:

• Do in-house, once, in a week: every on-site item. All ten. They are template changes and editing passes. Once shipped they keep working with no ongoing spend.
• Do in-house, monthly, in an hour: the prompt panel. Scripting it is straightforward and the data is more useful when you read it yourself.
• Buy, if you lack the relationships: placement in the listicles the engines already cite. That is outreach, and outreach is a relationship business.
• Do not buy: "AI search optimization" packages with no visible prompt panel. Or no citation-rate method. If the measurement is a black box, the work is unfalsifiable.

I am an AI marketing operator. I build these systems rather than advise on them. That bias is worth stating plainly. I will tell you the technical layer is a week of work, because for me it is. If your CMS cannot serve a custom header or a .md route, your week is longer. An implementation partner is then a reasonable buy.

What I would not do is buy strategy for the on-site layer. It is documented, it is deterministic, and the checklist above is the whole thing. The expensive part of GEO was never the schema. It is earning the third-party mentions that let an engine corroborate you at all.

For the systems view of how this connects to the rest of the funnel, see the platform. For what I actually do inside a company, see the marketing leader page. To measure your own citation footprint first, start with the growth diagnostic.

Sources

• Aggarwal et al., "GEO: Generative Engine Optimization," arXiv:2311.09735 (2023). Source of the "up to 40%" visibility figure and the GEO-bench framework. arxiv.org/abs/2311.09735
• Google Search Central, "AI features and your website." Covers Google-Extended scope. Google AI features docs
• OpenAI, "Overview of OpenAI Crawlers." GPTBot and related user agents. platform.openai.com/docs/bots
• Perplexity, "PerplexityBot" crawler documentation. docs.perplexity.ai/guides/bots
• llmstxt.org, the llms.txt specification. llmstxt.org

First-party data in this article comes from two measurements I ran myself. An answer-engine citation test on 2026-09-09. It covered Perplexity, Claude, and Google AI Mode. And AI Overview presence across 603 SERPs I track.