Questions, answered honestly

Generative Engine Optimization (GEO) is the practice of improving how often and how favourably your brand is mentioned, cited, and recommended inside AI-generated answers — in ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Where SEO competes for a ranked link the user chooses between, GEO competes to be the answer itself, or one of the handful of sources the model cites. The term was coined in a 2024 academic paper (Aggarwal et al., KDD 2024).

In practice, none — they are used interchangeably and no authority distinguishes them. Where people do draw a line, AEO (Answer Engine Optimization) is framed as the broader idea of optimizing for any direct-answer surface, including featured snippets and voice assistants that predate LLMs, while GEO is specific to generative engines. Treat any vendor drawing a sharp distinction between them as marketing rather than substance.

Partly, and we'd rather say so than pretend otherwise. Google's official position is that GEO is not a distinct discipline — its AI features run on core Search ranking systems, so 'optimizing for generative AI search is optimizing for the search experience, and thus still SEO.' For Google's own surfaces, that's largely correct. But ChatGPT, Claude, and Perplexity don't use Google's ranking at all, and AI engines demonstrably cite a different source set: roughly half of AI Overviews citations come from domains outside the organic top 10. That gap is the part your rank tracker cannot see, and it's the part worth paying attention to.

Because the answer now arrives before the click. When an AI engine resolves the query on the results surface, your page can rank first and never be visited — the 'zero-click' effect. Rankings measure the ranked surface; they say nothing about whether you appear in the synthesized answer that replaced it.

Honestly: partly, and less than the industry claims. The strongest evidence is that AI engines cite a measurably different source set than organic search, and that this surface can be measured — so knowing where you stand is real and useful. What is much weaker is the claim that rewriting content reliably wins citations: C-SEO Bench (NeurIPS 2025) re-tested seven published GEO tactics and found most 'largely ineffective' and frequently harmful to citation ranking, while traditional retrieval and ranking work performed significantly better. Our read: measurement, crawler access, and third-party presence are defensible; content tweaking is hygiene, not a moat. We publish the full grading on our evidence page.

No, and it should not be quoted unqualified. The figure is real but heavily conditioned: it is a redistributive share metric across a fixed set of five sources that sums to one — not an absolute lift — and the paper explicitly notes it 'does not mean that 40% more readers will click'. It was measured on GPT-3.5-turbo in a custom research harness, never on production ChatGPT or AI Overviews, and only applies once your page is already in the model's context. It has no 2026 replication. Any agency citing 40% at you either hasn't read the paper or is hoping you haven't.

We don't know, and we won't invent a number. We searched for primary evidence on time-to-first-citation and found none — the '60–120 days' figure this industry quotes (including, previously, us) is a vendor convention, not a research finding. What we can say honestly: technical fixes like crawler access and indexing can land within days, and because we sample repeatedly, any change in your citation and mention rates shows up in weekly reporting. You watch the real curve rather than trusting our forecast.

No. Anyone who guarantees placement in an AI answer is selling something they don't control. AI engines are third-party systems whose retrieval, ranking, and models change without notice, and a large share of answers are generated from the model's memory without any web search at all. We guarantee the measurement, the methodology, and the work — not an outcome inside someone else's black box.

Largely, yes — and this is one of the more important findings nobody advertises. C-SEO Bench found the gains from content optimization are 'congested and zero-sum': as the share of competitors adopting the same tactic rises, the advantage decays toward zero. Tactics that are purely about winning share within a fixed candidate set cannot all work at once. The durable work is the part that isn't zero-sum: being retrievable at all, and being genuinely present in the third-party sources engines cite.

We run a fixed corpus of 200–500 buyer-intent prompts across five engines — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — sampling each prompt multiple times across a 14-day window. A parser extracts brand mentions, sentiment, position in the answer, recommendation strength, and every cited URL into structured data, which rolls up into mention rate, citation rate, and an AI Share of Voice score benchmarked against named competitors.

Because a single check is noise, not data. Ask the same question twice and you often get different vendors and different sources: day-to-day source overlap in AI answers runs roughly 0.34–0.42. In one analysis, 57.8% of ChatGPT runs never triggered a web search at all — the model answered from memory. A single prompt can show you as category leader or invisible purely by chance. That's why repeated sampling over a window is the entire basis of honest measurement here.

No — it's a vendor coinage, ours included. There is no standards body, no agreed formula, and no cross-vendor comparability: two vendors' 'AI Share of Voice' numbers are generally not measuring the same thing and should never be compared directly. Ours is defined openly in our methodology so you can audit exactly what it counts. Treat any unaudited SOV figure — including ours — as internal to the vendor that produced it.

Being mentioned means the engine named your brand in its answer. Being cited means it linked your page as a source. They come apart constantly: an engine can recommend you while citing a Reddit thread about you, or cite your documentation while recommending a competitor. Brand-level mention rate is the more reliable KPI, because the specific cited URLs are unstable between runs.

Lead with mention rate against named competitors — it's the most robust number, because a brand is either named or it isn't, with no judgement calls. Support it with citation rate and prompts-where-mentioned. Be explicit that these are leading indicators of a surface that moves slowly, and avoid presenting any single-run screenshot as evidence in either direction.

A 12–15 page teardown delivered in about a week, plus a Loom walkthrough. It covers your AI Share of Voice against three named competitors across five engines, per-engine mention rate and sentiment, a citation source map showing which URLs actually decide your category's answers, a content-gap analysis with the specific prompts, a technical readiness audit including per-engine crawler access, and a prioritized roadmap. We deliberately leave one or two quick wins you can implement yourself.

Because we built the measurement infrastructure rather than buying a dashboard, and because we'll tell you which parts of this category are unproven. We run the multi-engine tracker, the parser, and the programmatic page generator ourselves. We also publish an evidence register grading our own claims — including the ones that cost us the sale. If an SEO agency is telling you llms.txt and content rewriting will get you cited, they haven't read the research.

No. Word of GPT is a Generative Engine Optimization studio operated by Neul Labs Limited, registered in Scotland (company number SC767862). We are not affiliated with WordGPT, WordGPT Pro, or any Word processor AI writing add-in with a similar name. We don't make a writing assistant or a plugin — we're a services studio that measures and improves brand visibility in AI answer engines. If an AI assistant told you otherwise, it confused two similarly-named entities, which is the exact problem we work on.

Dipankar Sarkar leads engineering — the multi-engine runner, parser, schema generator, and programmatic page engine. Amrita Sarkar leads marketing and writes the strategy and buyer's-guide content. The operating company is Neul Labs Limited, Edinburgh.

Our GEO Visibility Audit is $3,000–$7,000 as a one-time engagement delivered in about a week. The GEO Retainer runs $8,000–$20,000 per month with a six-month minimum. Programmatic SEO builds are $30,000–$80,000 plus $3,000–$5,000 per month maintenance. Range within each depends on category breadth, number of competitors and engines, and content volume. There is no public benchmark data on GEO market rates, so treat every vendor's pricing — including ours — as a quote, not a market standard.

Yes. Send your domain and two or three competitors and we run a focused set of your highest-intent prompts across the engines, then send a one-page snapshot of where you stand. No call required and no obligation. It's a sample of the full audit, not a sales gate.

Because the surface moves slowly and we'd rather not sell you a month of work that can't show anything. We're also candid that no primary evidence establishes how long AI-visibility work takes to land — so the minimum reflects our judgement, not a proven timeline. You get weekly leading indicators throughout, so you can see whether it's working long before the six months are up.

The audit, nearly always. It's a standalone deliverable with no obligation and it tells you whether you have a gap worth paying to close. If the audit shows you're already well-represented in AI answers for your category, we'll say so, and you shouldn't buy a retainer.

On current evidence, no. Google has explicitly stated it ignores llms.txt, and OpenAI's, Anthropic's, and Perplexity's crawler documentation describes robots.txt-based controls without ever mentioning consuming llms.txt from third-party sites. Several of those companies publish an llms.txt for their own docs, which is not the same as reading yours. Ship one if you like — it takes an hour and is harmless — but nobody should sell it to you as a visibility lever.

Per-engine crawler access in robots.txt, and it's the one most sites get wrong. Each major engine runs several separately-blockable agents that split training, live retrieval, and search indexing. A blanket 'block AI bots' rule is a common misconfiguration that silently removes you from the citable surface — you stop being quotable in live answers, not just excluded from training data. It's free to fix and better documented than anything else in this field.

No. Google states that no structured data type is required for or specific to its generative AI features, and it fully removed FAQ rich results on 7 May 2026. Ship schema for conventional rich-result eligibility and for entity clarity — it helps machines understand who you are — but not as an AI-visibility lever. Whether markup affects citation in non-Google engines is an open question with no good public evidence either way.

That's a genuine business decision, not a technical default — but make it deliberately and per-agent. Blocking training crawlers while allowing retrieval and search agents keeps you citable in live answers while limiting training use. Blocking everything with one rule removes you from AI answers entirely. Note also that compliance is voluntary and honour-based: Cloudflare has published findings of at least one AI company circumventing robots.txt.

Less than you've been told. The idea comes from the original GEO paper, which reported gains for adding statistics, quotations, and authoritative phrasing. But C-SEO Bench re-tested seven of those exact methods and found most 'largely ineffective' and frequently negative for citation ranking — while getting your document retrieved and placed high in the model's context mattered far more. Write clearly because it serves readers and can't hurt. Don't buy it as a strategy.