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GEO & AEO Services

Getting your business cited inside AI Overviews, ChatGPT, Perplexity and Copilot — a discipline related to SEO but scored on different signals.

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At a glance

Focus
Generative engine optimisation
Deliverables
10 documented items
Market
United Kingdom
From
£350 / month
Commitment
3 months, then rolling

On this page

A growing share of research now ends inside an AI answer rather than on a website. The user asks a question, gets a synthesised response with three or four cited sources, and never clicks through to the other twenty results. If you are not among the cited sources, you are invisible for that query regardless of where you rank.

Citation and ranking overlap but are not the same. Models select passages they can extract cleanly and attribute confidently, which is why smaller, well-structured sites regularly get cited above much larger competitors. That gap is the opportunity, and it is closing.

01

What earns an AI citation

  • Extractable passages. A self-contained answer in the first two or three sentences of a section, understandable without the surrounding context. Models lift passages, not pages.
  • Clean definitional sentences. "Generative Engine Optimisation is the practice of..." is directly quotable. Preamble is not.
  • Structured comparison. Tables and parallel lists are extracted far more readily than the same content in prose.
  • Original data. A number that exists only on your page gives the model a reason to cite you specifically.
  • Entity consistency. The same business name, description and details across your site, your profiles and third-party mentions, so the model can resolve who you are with confidence.
  • Presence on cited sources. Models draw heavily on Reddit, industry forums, well-maintained directories and news. Visibility there feeds citation even when your own site is not the source.
02

Crawler access is the first blocker

You cannot be cited by a system that cannot read you. A large number of sites block AI crawlers without a deliberate decision — inherited robots.txt rules, WAF configuration, aggressive bot protection at CDN level, or a plugin default.

We audit access for GPTBot, OAI-SearchBot, ChatGPT-User, PerplexityBot, ClaudeBot, Claude-SearchBot, Google-Extended, Bingbot and Applebot, and test actual fetchability rather than trusting the robots.txt file — WAF rules routinely block crawlers that robots.txt permits.

Blocking is a legitimate choice for some businesses. But it should be made deliberately, per crawler, with the visibility trade-off understood, rather than discovered a year later.

03

llms.txt and structured access

llms.txt is an emerging convention: a markdown file at your root that gives language models a curated map of your most useful content, in a clean format without navigation and markup noise.

Adoption is still early and support is inconsistent, so it is not yet a reliable ranking or citation input. It is cheap to implement and does no harm, so we include it — but we do not present it as a primary lever, and we would be sceptical of anyone who does.

04

Measuring AI visibility

There is no reliable automated rank tracker for AI citation yet. Responses vary by session, by phrasing and by user context, and vendor tools that claim precision are largely inferring it.

We measure by structured manual testing: a fixed set of priority queries run monthly across Google AI Overviews, ChatGPT, Perplexity and Copilot, logging whether you appear, in what context, and which of your pages was cited. It takes about an hour a month and produces data you can actually act on — which is more than the automated alternatives currently deliver.

What's included

  • AI crawler access audit across all major agents, tested by fetch not by robots.txt
  • Passage-level citability scoring across priority pages
  • Content restructuring for answer-first extraction
  • Entity consistency audit across site, profiles and third-party mentions
  • Structured data implementation supporting entity resolution
  • llms.txt implementation
  • Comparison and definitional content development
  • Original data asset planning
  • Monthly manual citation testing across four AI surfaces
  • AI visibility reporting with query-level citation tracking

How we work

  1. 1 Unblock the crawlers Verify actual fetchability for every AI agent. Nothing else matters if you cannot be read.
  2. 2 Score citability Assess priority pages passage by passage for extractability, not page by page for quality.
  3. 3 Restructure for extraction Answer-first openings, clean definitions, tables where comparison is involved.
  4. 4 Consolidate entity signals Consistent naming and description everywhere a model might encounter you.
  5. 5 Test monthly by hand Structured manual prompt testing across four surfaces. Currently the only reliable measurement available.

Frequently asked questions

What is the difference between GEO, AEO and SEO?
SEO optimises for position in ranked results. AEO optimises to be the extracted answer in featured snippets and voice results. GEO optimises to be cited inside generative AI responses. They share foundations — crawlability, clarity, authority — but GEO rewards extractability and entity clarity more than raw domain authority.
Does GEO replace SEO?
No. AI answers draw heavily on search indexes, so ranking well remains a strong input to being cited. GEO is an additional layer that changes how you structure and present content, not a replacement for the underlying work.
Should we block AI crawlers?
It depends on your business. If content is your product, blocking may protect it. If discovery drives your revenue, blocking removes you from a growing share of research. The important thing is that it is a deliberate per-crawler decision rather than an inherited default — most sites blocking today never chose to.
How do we know if we are being cited?
Manual testing, currently. Run your priority queries across ChatGPT, Perplexity, AI Overviews and Copilot monthly and log the results. Automated trackers exist but their accuracy is not yet good enough to base decisions on.
Does llms.txt actually work?
Support is early and inconsistent, so treat it as cheap insurance rather than a lever. Implement it, keep it accurate, and do not expect measurable citation change from it alone.
Why do small sites get cited over large ones?
Because models select extractable passages rather than authoritative domains. A precise, well-structured answer on a small site beats a rambling one on a large site. This is the clearest current opportunity in AI search and it will not stay open indefinitely.

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