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GEO
AI Search Optimization
Show up when buyers ask AI — not just when they Google.
Overview
What we do & why it matters
AI search optimization makes your brand visible inside AI-generated answers across ChatGPT, Perplexity, Gemini and Copilot. We engineer the retrievability, authority and structure these engines reward, and track your share-of-voice per engine.
What’s included
- AI-visibility audit across engines
- Retrievability & structured-data work
- Answer-ready content
- Entity & authority building
- Per-engine share-of-voice tracking
- CRM attribution for AI leads
Part of our GEO engagement · from transparent pricing.
The shift
AI search changes the funnel, not the fundamentals.
Generative results sit above the links and answer part of the question before anyone clicks. The mechanics underneath are still semantic retrieval and entity understanding — the same machinery that decides classic rankings — but the outcome is measured differently.
Visibility without traffic
Being the source of an answer does not guarantee a visit. Presence has to be measured separately from sessions, or the work looks like it failed.
Fewer, better-qualified clicks
The people who do click after reading a summary arrive further along the decision — landing pages should assume more knowledge, not less.
Brand becomes the retrieval key
Models connect scattered mentions to an entity. Inconsistent naming across your site, directories and press splits that entity in two.
Volatility is normal
The same prompt returns different sources between runs. One check proves nothing; distribution across repeated runs does.
Practically, AI search work is 70% the SEO you should be doing anyway and 30% specific to retrieval: structure, entities, and measurement that does not depend on clicks.
The programme
How we run it.
The surfaces
Five places answers get generated, and they do not behave alike
“AI search” is not one system. Each surface assembles its answer from a different retrieval layer, which means a page can be cited constantly in one and invisible in another. Treating them as a single channel is how programmes end up optimising for an average that does not exist.
Google AI Overviews
Built largely on what already ranks, so classical search position remains the strongest lever. Weak organic presence means almost no chance of being drawn on.
ChatGPT with browsing
Retrieves live and favours pages that answer a specific question in a self-contained passage. Clean markup and fast, unblocked access matter here.
Perplexity
Cites aggressively and visibly, which makes it the easiest surface to measure. Rewards pages structured as direct answers with evidence attached.
Microsoft Copilot
Leans on Bing's index, so Bing coverage — often neglected entirely — becomes a prerequisite rather than an afterthought.
Gemini
Blends Google's index with model knowledge, so brand familiarity in training data and current ranking both contribute.
Model memory
Answers given without retrieval at all, drawn from what the model absorbed during training. Slow to shift, and influenced mostly by third-party coverage rather than your own site.
We check your presence on each separately and tell you which are realistically winnable in the next two quarters, rather than quoting a single blended visibility score.
Content shape
What makes a passage quotable
Retrieval happens at the passage level, not the page level. A long, excellent page that never states a clean answer in one place loses to a shorter page that does — which is why so much well-written content gets read by the model and credited to someone else.
Answer first, context after
The direct answer in the opening lines, in the question's own vocabulary. Everything the reader needs to trust it follows underneath.
Self-contained passages
Each section standing on its own, because the model will extract it without the three paragraphs above that gave it meaning.
Specifics over adjectives
Numbers, named conditions, dates and thresholds. Concrete statements get quoted; marketing language gets paraphrased away without attribution.
Stated scope and caveats
Where something is true and where it is not. Assistants favour sources that qualify their claims, because hedged text is safer to repeat.
Structure the parser can read
Real headings, real lists, real tables. Visual formatting built from styled divs is invisible to the thing deciding what to quote.
Freshness signals that are honest
Dates that reflect genuine revisions. Rewriting a timestamp without changing the content is noticed and discounted.
Measurement
How we know whether any of this worked
AI visibility has no equivalent of Search Console, so measurement has to be constructed. Here is what we actually track, and how.
Variance between runs is real and we report it as a range. A single flattering screenshot of a chat answer is not evidence, and we will not present it as one.
Off-site
Most of what assistants say about you is not written by you
Your own site is one source among many, and on brand and comparison questions it is rarely the one that gets quoted. Assistants reach for sources they read as independent — directories, review platforms, community threads, trade press. Which means a share of this work happens on properties you do not control.
Directory and listing accuracy
Outdated descriptions and wrong categories propagate quietly into answers. Cheap to fix, and one of the most common causes of an assistant describing you wrongly.
Review platforms
Frequently cited on ‘best tool for’ questions because they read as comparative and independent. Profile completeness matters more than most teams assume.
Community threads
Where buyers ask each other. Participating honestly as yourself works; astroturfing is detectable, and we will not do it.
Trade and analyst coverage
Earned mentions carry disproportionate weight in answers about credibility, because the model treats them as third-party corroboration.
Consistency across all of it
One description, one set of facts, everywhere. Contradictory claims across sources make a model hedge or pick the wrong one.
Correcting the record
When an answer is wrong, the fix is usually at the source feeding it. Finding that source is the work; the edit itself is often trivial.
We audit the off-site picture alongside your own pages, because fixing a stale listing frequently moves an answer further than rewriting a page does.
Honest limits
What nobody can promise you here
This field attracts confident claims. Some of them are not possible, and you should know which before anyone takes your money.
Guaranteed citations
Answers are generated probabilistically and change between runs on the same prompt. Anyone guaranteeing placement is describing something they cannot control.
A fixed timeline
Retrieval-based surfaces can shift within weeks; model memory shifts over training cycles you have no visibility into. We state which bucket each target falls in.
Winning without organic presence
On the surfaces built over a search index, ranking remains the prerequisite. GEO does not route around an absent SEO foundation; it compounds one.
Control over what is said
You can improve the evidence available and correct the record. You cannot dictate phrasing, and corrections to model memory propagate slowly.
One number for all surfaces
A single ‘AI visibility score’ averages systems that work differently. We report per surface, which is less tidy and more useful.
Immunity from platform change
These systems are rebuilt frequently. The work that survives is accurate, well-structured, genuinely useful content — which is also the work that survived every previous algorithm change.
More GEO
The rest of the GEO stack.
Questions
AI Search Optimization, answered.
How do you track AI visibility?
We monitor citations and share-of-voice across engines for your core prompts, and tie resulting leads to your CRM.
Is this different from SEO?
It shares foundations but optimizes for being cited in generated answers, not just ranking a page. We run both together.
Do we need SEO before AI search optimisation makes sense?
For the surfaces built on top of a search index — AI Overviews, Copilot, to a degree Gemini — yes, because they draw heavily on what already ranks. If you are absent from page one, those systems mostly cannot find you to cite. Retrieval-based assistants are more forgiving and can surface a well-structured page with modest rankings, so there is real work to do in parallel. We check which situation you are in during the audit rather than assuming.
How is this different from the GEO work you sell?
In practice they are the same programme described from different angles, and we run them together. AI search optimisation is the broader framing — being present and correct across every surface that generates answers. Answer engine optimisation focuses on the page-level craft of making content quotable, and entity work focuses on being understood as a distinct organisation. If you engage on one, you get the parts of the others that your situation needs, rather than three separate retainers.
Should we block AI crawlers to protect our content?
That is a business decision, not a technical one, and it is worth making deliberately. Blocking protects content from being used without attribution; it also removes you from the surfaces where your buyers increasingly research. For most B2B companies whose content is marketing rather than the product, visibility is worth more than protection — but if your content is the product, the calculus genuinely reverses. We will lay out what each choice costs and let you decide.
What does the prompt set look like?
Between thirty and a couple of hundred prompts, written the way your buyers actually phrase the problem rather than taken from a keyword tool. It includes comparison prompts, problem prompts, vendor-selection prompts and prompts where a competitor is named. It is fixed at the start so results stay comparable, and reviewed quarterly as your market language changes.
How often do you re-run the measurement?
Monthly for the core prompt set, which is frequent enough to see movement and infrequent enough that run-to-run noise does not dominate. Because the same prompt can return different sources on consecutive runs, we run each prompt several times and report a range rather than a single figure. A metric that moves five points on re-measurement is not a result, and we would rather say so than let a chart imply otherwise.
Can you fix an assistant saying something wrong about us?
Often, and it is one of the more valuable things this work does. Incorrect claims usually trace back to a specific source — an outdated directory listing, an old pricing page, a stale third-party article, or inconsistent descriptions across your own properties. We find what is feeding the error and correct the underlying evidence. Retrieval-based answers then update within weeks; claims baked into model memory take considerably longer and sometimes do not fully clear.
Who on our side needs to be involved?
Less than most programmes, but not nobody. We need someone who can answer questions about how the product actually works and where it genuinely fits, because the content only earns citations by being specific and correct. We also need access to publish, and sign-off on claims. Where we will push back is on marketing language that cannot be substantiated — it reads as noise to a model and gets paraphrased away without attribution, so it costs you the citation it was meant to win.
Ready when you are
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