Intro

Search visibility is no longer limited to where a website ranks on Google.

Consumers and business buyers are increasingly using AI-driven platforms to research categories, compare products and services, understand complex buying decisions and ask directly which brands or providers they should consider. That changes what visibility means.

A company can perform well in traditional organic search and still be largely absent from AI-generated answers. Another brand may be mentioned frequently for informational questions but disappear when the same system is asked to recommend a provider, compare alternatives or create a shortlist.

Generative Engine Optimisation, commonly known as GEO, has emerged in response to this change. While definitions and terminology continue to evolve, the discipline increasingly covers far more than rewriting content for large language models.

Modern GEO can involve technical accessibility, semantic relevance, entity understanding, structured information, content engineering, prompt research, citation analysis, digital PR, third-party authority, competitive benchmarking and AI visibility measurement.

The agencies below approach these challenges from different directions. Some come from information retrieval and technical SEO, others from content, digital PR, B2B growth or dedicated AI Search strategy.

This is not a numerical ranking. The order is intended to make the agencies easier to compare. The most appropriate partner will depend on an organisation’s existing search maturity, market, technical environment, audience and specific AI visibility objectives.

What was considered for this list?

The focus is on agencies with publicly documented capabilities relevant to improving brand visibility across AI-driven discovery environments.

• dedicated GEO, AEO or AI Search capability
• AI visibility measurement and competitive analysis
• prompt and buyer-intent research
• understanding of mentions, citations and recommendations
• technical search and structured data expertise
• entity and semantic optimisation
• content designed for retrieval and citation
• digital PR and third-party authority
• ability to translate AI visibility data into practical strategic action

One principle is particularly important when comparing providers: AI visibility should not be judged by a single percentage alone.

A meaningful GEO programme should help a brand understand not only whether it appears, but where it appears, for which audiences and intents, how competitors perform, which sources influence the surrounding information environment and whether the brand is actually being surfaced when a user asks for a recommendation.

At a glance

Agency	Particular strength	Best suited to
iPullRank	Information retrieval and Relevance Engineering	Technically complex organisations
Go Fish Digital	Technical GEO, content and digital PR	Brands needing owned and off-site visibility
Netsleek	AI visibility measurement, Persona-Based Visibility and selection analysis	Established brands seeking a specialist AI Search partner
NoGood	AEO connected to growth	Growth-oriented companies
Siege Media	Citation-focused content and digital PR	Content-led organisations
Blue Array	GEO integrated with specialist organic search	Established UK and international brands
Omniscient Digital	Buyer-intent-led AI Search strategy	B2B SaaS businesses
Directive	B2B GEO and demand generation	Technology and B2B organisations
WebFX	Enterprise-scale AI Search implementation	Large organisations requiring scale
First Page Sage	B2B GEO and authority-led visibility	B2B and thought-leadership-driven companies

1. iPullRank

Best suited to: Organisations with technically complex search environments

iPullRank approaches modern search through its discipline of Relevance Engineering, which brings together information retrieval, technical SEO, content strategy, user experience, artificial intelligence, measurement and digital PR.

This is an important distinction for organisations whose AI visibility problem cannot be solved simply by producing additional content. Modern retrieval systems need to find, interpret and evaluate information at a more granular level, including the relevance of individual passages and the relationships between topics and entities.

iPullRank has also invested extensively in explaining the mechanics of AI Search through its educational work around retrieval, generative search and modern search measurement.

For brands with large websites, sophisticated search programmes or complicated information architectures, iPullRank is particularly relevant where GEO needs to be approached as a technical information-retrieval problem as well as a marketing discipline.

2. Go Fish Digital

Best suited to: Brands that need technical GEO, content and off-site authority working together

Go Fish Digital approaches Generative Engine Optimisation through technical search, semantic relevance, retrieval-ready content and the wider information environment surrounding a brand.

Its published approach places particular emphasis on making content retrievable, understandable and useful enough to contribute to AI-generated answers. This goes beyond simply changing writing style. It includes the structure and semantic relevance of information as well as whether content is suitable for retrieval and citation.

The agency also brings digital PR expertise into the equation. That matters because the information influencing an AI-generated response does not necessarily come from the company being discussed. Independent publications, reviews, industry resources and other external sources can all contribute to the evidence available about a brand.

Go Fish Digital is therefore particularly relevant to organisations that need to work on both their owned digital properties and their wider third-party information footprint.

3. Netsleek

Best suited to: Established brands seeking a specialist AI Search & Brand Discoverability partner

Netsleek is a global, remote-first AI Search & Brand Discoverability agency specialising in Generative Engine Optimisation, Answer Engine Optimisation, AI visibility measurement, entity optimisation, AI-ready content engineering and technical search.

AI Search forms the centre of the agency’s practice rather than being an additional service added to a broader digital marketing portfolio.

A significant part of Netsleek’s approach concerns what AI visibility actually represents.

Rather than treating one percentage as a complete measure of performance, its methodology distinguishes between outcomes such as mentions, citations and recommendations, while also considering the prompts being tested, AI systems involved, competitor presence, underlying intent and the evidence available around a brand.

Netsleek has developed proprietary AI Search methodologies, including its Persona-Based Visibility framework, which examines how a brand’s visibility, citation and recommendation patterns can vary depending on the persona, intent and decision context represented by a prompt.

The underlying principle is that a brand does not necessarily have one universal AI visibility state. A CMO, technical evaluator, procurement lead and Head of Search can frame similar buying decisions differently. Those differences can change which brands appear relevant, which competitors are surfaced and which providers are ultimately recommended.

A blended visibility score can therefore conceal commercially important variation between audiences. Persona-Based Visibility is intended to show not simply whether the brand appears, but whether it is appearing for the people and decision contexts that matter to the business.

Netsleek’s research also explores what it calls the Selection Layer in AI Search, an analytical framework for examining the point where retrieval and eligibility alone no longer explain which brands, entities, sources and evidence ultimately influence the generated response. It focuses on the distinction between being available for consideration and actually being selected for a mention, citation or recommendation.

The Selection Layer is used as a conceptual model rather than a claim that every AI platform contains a literal software component by that name. Its purpose is to help examine the gap between information being retrievable and that information surviving into the response a user ultimately sees.

Together, these methodologies support a broader measurement philosophy: AI visibility is more useful when organisations can understand where they appear, why they appear, who they appear for and whether visibility survives into commercially meaningful recommendation contexts.

Netsleek combines this analysis with entity optimisation, structured information, technical search, AI-ready content and third-party corroboration. It works primarily with established, mid-market and enterprise organisations across the UK, Europe and the US.

4. NoGood

Best suited to: Growth-oriented brands that want AI visibility connected to acquisition

NoGood has built a substantial body of work around Answer Engine Optimisation and AI Search visibility, including the technical requirements that allow AI systems to access, interpret and potentially cite website content.

Its approach is particularly relevant to growth-focused companies because visibility is considered in a broader commercial context rather than as an isolated search metric.

That distinction matters. Increasing the number of AI mentions may have limited business value if those appearances occur for prompts unrelated to meaningful customer demand.

NoGood is therefore a useful option for companies that want AI Search optimisation integrated into a wider growth programme, with technical AEO, content and acquisition strategy considered together.

5. Siege Media

Best suited to: Content-led organisations focused on citations and third-party authority

Siege Media approaches GEO through the relationship between content, citations, digital PR and external authority.

Its service model includes prompt-level citation mapping, AI share-of-voice measurement, bottom-of-funnel content, data journalism, publisher outreach and ongoing reporting around citations and commercial outcomes.

This reflects an important characteristic of AI Search. The source that influences an answer is not always the website belonging to the brand being discussed.

Editorial roundups, research, publisher content and other third-party surfaces can become part of the information environment surrounding a category or buying decision.

Siege is therefore particularly relevant for organisations with mature content programmes that want to extend their strategy from creating information on their own websites to earning visibility across the external sources that shape AI-generated answers.

6. Blue Array

Best suited to: Established brands extending mature organic search programmes into GEO

Blue Array brings its specialist organic-search background into Generative Engine Optimisation.

Its GEO offering includes AI visibility monitoring, citation and mention analysis, competitive benchmarking, technical optimisation and work around content discoverability for generative systems.

This integrated approach is useful for organisations that already have established SEO programmes and do not want AI Search treated as a completely separate marketing channel.

Traditional search and generative discovery still share many underlying foundations, even though the observable outcomes can be different. Technical accessibility, authority, useful content and clear entities continue to matter, but the measurement surface expands from rankings and clicks to mentions, citations and AI-generated recommendations.

Blue Array is particularly relevant to UK and international brands that want GEO incorporated into an established specialist organic-search programme rather than run as an isolated experiment.

7. Omniscient Digital

Best suited to: B2B SaaS organisations connecting AI Search with buyer intent

Omniscient Digital approaches AI Search through the lens of B2B content strategy and buyer behaviour.

Its research has examined tens of thousands of LLM citations to understand how the types of sources appearing in AI-generated answers can change across different stages of buyer awareness.

This illustrates an important point for GEO measurement: different prompts perform different jobs.

A general educational question, a category-exploration prompt and a vendor-comparison query represent very different moments in the buying journey. Measuring them as though they are interchangeable can hide where a brand is genuinely influencing customer consideration.

For B2B SaaS organisations, Omniscient is therefore particularly relevant where GEO needs to connect content, AI citation behaviour and the progression of buyers towards a commercial decision.

8. Directive

Best suited to: B2B and technology organisations connecting AI discovery with demand generation

Directive has developed a dedicated Generative Engine Optimisation offering for B2B brands.

Its approach covers areas such as entity clarity, structured information, content optimisation, prompt testing and AI visibility, with a strong emphasis on the commercial role AI-generated discovery can play in the B2B buying process.

This is particularly relevant because AI platforms can influence buyers before they ever reach a company’s website.

A prospective customer might ask an AI system to explain a category, compare technologies, identify potential vendors and evaluate different approaches before beginning a traditional website journey.

For B2B organisations, entering those early consideration environments can therefore become part of demand creation as much as search optimisation.

Directive is particularly suitable for technology and B2B companies that want GEO connected to demand generation, category visibility and pipeline rather than evaluated purely through search metrics.

9. WebFX

Best suited to: Large organisations requiring AI Search implementation at scale

WebFX offers enterprise AI Search optimisation covering technical optimisation, authoritative content, entity work, structured information, authority building and AI citation tracking.

Its service also includes visibility audits, competitive benchmarking and monitoring across multiple AI and traditional search environments.

For larger organisations, the challenge is often not identifying one GEO tactic but coordinating implementation across substantial websites, different business units and multiple internal stakeholders.

That makes scale an important consideration when choosing a provider.

WebFX is particularly relevant to enterprises that need AI Search visibility incorporated into a broader digital operation with substantial reporting, content and implementation infrastructure.

10. First Page Sage

Best suited to: B2B organisations where authority and recommendation visibility influence buying decisions

First Page Sage approaches GEO from a strong B2B and thought-leadership background.

The agency has also invested in researching the GEO agency market itself, including comparisons of agencies working in both general and B2B Generative Engine Optimisation.

Its orientation is particularly relevant to industries where buying decisions depend heavily on perceived expertise, credibility and authority.

B2B users increasingly ask AI systems to identify providers, compare approaches and suggest companies suited to particular requirements. In these environments, merely publishing content around relevant keywords may not be sufficient. The broader evidence surrounding the organisation can affect whether it is considered an appropriate recommendation.

First Page Sage is therefore a useful option for organisations where GEO needs to work alongside long-form expertise, authority development and B2B thought leadership.

Why AI visibility cannot be reduced to one score

One of the first questions organisations entering GEO usually ask is how AI Search visibility should be measured.

A percentage can be useful as a summary indicator, but the number becomes difficult to interpret when the conditions behind it are unclear.

Consider three different outcomes.

A mention tells you that the brand appeared.

A citation indicates that a brand or its content was referenced as supporting information.

A recommendation tells you that the brand was actively surfaced as an option in response to a question involving evaluation or choice.

These outcomes are related, but they are not equivalent.

A company could be mentioned regularly without being cited. It might be cited frequently as an informational resource but rarely recommended as a provider. Another could have relatively low overall visibility while performing exceptionally well for a smaller set of commercially important recommendation prompts.

The prompt set matters too.

If an organisation tests 100 educational questions, its resulting visibility score represents something very different from a study containing category comparisons, buyer research, vendor shortlisting and high-intent recommendation prompts.

The AI platforms included in the measurement also matter because different systems can retrieve different information, use different sources and produce different outputs.

This is why the useful question is rarely just:

How visible are we?

It is:

Visible where, for whom, for which intentions, against which competitors and in what role?

The same brand can have different visibility for different audiences

AI Search introduces another layer of complexity because the persona, intent and decision context represented by a prompt can influence the answer.

Consider a company looking for a technology provider.

A CMO might frame the question around strategic impact.

A technical director might emphasise integration, architecture and implementation.

A procurement lead could prioritise organisational scale, risk and supplier suitability.

A smaller company might ask about affordability and ease of deployment.

Those queries concern the same general category, but they do not necessarily imply the same ideal provider.

As a result, a brand can appear strongly for one decision-maker profile while remaining almost invisible for another.

This is why persona and intent segmentation can add useful context to AI visibility measurement. Instead of assuming that visibility is universal, organisations can examine where their brands are present for different audiences, decision stages and commercial scenarios.

The goal is not simply more visibility.

For many organisations, the more valuable objective is relevant visibility, appearing for the people and situations where the brand is genuinely suited to the requirement.

Retrieval is only part of the visibility problem

Technical accessibility remains fundamental to AI Search.

If a retrieval system cannot reach, parse or interpret information effectively, that information is less likely to influence an answer.

But retrieval should not be confused with final visibility.

A system may have access to numerous relevant sources while only a subset influences the generated response. Similarly, several brands can be plausible candidates for a request while only a few are ultimately presented to the user.

This creates an important distinction between being available for consideration and actually being surfaced.

That distinction becomes particularly important for recommendation queries.

When a user asks for suitable providers, software platforms, agencies or products for a specific requirement, the resulting answer may contain only a limited selection from a much larger group of plausible entities.

From a brand perspective, the optimisation problem therefore develops in stages.

First, can the system discover and interpret the brand?

Then, does the available information support its relevance to the category and context?

Is there sufficient evidence surrounding that interpretation?

And finally, when multiple plausible entities are available, does the brand survive into the response the user actually sees?

This is why modern GEO increasingly involves entity clarity, contextual relevance, corroborating evidence, content quality and third-party authority alongside conventional technical optimisation.

AI Search is becoming a consideration-set problem

Traditional SEO has largely focused on earning visibility within a ranked set of results.

The user was then responsible for evaluating those options.

AI-generated discovery changes that relationship.

A user can now ask:

Which platforms should I consider?

What providers are suitable for a company like mine?

Which agencies specialise in this problem?

What would you recommend for an enterprise organisation?

The system may return a relatively small group of brands.

At that point, visibility is no longer simply a ranking problem. It becomes a consideration-set problem.

A business that is consistently absent from those shortlists can lose potential consideration before the buyer reaches its website.

This does not mean traditional SEO has stopped mattering. Strong technical foundations, useful content, authority and discoverability remain important.

What has changed is that brands also need to understand how they are represented as entities, how they compare with alternatives, what external evidence exists around them and whether the information available supports their inclusion in high-value recommendation contexts.

What should a brand ask a GEO agency?

Before appointing a GEO or AI Search partner, organisations should look beneath headline visibility claims and understand the methodology being used.

• Which AI systems are actually being measured?
• How are prompts selected?
• Are the prompts connected to genuine customer or buyer intent?
• Are different personas and stages of the decision journey represented?
• How many observations or runs support the conclusions?
• Are mentions, citations and recommendations measured separately?
• How are competitors benchmarked?
• Can the agency identify which external sources appear around competing brands?
• Does the work address entity understanding as well as content?
• Are technical accessibility and structured information evaluated?
• Does the strategy consider third-party authority beyond the company’s own website?
• Can findings be translated into specific implementation priorities?
• Are referral traffic, leads, pipeline or other commercial outcomes measured where appropriate?

The answers to those questions often reveal more about the maturity of a GEO programme than the headline percentage displayed in a dashboard.

Choosing the right GEO agency

There is unlikely to be one universal model for Generative Engine Optimisation.

A technically complex enterprise may need deep retrieval expertise and large-scale implementation.

A B2B SaaS company may care more about becoming visible during vendor research and comparison.

A content-led organisation may have strong owned assets but weak representation across the external sources AI systems retrieve.

Another brand may already appear regularly in AI answers but discover that its visibility is concentrated among low-value informational prompts rather than the audiences most likely to buy.

That is why choosing a GEO agency starts with identifying the actual visibility problem.

The agencies in this list approach that problem from different directions. iPullRank brings deep information-retrieval thinking. Go Fish Digital and Siege Media combine owned content with wider authority signals. Blue Array integrates GEO with established organic search. Omniscient and Directive bring strong B2B and buyer-intent perspectives. WebFX offers enterprise-scale delivery, while Netsleek focuses specifically on AI Search visibility, persona context, entity understanding and the conditions surrounding selection and recommendation.

As AI-driven discovery continues to develop, the most useful question for brands may no longer be simply:

Are we visible?

It may be:

When does our brand become one of the options an AI system chooses to surface, for which audiences, and why?