AI search visibility is not created by adding a few keywords to a website or publishing isolated articles. It depends on whether search engines and AI systems can access, understand, verify and confidently recommend a brand in response to relevant user questions.
In this guide, you will learn how the funkyMEDIA AI Search process works—from the initial visibility audit and competitor analysis to implementation, monitoring and continuous improvement. You will also understand what affects the time needed to achieve results, which metrics matter and why AI Search requires a broader approach than conventional search engine optimisation alone.
What Is an AI Search Process?
An AI Search process is a structured method of improving how a brand is discovered, interpreted, cited and recommended by AI-powered search systems.
These systems include:
- ChatGPT Search
- Google AI Overviews
- Google AI Mode
- Microsoft Copilot
- Bing generative search
- Perplexity
- Gemini
- Claude and other AI assistants with web access
- Industry-specific AI recommendation and research tools
Traditional SEO primarily aims to improve the position of a webpage for a particular search query. AI Search goes further. It considers whether an AI system can identify the company as an entity, connect it with the correct services and markets, verify its claims through reliable sources and use its content when constructing an answer.
The goal is not simply to rank a page. The goal is to make the brand understandable, retrievable and recommendable.
At funkyMEDIA, we treat AI visibility as the result of several connected elements:
- technical accessibility,
- semantic clarity,
- topic coverage,
- brand entity consistency,
- source authority,
- third-party mentions,
- content quality,
- reputation signals,
- structured business information,
- and continuous measurement.
AI Search does not replace SEO. It extends SEO into a broader environment in which the answer may be generated before the user visits a website.
The Six Stages of the funkyMEDIA AI Search Process
Our process consists of six connected stages:
- AI Visibility Audit
- Competitor and Market Analysis
- Brand Entity and Source Analysis
- AI Search Strategy
- Implementation
- Monitoring and Continuous Improvement
Each stage answers a different question. Together, they create a complete framework for increasing a brand’s presence across traditional search results and AI-generated answers.
1. AI Visibility Audit
The AI Visibility Audit establishes the starting point.
Before recommending changes, we need to understand how the brand is currently presented across its website, search results, external sources and AI platforms. We examine not only whether the company appears, but also how accurately and consistently it is described.
What We Audit
The audit may include:
- brand visibility in AI-generated answers,
- citations and source references,
- answers to branded and non-branded prompts,
- visibility across different stages of the customer journey,
- technical crawlability,
- indexation and rendering,
- robots.txt directives,
- access for relevant search and AI crawlers,
- information architecture,
- internal linking,
- structured data,
- content quality and depth,
- author and expert information,
- service and product descriptions,
- company data consistency,
- reputation signals,
- third-party mentions,
- and the conversion path after an AI referral.
We also check whether important information is available in HTML and can be understood without relying entirely on visual design, scripts or images.
Prompt-Based Visibility Testing
AI visibility cannot be evaluated using only one direct question such as:
“What is funkyMEDIA?”
Users approach AI systems with much broader prompts. They ask for comparisons, recommendations, explanations, alternatives, costs and risk assessments.
A useful audit therefore tests several query groups:
- branded questions,
- category questions,
- problem-based questions,
- comparison prompts,
- recommendation prompts,
- purchase-intent prompts,
- local or geographic prompts,
- reputation and trust prompts,
- and follow-up questions.
For example, a company may be visible when its name is entered directly but completely absent from answers such as “Which agencies specialise in AI Search for B2B brands?” This difference reveals whether the system merely recognises the name or genuinely associates the brand with a commercial category.
What the Audit Produces
The audit creates a documented baseline that may include:
- current AI visibility,
- citation frequency,
- brand inclusion rate,
- answer accuracy,
- entity consistency,
- content gaps,
- technical barriers,
- source gaps,
- competitor advantages,
- and recommended priorities.
Without this baseline, later changes cannot be evaluated reliably.
2. Competitor and Market Analysis
AI Search competitors are not always the same companies that rank next to you in Google.
An AI system may cite a trade publication, marketplace, comparison website, research report, directory, forum or independent expert instead of a direct commercial competitor. These sources influence how the market is understood and which brands are considered credible.
Identifying the Real AI Search Competition
We analyse several types of competitors:
- direct business competitors,
- organic search competitors,
- frequently cited domains,
- publishers that define the category,
- directories and comparison platforms,
- recognised experts,
- organisations producing original data,
- and brands repeatedly recommended by AI systems.
This wider perspective helps us identify who controls the information environment around the topic.
What We Compare
Competitor analysis can cover:
- topics and questions addressed,
- depth of content,
- page and site structure,
- authority of authors,
- original research,
- case studies,
- definitions and explanatory resources,
- service positioning,
- customer reviews,
- external mentions,
- citation sources,
- structured data,
- brand consistency,
- and the clarity of relationships between services, people and organisations.
We also look for areas in which competitors are repeatedly described using specific attributes. If several reliable sources connect a competitor with a certain specialisation, AI systems may reproduce that association.
Query Fan-Out and Market Coverage
AI systems may expand one complex question into multiple related searches. A user asking for the best solution may indirectly trigger research into:
- available options,
- technical requirements,
- prices,
- risks,
- use cases,
- customer opinions,
- implementation time,
- alternatives,
- and provider credibility.
This means a brand does not need only one page targeting the original query. It needs a connected body of evidence covering the questions that support the final answer.
Competitor and market analysis reveals which parts of this wider query network are already occupied and where the brand can build a defensible advantage.
3. Brand Entity and Source Analysis
A brand entity is the identifiable concept that represents a company, organisation, product, service or person.
For an AI system to recommend a business correctly, it should be able to distinguish that business from similarly named entities and understand its most important attributes and relationships.
What an AI System Should Understand
Depending on the organisation, the system may need to determine:
- the official brand name,
- what the company does,
- which services it provides,
- who founded or manages it,
- where it operates,
- which markets it serves,
- what expertise it has,
- which products or methods belong to it,
- how it differs from competitors,
- and which external sources confirm these facts.
If these details are missing or contradictory, the brand may be misclassified, confused with another company or omitted from generated recommendations.
First-Party and Third-Party Sources
We divide the brand’s information environment into two main categories.
First-party sources are controlled by the brand:
- the official website,
- service pages,
- about pages,
- expert profiles,
- case studies,
- research,
- documentation,
- reports,
- product feeds,
- and official social profiles.
Third-party sources are published independently:
- editorial articles,
- industry publications,
- interviews,
- directories,
- reviews,
- conference profiles,
- podcasts,
- databases,
- partner websites,
- and professional associations.
First-party content explains the brand’s preferred position. Third-party content can corroborate that position.
A strong AI Search strategy usually requires both.
Entity Consistency
We check whether essential information remains consistent across sources, including:
- company name,
- business category,
- descriptions,
- addresses,
- service areas,
- founding information,
- key people,
- author biographies,
- contact details,
- and areas of expertise.
Consistency does not mean repeating one identical paragraph everywhere. It means maintaining the same underlying facts while adapting the presentation to each source and audience.
Source Quality Matters More Than Mention Volume
Not every mention has the same value.
A small number of relevant, credible and context-rich references can be more useful than hundreds of generic listings. A valuable mention clearly connects the brand with a topic, service, market or verifiable achievement.
We therefore assess:
- relevance,
- editorial quality,
- independence,
- topical authority,
- factual accuracy,
- contextual depth,
- and accessibility to search and AI systems.
AI Search is not a contest to produce the largest possible number of brand mentions. It is a process of building a coherent and credible source environment.
4. AI Search Strategy
After the audit and analysis stages, we translate the findings into a prioritised strategy.
The strategy connects business objectives with technical improvements, content development, entity building, digital PR and measurement.
Defining the Business Goal
Different organisations require different AI Search outcomes.
A company may want to:
- appear in provider recommendations,
- increase product discovery,
- establish a new market category,
- improve brand recognition,
- correct inaccurate AI answers,
- generate qualified leads,
- support international expansion,
- build expert authority,
- or increase citations of its research.
The strategy must define what meaningful visibility looks like before content or technical work begins.
Mapping the Customer Journey
Users may interact with AI systems at several stages:
Awareness
- What is this problem?
- What causes it?
- What solutions exist?
Consideration
- Which approach is best?
- What are the differences?
- What should I look for in a provider?
Decision
- Which company should I choose?
- How much does the service cost?
- How long will implementation take?
- Is this brand credible?
Validation
- What do customers say?
- Does the company have relevant experience?
- Are its claims confirmed elsewhere?
- Are there better alternatives?
Our strategy covers the entire path rather than concentrating only on high-volume keywords.
Building a Topic and Entity Architecture
We organise content around connected topic clusters.
A typical architecture includes:
- a central pillar page,
- supporting guides,
- service pages,
- definitions,
- comparison pages,
- case studies,
- original research,
- expert commentary,
- FAQs,
- author profiles,
- and external source development.
Internal links show how these resources relate to one another. They also help users and search systems move from broad concepts to specialised information.
The result is a knowledge structure, not a collection of isolated articles.
Prioritising Actions
Actions are normally divided into:
- technical priorities,
- content priorities,
- entity priorities,
- source and reputation priorities,
- measurement priorities,
- and conversion priorities.
We assess each action according to its potential impact, urgency, required effort and dependency on other tasks.
A technically inaccessible page, for example, should usually be fixed before investing in additional content for that page.
5. Implementation
Implementation turns the strategy into visible, accessible and verifiable brand assets.
Depending on the project, funkyMEDIA may implement the work directly, cooperate with the client’s team or provide detailed recommendations for developers, editors and PR specialists.
Technical Implementation
Technical improvements may include:
- correcting crawl and indexation barriers,
- reviewing robots.txt rules,
- allowing relevant search crawlers,
- improving server responses,
- strengthening page rendering,
- correcting canonicalisation,
- improving navigation,
- creating logical URL structures,
- adding or refining structured data,
- improving accessibility,
- updating sitemaps,
- and ensuring important information is present in accessible page content.
There is no universal technical file or special markup that guarantees AI visibility. The purpose of technical implementation is to make high-quality information accessible, unambiguous and eligible for retrieval.
Content Implementation
Content work may involve:
- creating pillar pages,
- expanding service pages,
- answering missing customer questions,
- adding clear definitions,
- developing comparison content,
- publishing expert commentary,
- creating original research,
- adding case studies,
- improving author biographies,
- documenting processes,
- and updating outdated or unsupported claims.
Content must provide information that is useful beyond a generic summary. Practical experience, original examples, expert interpretation and proprietary data create stronger reasons for a source to be selected and cited.
Brand Entity Implementation
Entity improvements may include:
- creating a complete company page,
- clarifying the relationship between the brand and its services,
- improving founder and expert profiles,
- standardising essential company information,
- connecting relevant organisational profiles,
- documenting credentials and experience,
- and correcting inconsistent descriptions.
Structured data can support this work, but it cannot replace visible, accurate and useful content.
Source and Brand Mention Development
Where necessary, implementation also extends beyond the company website.
This may include:
- digital PR,
- expert articles,
- interviews,
- research distribution,
- industry commentary,
- podcast appearances,
- relevant directory profiles,
- partner references,
- and publication of original datasets or reports.
The objective is to create genuine, useful evidence of the brand’s relevance. Artificial mentions, low-quality placements and mass-produced profiles can create noise without building meaningful authority.
Conversion Implementation
AI visibility is valuable only when it supports a business outcome.
We therefore review what happens after a user reaches the website:
- Is the service immediately understandable?
- Can the visitor verify the claims made in the AI answer?
- Is there a clear next step?
- Are trust signals visible?
- Is the page appropriate for the user’s stage of the journey?
- Can AI referral traffic be measured?
- Are forms and contact options working correctly?
AI-generated answers may give users substantial context before the visit. Landing pages should therefore help them validate and act, rather than forcing them to repeat their research.
6. Monitoring and Continuous Improvement
AI-generated answers are dynamic.
Results can vary according to the platform, model, location, language, query wording, search context and available sources. A brand’s visibility should therefore be measured as a pattern rather than judged from a single prompt.
What We Monitor
Depending on the strategy, we may monitor:
- brand inclusion rate,
- citation frequency,
- cited pages,
- accuracy of brand descriptions,
- recommendation context,
- share of voice,
- competitor inclusion,
- sentiment and positioning,
- visibility by prompt category,
- AI referral traffic,
- assisted conversions,
- organic search performance,
- crawler activity,
- and changes in the external source environment.
Why Repeatable Prompt Sets Matter
Random testing can create misleading conclusions.
We use defined prompt groups that represent actual customer journeys. The same themes are tested periodically across relevant systems, while allowing for natural variations in wording and follow-up questions.
This makes it possible to identify:
- visibility trends,
- new citations,
- disappearing sources,
- inaccurate answers,
- competitor movement,
- and gaps that require additional evidence.
From Monitoring to Improvement
Monitoring is not a reporting exercise alone. Findings should lead to action.
For example:
- an inaccurate answer may require clearer entity information,
- weak visibility for comparison prompts may indicate a missing comparison resource,
- reliance on one citation may reveal a source diversity problem,
- high visibility but low traffic may require stronger brand recognition,
- traffic without conversions may indicate a landing-page mismatch,
- and declining inclusion may require content updates or new external validation.
AI Search strategy develops continuously because markets, sources and answer systems continue to change.
When Can You Expect Results?
There is no single guaranteed timeline for AI Search visibility.
Some technical and entity changes can be detected relatively quickly after pages are recrawled. Broader improvements in recommendations, source diversity and brand authority usually require more time.
A practical timeline may look like this:
First 30 Days
Typical activity includes:
- baseline measurement,
- technical diagnosis,
- prompt testing,
- competitor analysis,
- entity mapping,
- source analysis,
- and strategic prioritisation.
Early changes may appear after technical barriers or major factual inconsistencies are corrected.
30 to 90 Days
This period may include:
- implementation of priority fixes,
- publication of core pages,
- internal-linking improvements,
- entity clarification,
- content expansion,
- and initial source development.
The brand may begin appearing for a wider range of informational and category-level questions.
Three to Six Months
At this stage, a consistent programme can begin producing broader effects:
- improved topic coverage,
- more relevant citations,
- increased inclusion in recommendation prompts,
- stronger associations with key services,
- and measurable AI referral or assisted traffic.
Six to Twelve Months
Competitive markets, new brands and projects requiring substantial external authority may need six to twelve months or longer.
Long-term progress can include:
- higher share of voice,
- increased source diversity,
- stronger recommendation consistency,
- improved branded demand,
- and greater resilience when AI platforms change their retrieval or answer systems.
Factors That Affect the Timeline
Results depend on:
- the current authority of the brand,
- website accessibility,
- market competition,
- existing content quality,
- source availability,
- the severity of entity inconsistencies,
- implementation speed,
- access to experts and original data,
- publication frequency,
- and the credibility of third-party validation.
AI Search should not be sold as an instant ranking mechanism. It is the development of an information and reputation environment that makes a brand easier to understand and safer to recommend.
AI Search Numbers and Statistics
The following figures illustrate why AI accessibility and AI-originated customer journeys deserve separate attention.
An analysis covering 858,457 websites found that sites crawled by AI systems recorded, on average:
- 527.7 visits, compared with 164.9 visits for sites without observed AI crawling,
- 4.17 form submissions, compared with 1.57,
- and 8.62 click-to-call actions, compared with 3.46.
Among websites generating more than 10,000 sessions, the observed AI crawler rate reached 90.5%.
The same research period recorded an increase in LLM referral visits from 93,484 to 161,469, representing growth of approximately 72.7%.
Platform-level changes included:
- ChatGPT referrals increasing from 81,652 to 136,095, or approximately 66.7%,
- Claude referrals increasing from 106 to 2,488,
- Copilot referrals increasing from 22 to 9,560,
- and Perplexity referrals increasing from 11,533 to 13,157, or approximately 14.1%.
These figures show correlation rather than proof that crawler access alone causes higher traffic or conversion levels. Larger, more established and more active websites may naturally attract both more crawler activity and more users.
The practical conclusion is not that allowing a crawler guarantees growth. It is that technical accessibility, measurable AI referrals and strong website performance increasingly belong in the same visibility strategy.
AI Search, SEO and Digital PR: A Decision Table
| Business situation | Primary requirement | Recommended starting point | Supporting activities | Main success measure |
|---|---|---|---|---|
| The brand is not visible in AI answers | Establish the baseline and identify barriers | AI Visibility Audit | Technical review and prompt testing | Brand inclusion rate |
| AI systems describe the company incorrectly | Clarify the brand entity | Brand Entity and Source Analysis | Company-page updates and data correction | Answer accuracy |
| Competitors are recommended more often | Identify their source and topic advantages | Competitor and Market Analysis | Content-gap and citation analysis | AI share of voice |
| The website ranks but is rarely cited | Improve retrievability and source value | Content and source audit | Original insights, clearer answers and expert content | Citation frequency |
| The brand is visible only for its own name | Build category and problem associations | AI Search Strategy | Topic clusters and external mentions | Non-branded visibility |
| The website has strong content but weak authority | Strengthen independent validation | Source and Brand Mention Development | Digital PR, research and expert commentary | Relevant source diversity |
| AI traffic is growing but leads are not | Improve the post-answer customer journey | Conversion Implementation | Landing pages, trust signals and calls to action | Conversion rate |
| Results fluctuate between tests | Establish repeatable measurement | Monitoring Framework | Defined prompt sets and scheduled testing | Visibility trend |
| The company operates in several markets | Separate entities, languages and locations clearly | International Entity Strategy | Local sources and market-specific content | Visibility by market |
| The brand launches a new service or category | Define and substantiate the new association | Topic and Entity Architecture | Pillar content, research and third-party validation | Category association |
Common AI Search Myths
Myth: AI Search Is Just Traditional SEO with a New Name
SEO remains an essential foundation, particularly for systems that retrieve information from search indexes. However, AI Search also requires analysis of generated answers, citations, entity relationships, third-party validation and recommendation contexts.
Myth: Adding an llms.txt File Guarantees Visibility
No individual file guarantees inclusion in AI-generated answers. Different systems use different retrieval methods, and some major search platforms do not require or use llms.txt for their generative search features.
Crawler accessibility, technically sound pages and useful content remain more important than a single experimental file.
Myth: Schema Markup Makes AI Systems Recommend a Brand
Structured data can help clarify information and qualify pages for certain search features. It does not prove that a company is credible, authoritative or the best recommendation.
Markup should accurately support visible content—not replace it.
Myth: More Content Always Produces More AI Visibility
Publishing more generic pages can increase duplication without adding useful evidence.
A smaller collection of original, well-connected and expert-led resources may be more valuable than hundreds of shallow articles.
Myth: Repeating the Brand Name Builds Entity Strength
Recognition depends on meaningful relationships and corroborated facts. Repetition without context does not explain what the brand does, why it is relevant or whether its claims can be trusted.
Myth: One AI Answer Proves That the Strategy Works
Generated answers can vary. Reliable evaluation requires multiple prompts, platforms, user intents and measurement periods.
Myth: AI Referral Traffic Is the Only Important Metric
Many users may discover a company in an AI answer and later visit it through a branded Google search, direct navigation or another channel. Direct referrals matter, but so do assisted conversions, branded demand, citations and recommendation frequency.
Common AI Search Mistakes
The most common strategic and implementation errors include:
- testing only branded prompts,
- treating one generated answer as representative,
- concentrating on keywords without mapping entities,
- publishing generic AI-written content without expert value,
- ignoring third-party sources,
- creating inconsistent company descriptions,
- blocking relevant crawlers unintentionally,
- using structured data that conflicts with visible content,
- building artificial brand mentions,
- measuring visibility without recording citations,
- ignoring the conversion path,
- and expecting immediate, guaranteed results.
Another major mistake is separating technical SEO, content, digital PR and brand strategy into unrelated activities. AI systems may use signals and sources from all of these areas when constructing an answer.
Frequently Asked Questions
What is AI Search optimisation?
AI Search optimisation is the process of improving a brand’s visibility, accuracy, citations and recommendation potential in AI-powered search and answer systems. It combines technical accessibility, SEO, content architecture, entity optimisation, source development, reputation management and measurement.
Is AI Search optimisation the same as GEO or AEO?
The terms overlap.
Generative Engine Optimisation, or GEO, usually focuses on visibility in generative systems. Answer Engine Optimisation, or AEO, focuses on content that can be used to answer questions. AI Search is a broader practical framework covering discovery, retrieval, entity recognition, citations, recommendations and the customer journey.
Does AI Search replace SEO?
No. Search indexes, crawlability, page quality and authority remain important foundations. AI Search expands the scope by examining how information is retrieved, combined, cited and presented in generated answers.
Which AI platforms does funkyMEDIA analyse?
The selection depends on the client’s market and audience. It may include ChatGPT Search, Google AI Overviews, Google AI Mode, Gemini, Microsoft Copilot, Bing, Perplexity and other relevant systems.
Can you guarantee that a brand will be recommended by ChatGPT?
No agency can control or guarantee a specific generated answer. AI systems change, use multiple sources and may produce different results depending on the query and context.
funkyMEDIA can improve the conditions that influence visibility: accessibility, clarity, topic coverage, entity consistency, source authority and external corroboration.
Do we need to rebuild our website?
Not always. Some projects require structural or technical changes, while others need better content, internal linking, entity information or third-party validation. The audit determines the scope.
How much content do we need?
There is no universal word count or number of pages. The required content depends on the complexity of the offer, customer questions, competitive landscape and existing topic coverage.
The objective is complete and useful coverage—not content volume for its own sake.
Are brand mentions important for AI visibility?
Yes, when they are relevant, credible and context-rich. Independent sources can help confirm the relationship between a brand, its expertise, services and market.
Mentions created only to manipulate visibility are unlikely to build sustainable authority.
How do you measure AI visibility?
Measurement may include brand inclusion, citation frequency, source diversity, answer accuracy, share of voice, visibility by prompt category, AI referral traffic, assisted conversions and changes in branded demand.
Why does the same prompt produce different answers?
AI answers can vary because of model updates, personalisation, location, language, query interpretation, search context and changes in available sources. This is why monitoring should use prompt groups and trends instead of isolated screenshots.
Can a small company compete with a large brand?
Yes, particularly in specialised or local topics. A smaller company can build visibility through precise expertise, original information, strong entity clarity, relevant external sources and content that answers specific customer needs better than generic corporate pages.
When should an AI Search programme begin?
The best time is before competitors establish dominant topic and source associations. It is particularly important during a website redesign, market expansion, rebranding, new service launch or decline in traditional organic visibility.
Start with Evidence, Not Assumptions
Effective AI Search work begins by discovering how search and answer systems currently understand the brand.
funkyMEDIA’s process connects technical accessibility, topic authority, brand entities, external sources and user intent into one measurable strategy. We identify what prevents the brand from appearing, determine which competitors and sources shape the market, implement the required improvements and monitor how visibility develops over time.
The outcome is not simply another collection of optimised webpages. It is a structured, verifiable brand presence designed for a search environment in which AI systems increasingly explain, compare and recommend businesses before the first website visit.