AI search visibility: help systems understand and represent your business accurately
First we check where AI systems lack clarity: in the company description, services, experts, facts, sources or SEO foundation. Then we remove the main constraint and build a system you can verify and maintain.
Companies and partners we have worked with
AI systems need consistent, verifiable sources — not slogans
Accurate representation starts with clear facts: who you are, what you offer, whom you help, on what terms and what supports those claims. We connect that information to primary pages, authors, external profiles and the technical accessibility of the site.
How we make decisionsA company can be visible in search yet unclear to AI systems
Conflicting service names, scattered facts, weak source pages and unverified claims raise the risk of an incomplete or inaccurate brand picture. Separate “optimisation for a neural network” does not replace this work.
AI search visibility starts with the main source of uncertainty
We include only changes that make business information clearer, more verifiable and more useful for people and search systems.
SEO foundation and accessibility
We check whether important pages can be crawled and indexed, whether the architecture is clear and whether technical issues cause sources to be lost.
Business entities and facts
We align company, service, product, expert and location names and the key facts that should be understood the same way across all materials.
Source pages
We define where primary information about a service, product, expert or term should live so it can be found and checked.
Questions and useful answers
We connect real customer questions with self-contained answers, evidence and a next step — without mass-producing similar pages.
Expertise and evidence
We add authorship, methodology, documents, case evidence and limitations wherever the business can support every claim.
Monitoring and decisions
We track source health, search visibility, citations and observed answers so we can refresh priorities — not promise control over platforms.
We own the quality of the source system — not a promise of being mentioned
First we check whether the business needs dedicated AI search visibility work. If the problem is better solved through ordinary SEO, the website, content or data, we start there.
We start from the business task
We define which customer decision depends on AI search and which error, loss of trust or uncertainty we need to reduce.
We do not sell a new term as new magic
We treat GEO and LLMO as an extension of SEO, content and fact management — not a way to order a platform’s answer.
Facts beat promo
We separate verifiable information from advertising promises and assign an owner to every significant claim.
Content helps people
An answer should help someone understand the choice, terms and limits — not only be easy for a system to extract.
Expertise is evidenced
We do not simulate experience. Authors, cases, documents and methodology are published only after the business confirms them.
We measure observable signals
We separate indexing, visibility, citation, click-through and commercial outcome, and do not treat correlation as causation.
One fact — one primary source
We remove contradictions between pages, profiles and language versions so the team knows where current information lives.
The system stays with the client
The fact map, rules, access, backlog and change history are handed to the team and do not create vendor lock-in.
Before kick-off you can check facts, gaps and priority logic
We do not replace working artefacts with a promise of appearing in ChatGPT, Google AI Overviews or another AI platform.
Entity and fact map
Shows which information is confirmed, where it is published and where contradictions remain between sources.
Source and gap map
Identifies primary pages, external profiles, missing evidence and areas that need updating.
Backlog with verification criteria
Each task gets a rationale, an owner, a dependency and an observable result after delivery.
AI search visibility depends on the decisions your customer makes
B2B, local business, a store or a clinic need different facts, evidence and sources. We adapt the system to the real choice path — not a universal template.
B2B
We help explain a complex service, project roles, methodology and evidence that several decision-makers need.
Local business
We align services, addresses, service areas, hours, specialists and booking terms so data matches on the website and local sources.
Improve local visibility
E-commerce
We structure categories, products, attributes, availability and purchase terms so information stays accurate across the catalogue.
Strengthen store visibility
Real estate
We separate properties, locations, service types, terms and limits so answers do not mix different offers.
Strengthen trust in the offer
Clinics
We connect services, specialists, evidence-based information and local data without unverified medical promises.
Personal brands
We gather biography, expertise, publications, talks and competence boundaries into a clear source system.
Strengthen the expert profile
Multilingual business
We separate languages, markets, local facts and offer versions so different pages do not contradict each other.
Clear rules for facts, content and technical changes
Before kick-off we fix the goal, sources, fact owners, access, publication boundaries and verification criteria.
The business confirms the facts
The company approves information about services, products, experts, terms, limitations and legally significant wording.
Access on a need-to-know basis
We use only the rights needed for the website, CMS, Search Console, analytics and internal documentation.
Delivery boundaries are defined
Content, schema, development, translations and legal review have separate owners and are not merged automatically.
Verification is not a guarantee
We control delivered changes and available signals, but we do not promise source selection or a specific platform answer.
How AI search visibility work begins
Five steps from the business task to changes you can implement, verify and maintain.
01
Understand the business task
We clarify which customer decisions depend on search and where incomplete or incorrect information creates risk.
02
Check the foundation and sources
We review site accessibility, key pages, entities, facts, authorship, markup and external consistency.
03
Find the main constraint
We identify what blocks progress most: technical accessibility, ambiguous facts, weak evidence or content gaps.
04
Deliver the priority
We form a minimum sufficient backlog, assign owners and support publication and technical changes.
05
Measure and review
We check sources, visibility, citation, click-through and representation errors, then set the next cycle.
First we will find where AI systems lack clarity about your business
Describe the company, key services or products, languages and current materials. We will check whether AI search visibility is the right first step and what to fix first.
Questions about AI search visibility
What are AI SEO, GEO and LLMO?
They are different names for work on visibility in search and generative answers. In practice the foundation stays the same: an accessible site, clear entities, verifiable facts, useful content and consistent sources. GEO means Generative Engine Optimization; LLMO means optimisation for scenarios where language models process information. Generative AI optimisation for Google remains SEO: there is no special required schema, and a separate llms.txt is not needed for Google.
Do you guarantee a mention in ChatGPT or Google AI Overviews?
No. The platform chooses sources and forms the answer. We can improve accessibility, clarity and evidential quality of information, but we do not control a specific citation, wording or display frequency — including ChatGPT, AI Overviews, AI Mode, Gemini or similar surfaces.
Does AI search visibility replace ordinary SEO?
No. If a page is not indexed, poorly structured or fails to meet real demand, special wording for AI will not fix the problem. A strong SEO foundation comes first.
Do we need separate pages for every question or an llms.txt file?
There is no universal requirement. A new page is justified only when it helps people and has a standalone role. For Google, a separate llms.txt or special AI markup is not required; accessible HTML, useful content and correct indexing matter more.
How is AI search visibility measured?
We use a set of observable signals: source indexability, visibility on relevant topics, brand citation, fact accuracy, click-through from AI systems and enquiry quality. No single metric alone proves commercial impact.
What remains with the company after the work?
An entity and fact map, a list of primary sources, publishing rules, a backlog, change history and monitoring criteria. The team should understand the system and maintain it without depending on a vendor.