Who is it for?
Enterprise brands
Organisations that are represented with inaccurate, incomplete or outdated information and need a reliable narrative.
Expertise-led companies
Service businesses that want to be understood, mentioned and recommended for a particular category or problem.
Organisations using AI
Companies that need governance for employee tools, data sharing, model risk and approval processes.
Executive teams
Leaders who want to turn AI investment from a list of possibilities into a framework for risk, ownership and measurement.
Two connected problems
External visibility: How do AI systems describe the brand, which sources do they use, and for which questions do they mention or exclude it?
Internal governance: Which AI tools may be used with which data, who owns decisions, what risks are measured, and which use cases require approval?
Handled separately, visibility becomes a marketing campaign and governance becomes a policy document on a shelf. The service connects both through measurement and operating cadence.
Standard deliverables
- AI Visibility Baseline: mention, citation, recommendation and narrative-accuracy measurement across a target question set.
- Entity & Source Audit: consistency of brand, people, services, category relationships and supporting sources.
- Technical GEO Audit: crawlability, canonical, hreflang, sitemap, robots.txt, JSON-LD and machine-readable summaries.
- Content & Evidence Plan: service pages, Q&A content, expert viewpoints, cases and evidence gaps.
- AI Governance Framework: tool inventory, use principles, data classes, risk levels, approvals and accountability.
- Monthly Visibility Report: change across repeated prompts, new risks, opportunities and actions.
Working model
Baseline
Target questions, engines and current visibility are measured.
Visibility BaselineEntity Audit
Brand, people, services and category relationships are clarified.
Entity MapTechnical
Schema, canonical, robots, sitemap and crawl structure are corrected.
Technical BacklogContent
Direct answers, service content and evidence structures are developed.
Content PlanAuthority & Measure
Source authority is strengthened and the question set is measured again.
Measurement ReportRepeat
Monthly measurement and quarterly strategy updates continue.
Improvement CycleIncluded and excluded
Included
- AI visibility and narrative baseline
- Entity and technical audit
- Schema, canonical and content backlog
- Measurement method and reporting
- AI-use principles and risk model
- Monthly actions and quarterly strategy
Not included
- Guarantees of a specific AI answer or ranking
- Fabricated sources, reviews or backlinks
- Unlimited content production
- Legal compliance opinion or certification
- Model development and MLOps unless scoped
- Control over third-party platforms
Measurement cadence
Frequently asked questions
What is AI visibility?
The degree to which a brand, person or service is represented accurately, currently and in the right context in AI answers. Mention, citation, recommendation and narrative accuracy are assessed together.
How is GEO different from SEO?
SEO focuses on ranking and clicks. GEO addresses whether AI answers understand, mention, cite or recommend a brand correctly through entity clarity, content, technical structure and third-party authority.
Can AI visibility be guaranteed?
No. These systems are variable and probabilistic. The objective is to establish a measurable baseline, reduce inaccurate representation and systematically improve the probability of visibility.
Does llms.txt create visibility on its own?
No. It may provide a machine-readable summary for some consumers, but it is not a universal ranking factor. It belongs alongside structured data, clear content, crawlability and source authority.