What is AI visibility?
It is the degree to which public sources allow AI-assisted systems to discover, understand, and represent an organisation accurately.
AI discovery / Edinburgh
AI visibility for Edinburgh organisations
AI visibility describes whether an organisation can be discovered, understood, and represented accurately in AI-assisted research and search. It is not another name for rankings. mccaigs assesses public source signals, entity consistency, service clarity, structured knowledge, and representative prompts, then separates monitoring findings from implementation work.
Measure understanding, not just mentions
A useful review asks whether the brand is identified correctly, whether its services and geography are clear, which public sources support the description, and where systems return incomplete or inaccurate answers. A mention without the correct context is not necessarily useful visibility.
Prompt checks are snapshots. Results can vary by platform, model, location, account, and date. Monitoring records what was observed and connects each gap to source material that can be improved.
Monitoring and implementation are different
Monitoring provides a baseline and shows changes over time. Implementation improves the owned sources through clearer pages, entity relationships, technical SEO, structured data, citations, internal links, and public knowledge.
mccaigs reports the limits of the evidence. It does not imply control over ChatGPT, Claude, Gemini, Perplexity, Google, Bing, Copilot, or another external system.
How mccaigs approaches the work
A controlled route from diagnosis to working implementation.
- 01
Define representative discovery questions and record the date and platform tested.
- 02
Review brand, founder, services, geography, and organisation consistency.
- 03
Trace observed answers back to available public sources.
- 04
Prioritise owned-source improvements separately from monitoring.
- 05
Repeat tests carefully and report variance and uncertainty.
Typical deliverables
What the engagement can produce
- AI visibility baseline
- Entity and source consistency review
- Prompt observation record
- Owned-source improvement plan
- Technical implementation scope
- Measured follow-up report
Provider fit
Who this is for, and when to choose differently
A good fit
- Organisations already investing in clear public information
- Teams concerned about inaccurate or incomplete AI-assisted descriptions
- Businesses that want evidence before commissioning wider implementation
When mccaigs may not be the right provider
- Monitoring cannot prove how every user or platform will see a business.
- mccaigs cannot guarantee a recommendation, citation, mention, ranking, or inclusion.
Verifiable proof
Evidence already published by mccaigs
Related routes
Continue with the closest service or guide
AI Visibility Edinburgh FAQ
Useful answers before the first conversation.
Is AI visibility the same as search ranking?
No. Rankings describe ordered search results. AI visibility also considers descriptions, citations, entity accuracy, service understanding, and appearance across varied answer experiences.
How do you assess AI visibility?
mccaigs reviews owned sources, entity consistency, technical accessibility, structured knowledge, and a dated set of representative prompt observations.
Why can prompt results vary?
Platforms use different sources, models, personalisation, locations, and update cycles. A test is a dated observation rather than a universal result.
What is the approved starting price?
AI Visibility Management currently starts from £99 per month. Wider implementation is priced following discovery.
Can mccaigs guarantee rankings or inclusion in AI answers?
No. Search and answer systems decide what they index, rank, cite, or recommend. mccaigs can improve the accuracy, structure, crawlability, and usefulness of the source material, but cannot control an external platform.
Start a conversation
Bring us the part that should work better.
A useful first conversation can start with a messy workflow, a website that is not pulling its weight, or an AI opportunity that needs a practical route forward.