Be understood by AI search, not just ranked.

Business value

AI search optimisation addresses a specific problem: your business may be described incompletely or inaccurately when buyers use AI assistants and AI-powered search. Scattered service details, outdated profiles and vague page copy leave less reliable information for people and retrieval systems to work with. I focus on making your public information clear, consistent and accessible, rather than treating an AI mention as a business result in itself.

The commercial value comes from reducing ambiguity. A page that explains who a service suits, what it includes and where its limits lie can help a prospective buyer assess fit. Direct answers supported by factual detail also give search systems clearer source material. These improvements may support discovery and better-informed enquiries, but they do not control which sources an assistant selects.

This service builds on SEO rather than replacing it. Google's guidance on AI features says that existing SEO best practices remain relevant to AI Overviews and AI Mode. There is no special schema or AI text file required for inclusion. I therefore prioritise useful content and technical access over speculative shortcuts.

For a hypothetical software consultancy, an assistant might describe the firm as a software vendor because its homepage says “business solutions” while its implementation services are buried elsewhere. I would clarify the service model, supported platforms and delivery boundaries on relevant pages and reconcile conflicting profiles. The intended benefit is a more accurate public description—not a promised recommendation.

How I deliver: inputs, actions and outputs

I start with your website, priority services, target markets, approved business details and the questions buyers ask before enquiring. I request access to Search Console and existing analytics where available, plus CMS or developer support for implementation. I use anonymised question summaries rather than customer transcripts containing personal or confidential information.

I establish a baseline using an agreed set of branded and non-branded questions across selected assistants and search features. I record the question, date, product, available search settings, response and cited URLs. I distinguish an incorrect description from a missing mention: they are different observations and may require different actions. The output is an AI search review showing factual inconsistencies, content gaps and access issues, with evidence for each finding.

I then create a reference sheet of approved facts: business name, service definitions, audience, locations served and relevant official profiles. I compare these with important website pages and profiles your team controls. I propose corrections and flag third-party inaccuracies separately, since those changes depend on another publisher.

For content, I map important buyer questions to existing pages before proposing new ones. I draft direct answers followed by scope, conditions, supporting evidence and a useful next step. I retain necessary nuance rather than shortening everything into quotable claims. Where structured data is appropriate, I specify markup that matches visible content and validate its implementation. I do not treat valid markup as evidence that an assistant will cite the page.

For technical access, I review robots rules, indexing directives, internal links, rendered text and relevant hosting or CDN restrictions. For Google AI supporting links, I check the documented requirement that pages be indexed and eligible for a search snippet. I explain crawler choices by product and purpose rather than recommending blanket access. Search visibility and permission for other AI uses are not interchangeable decisions.

I hand over a prioritised change list with affected URLs, proposed edits, rationale, owners and acceptance checks. I agree which CMS changes I will make and which require developers. Your subject experts approve factual claims; editors receive content guidance; developers receive implementation notes. I re-check agreed changes after publication and provide periodic monitoring notes. Broader technical repairs or substantial content production are scoped separately when needed.

How success is measured and where the boundaries are

I agree measures before implementation across three levels: completed corrections, observed search representation and business activity. I check whether approved facts are consistent, priority pages are accessible and content answers the agreed questions. I then repeat the baseline questions and record description accuracy, mentions and citations as separate observations—not a universal AI ranking.

I review identifiable assistant referrals and relevant enquiries where measurement allows. Google's AI feature traffic is included in Search Console's overall Web reporting, so I do not present that total as an isolated AI channel. Missing referral data, changing responses and small samples limit attribution. Before-and-after changes can indicate a pattern, but cannot by themselves prove causation.

Any tracking changes I propose respect consent choices. I keep names, email addresses, phone numbers and free-text enquiry contents out of analytics events. I document measurement gaps rather than filling them with assumptions.

I cannot guarantee citations, rankings, accurate assistant responses or enquiries. Platforms choose their sources, and information outside your control may persist. The deliverables remain concrete: an evidence-led review, prioritised content and schema fixes, access recommendations and monitoring notes that guide the next decision.