Being found in AI search: a practical guide

When a customer asks an AI system for help finding a service, the business question remains familiar: can they discover you, understand what you do and take a useful next step? The answer may appear in a different interface, but your own information still needs to be clear.

An AI search brief should begin with improvements you can inspect. Accurate services, accessible pages, consistent business details and evidence behind claims are concrete work. A promise to make a particular system recommend your company is not something the website team can control.

Start with the information you own

Read the site as someone who has never heard of the business. Can they identify its name, services, location or service area, and contact route? Can they distinguish what you offer from what a case study client sells? Ambiguous identity is a problem for people before any discussion of automation.

Give each important service a useful explanation. Say who it is for, what the work involves, what information a quote requires and where the offer stops. Use language the customer recognises. A list of fashionable capabilities is harder to evaluate than a specific account of the work.

Keep the same facts consistent across the places you control. Record where a phone number, opening time or service description needs updating. When a project changes, update the relevant case and offer pages deliberately. A publishing habit is more useful than a one-time “AI ready” badge.

Understand Google’s stated requirements

Google’s guidance for AI features says existing search fundamentals still apply to AI Overviews and AI Mode. A supporting page must be indexed and eligible for a Search snippet. It does not require special AI files or special structured data, and inclusion is not guaranteed.

That provides a sensible starting point for a brief: check that important pages are accessible, useful and connected through links. It also sets a limit on sales language. A technical change can remove a barrier; it cannot buy a guaranteed answer placement.

Keep those statements specific to Google’s products. Other answer systems have their own behaviour and controls. If a proposal discusses ChatGPT or another service, ask it to identify that platform’s current guidance and distinguish documented requirements from the agency’s own experiment.

Write answers that stand on their own

Collect the questions a customer asks before enquiring. For a dealer, those may concern stock, part exchange or booking. For a local service business, they may concern the area served, preparation or what happens at the first appointment. Answer the question with enough context to be understood separately.

Avoid a page made entirely of disconnected questions. Build a useful main explanation, then use FAQs for uncertainties that remain. A good answer names the service and its conditions without forcing the reader to infer them from a heading somewhere else.

Use examples carefully. A real case can show what was built; an illustrative scenario can explain a decision. Label the latter as an example. Do not turn a possibility into a claim about a client, or turn an outside publication mention into evidence of an increase in enquiries.

Use structured data as a description

Structured data expresses information in a format machines can read. Google’s introduction to structured data explains how it can help interpret page content and enable eligible search appearances. It is not a substitute for the visible explanation.

Ask your developer to keep markup aligned with the page. An address, price, review or author should have a factual basis. Adding a more impressive label behind the scenes does not make it true. Include structured data in the same review process as the content it describes.

An llms.txt file can be discussed as an optional experiment where a clear use exists. It should not become a reason to postpone fixes to the website itself. Ask what system is expected to use it and what evidence will show that the experiment helped.

Measure observations and outcomes separately

A screenshot of one answer records what appeared for one question at one time. Keep that context with it: platform, question, date and any relevant account or location conditions. Repeat a defined set of questions if you are investigating change, and record results that do not favour the business too.

Separately, examine what visitors did on the website. A referral, an enquiry and a completed purchase are different events. Agree which ones the business can reliably observe. If attribution is incomplete, say so rather than assigning every unexplained lead to AI search.

Google reports traffic from its AI Search features within the Search Console Web performance data, rather than providing a simple standalone count of all AI recommendations. That limit is documented in its AI features guide. Keep reports proportionate to the available evidence.

Commission a practical first step

Choose a small set of commercially important pages. Check facts, sources, crawl access, internal links and the route to an enquiry. Make a dated record of changes. This produces something the business can review even when external answer behaviour varies.

For a Sunderland business, the work might begin with an accurate service explanation and local information. For a parts store, it may begin with product identity and fitment sources. Our AI search service starts from those underlying needs rather than a universal recipe.

Questions

Can an agency guarantee an AI recommendation?

No. A business can improve the information and access it controls, but it cannot promise how an external answer system will select or present sources.

Do we need an llms.txt file for Google AI search?

Google says no special AI text file or structured data is needed for its Search AI features. Prioritise accurate, accessible pages and established search fundamentals.

What should a useful report show?

It should identify what changed, what was observed, when and how it was checked, and the enquiry or sales evidence available. Separate a sampled answer from a measured business outcome.