A homeowner sees an F28 fault on a combi boiler and asks an AI assistant what it means, whether the situation is safe, and which local professional can inspect the appliance. The answer may combine general troubleshooting language with a business recommendation, a rough cost description, and claims about brand experience.
Each part can be wrong if the assistant finds an old directory record, an unclear service page, or an unsupported review summary. For a gas engineering business, AI visibility is therefore not just a matter of being named.
The more important goal is to be represented with the correct legal credentials, work categories, service area, availability, pricing context, and manufacturer expertise. High-risk prompts also require a clear boundary between general information and actions that should be handled through official emergency guidance or by a suitably registered engineer.
This guide shows how to map real prompt journeys, publish source-eligible facts, correct material errors, and measure inclusion, accuracy, citation, and referred behavior without implying that any markup or content format can force an AI system to recommend the firm.