Homeowners increasingly ask an assistant before they open Google. If the answer never names your client, the ranking underneath it stops mattering.
Content and structured data built so a model can lift a clean, quotable answer about your client, plus placement of that content where models go looking.
Direct question-and-answer structures, specific numbers and named sources instead of adjectives.
LocalBusiness, Service, FAQ and Person schema that validates, because you are writing for a parser.
Placement across the publications, forums and community sources that feed training and retrieval.
A fixed prompt set tracked monthly, so this stays accountable rather than theoretical.
We map the questions a homeowner in your area would ask an assistant, and record who gets named today.
Entity data, schema and site structure so a model can parse the business cleanly.
Quotable content on the client site plus placement across the sources models retrieve from.
Re-run the prompt set monthly and work the gaps.
Tell us the trade, the service areas and what you have already tried. We'll come back with a scoped approach and a price, or tell you to spend the money elsewhere first.
There is heavy overlap and some real differences. The target changes from a ranking position to a citation inside an answer, which puts more weight on structured data, clean semantics and third-party corroboration, and less on classic ranking factors.
ChatGPT, Perplexity, Gemini and Google AI Overviews. We log the same prompt set across all four monthly.
For a contractor with no rankings and a broken site, yes. Fix the fundamentals first. For a brand already ranking well, this is where the next share of attention goes, and the groundwork takes months.