The AI search playbook for local and service brands.

Local buyers ask AI assistants conversational questions, often with constraints a traditional keyword list misses: budget, urgency, opening hours, accessibility, specialisms, "near me" or a named neighbourhood.

Build a prompt set of 30 to 60 questions across three stages:

• Discovery — "Who are the best family dentists in Bristol?"
• Comparison — "Is Smile Studio or Clifton Dental better for nervous patients?"
• Decision — "Does Smile Studio offer weekend appointments and accept new NHS patients?"

Use the client's sales team, call recordings, enquiry forms and reviews as sources. The way customers describe their problem is usually the way they ask AI about it.

Run the prompt set across the answer engines that matter in the market — typically ChatGPT, Google AI Overviews, Gemini, Perplexity and Microsoft Copilot — and capture each answer with the date.

For every prompt, note whether the client appears, which competitors appear, how each business is described and which sources are cited. This is the evidence everything else is measured against, so store the raw answers, not just a score.

Local AI answers draw heavily on business listings and directories. If the business name, address, phone number, hours, services or categories differ between the website, Google Business Profile, Apple Business Connect, Bing Places, industry directories and review sites, engines have a harder time trusting any of it.

1. 01List every place the business appears online, starting with the major map and listing platforms.
2. 02Pick one canonical version of each fact: legal and trading name, address format, phone number, hours, primary category.
3. 03Update every listing to match, and close or merge duplicates.
4. 04Add LocalBusiness (or the most specific subtype) structured data to the website stating the same facts.
5. 05Set a quarterly reminder to check them again — listings drift.

A single "Services" page listing everything is a weak match for a specific question. Give each core service its own page, and where the business genuinely serves distinct areas, give each area a page with real local detail — not a copied template with the town name swapped.

Each page should open with a direct answer to the questions buyers ask about that service: what it is, who it suits, typical timelines, what affects price, what is and isn't included, and how to book. Follow with evidence — process, qualifications, photos of real work and relevant reviews.

Reviews are some of the most-read evidence in local answers. Engines use them to judge quality and to match businesses to specific needs — "great with anxious kids", "fixed it the same day", "clear pricing".

• Ask for reviews consistently, on the platforms that matter for the category, and never incentivise them in ways the platform prohibits.
• Encourage specific reviews by asking customers what the service helped them with.
• Reply to reviews, including negative ones, with calm, factual answers.
• Feature relevant reviews on the matching service pages.

Independent mentions confirm that a business is real, active and known for something. For local brands the most useful sources are often close to home: local news, chambers of commerce, community groups, sponsorships, trade associations, supplier and partner pages, and "best of" roundups in the area.

Check which sources the engines cited in your baseline. If the same directory or local publication keeps appearing for competitors, that is a concrete place to start.

Re-run the same prompt set each month. Report on the share of prompts where the client appears, how that compares with named competitors, how the business is described, and which new sources are being cited.

Keep the story simple for the client: where they stand, what changed, why, and what happens next. AI answers vary from run to run, so focus on trends across the full prompt set rather than single answers.