Examples

Case studies: how a before and after works

We have no real client results to show yet, so these are clearly marked example scenarios for four kinds of small business. Real case studies will appear here once real clients agree to them.

The method, in five steps

Every case study follows the same path

  1. 1AskThe same 10 buyer questions, on the main AI assistants.
  2. 2ReadWhat the business site and its public profiles actually say.
  3. 3Find gapsMissing, old or conflicting facts that AI could repeat wrongly.
  4. 4FixPlain-text pages, matching facts and short FAQ answers. You approve every line.
  5. 5Ask againThe same questions again, so the before and after is fair.
Four example scenarios

What it could look like for a business like yours

Each card is invented. The problems are the common kinds we check for. The numbers are examples, not promises.

Illustrative scenario

A neighbourhood clinic

Buyer asks: “Which family clinic near me is open on Saturdays?”

What AI can find

  • Opening hours are only in a photo on the contact page
  • The old phone number is still on 2 directory profiles
  • No page answers what happens at a first visit

What we would fix

  • Put the hours in plain text and add them to the page data
  • Match the phone number on every profile
  • Add a short first-visit FAQ
10502/106/10BeforeAfter 90 days

Example numbers for a made-up business, shown only to explain how a before and after would be recorded. Not a real result.

Illustrative scenario

A small accounting firm

Buyer asks: “Which accountant works with small shops?”

What AI can find

  • The services page never says who the firm works for
  • Fee ranges differ on 2 directories
  • No page answers when to start tax prep

What we would fix

  • Name the clients you serve in the first line of the services page
  • Agree one fee statement and use it everywhere
  • Add a short FAQ on deadlines and documents
10501/105/10BeforeAfter 90 days

Example numbers for a made-up business, shown only to explain how a before and after would be recorded. Not a real result.

Illustrative scenario

A family restaurant

Buyer asks: “Where can I find a good vegetarian dinner nearby?”

What AI can find

  • The menu is a photo, so the dishes cannot be read
  • The closing day is wrong on 2 profiles
  • No answer about group bookings

What we would fix

  • Publish the menu as text with the vegetarian dishes marked
  • Correct the closing day everywhere
  • Add a booking FAQ
10502/106/10BeforeAfter 90 days

Example numbers for a made-up business, shown only to explain how a before and after would be recorded. Not a real result.

Illustrative scenario

A local manufacturer

Buyer asks: “Who makes custom steel brackets near me?”

What AI can find

  • The capabilities page has no list of materials or sizes
  • The address differs on 2 directories
  • No answer about minimum order size

What we would fix

  • List materials, sizes and lead times as text
  • Make the address identical everywhere
  • Add a minimum-order FAQ
10501/105/10BeforeAfter 90 days

Example numbers for a made-up business, shown only to explain how a before and after would be recorded. Not a real result.

Sourced facts

What published research says

Real figures from published studies, with the source and year. They describe the market, not results we promise for any client.

25%

of US adults surveyed said they regularly use AI chatbots, and 15% regularly use AI search engines.

Claneo State of Search (2,000 US and German respondents), 2025Read the source →
12.5%

of 1,500 US respondents used ChatGPT for search in August 2025, up from 4.1% in February 2025.

Higher Visibility survey, via Search Engine Land, 2025Read the source →
-25%

Gartner forecast that traditional search engine volume would fall 25% by 2026 as people move to AI chatbots. It is a forecast, not a measured result.

Gartner press release, 2024Read the source →

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