How AI discovers each kind of business: the Daizie framework

Practical companion to AI does not treat every business the same. That post covers the research; this one is the working framework Daizie measures against.

Ask an AI assistant to recommend a bakery, a consultant, and an invoicing tool, and it will reach for three different kinds of evidence to answer. If you run a small business, knowing which evidence your category runs on is the difference between guessing and having a plan.

Here is the framework, category by category.

Local businesses

Bakeries, plumbers, dentists, restaurants, contractors.

AI commonly relies on:

• Google Business Profile and map data
• Consistent name, address, and phone information
• Service-area and location pages
• Customer reviews — and how recent they are
• Local directories, chambers, and trade associations
• Clear hours, services, pricing cues, and availability
• Local news and community mentions
• Structured LocalBusiness schema

The key question: can AI confidently identify this business, where it operates, what it provides, and whether local customers trust it?

Service businesses

Consultants, coaches, agencies, professional services, personal brands.

AI commonly relies on:

• A clear website explaining expertise, audience, services, and fit
• Consistent identity and offer language across the website and profiles
• LinkedIn and other authoritative professional profiles
• Detailed service and methodology pages
• Case studies, testimonials, credentials, and demonstrated outcomes
• Articles, interviews, podcasts, videos, and guest appearances
• Association directories and independent mentions
• Evidence connecting the person, the company, the expertise, and the current offers

The key question: can AI explain who this expert is, whom they help, why they are credible, and when they should be recommended?

Products and SaaS

Software platforms, apps, physical products, marketplaces.

AI commonly relies on:

• Clear product, feature, use-case, and pricing pages
• Documentation, help centers, FAQs, and comparison pages
• Review platforms such as G2, Capterra, or the relevant marketplaces
• Product directories and software databases
• Independent reviews and analyst coverage
• Reddit, forums, communities, and real user discussions
• Integration directories and partner ecosystems
• Product Hunt, GitHub, app stores, or other category-relevant platforms
• Consistent descriptions of audience, differentiation, limitations, and alternatives

The key question: can AI understand what the product does, who it is for, how it differs, and whether credible users or sources support it?

The four layers every business should check

Whatever your category, the assessment is the same four layers, in order:

1. Identity — did AI find the correct business? Same-name confusion is more common than most owners expect, and a wrong-you never counts as visibility.
2. Visibility — does your business appear in the questions real buyers ask, when you are not named in the question?
3. Accuracy — is what AI says about you correct and current? Being described wrongly can cost more than not being described at all.
4. Recommendation fit — does AI understand when to recommend you, and just as important, when not to?

Recipes, not requirements

One honest caveat, and it is the one most tools skip: these are discovery recipes, not universal platform requirements. No business needs every surface on these lists. A study finding that AI often cites a platform does not mean your business belongs there.

What matters is your actual measured gap: which buyer questions you missed, which surfaces those questions are answered from, and what AI got wrong about you. Recommendations should always follow the measurement — which is exactly how Daizie's action plans are built.