How Structured Data Helps AI: The Approach We Use at Keboost

Structured data helps AI by making facts about a business easy to read and hard to misread. At Keboost we treat it as one supporting layer, not the main event. Our approach starts with what AI assistants already say about a brand, fixes the facts at the source, and then uses clear page structure and schema to support content that answers real buyer questions.

Why we do not start with schema

Many people expect AI visibility work to begin with code. In our experience, the bigger problems are usually elsewhere: a brand is simply not described clearly anywhere, its facts conflict across profiles, or it has no page answering the questions buyers ask. Adding markup to a weak page does not make it useful. So structured data comes after we know what is missing.

Our order of work

1. Look at the prompts. We track questions customers ask AI assistants in your market, such as “best invoicing tool for freelancers in France” or “family-friendly hotel near Aix-en-Provence”, and see who appears.
2. Read what AI says about you. Are you mentioned? Is the description right? Are prices, locations or products out of date?
3. Agree one set of facts. Name, short description, categories, locations, prices and key links. This becomes the reference for everything else.
4. Create focused content. Pages that answer specific buyer questions with a direct answer, clear sections and real detail.
5. Support it with structure. Logical headings, lists and tables in the content, plus schema suggestions that match what is on the page.
6. Distribute and track. Publish, then keep checking the same prompts over time.

Where structured data earns its place

• Identity: Organization markup with sameAs links tells machines which profiles are really yours. Useful if your name is common or similar to another company.
• Changing facts: Product prices, stock and opening hours change. Markup that updates with the page reduces stale information.
• Authorship: Article and Person markup make it clear who wrote something and when.
• Locations: LocalBusiness markup per location helps avoid one branch’s details being mixed with another’s.

Where it does not help much

Schema does not create authority, reviews or mentions in respected sources. It also cannot change what a model learned during training. When we see a brand missing from answers, the fix is usually better content and a stronger presence on credible sites, with structured data playing a supporting role. We explain the reasoning more in how LLMs decide which companies to mention.

What this means for you

If you already have basic schema on your site, great: keep it accurate. If you do not, add Organization and the types that match your business, then focus on content. Our beginner’s guide to schema markup covers the basics if you want to start yourself.

An example of how this plays out

Picture a small software company whose name is close to a larger firm abroad. When we track prompts, assistants sometimes merge the two. The fix is not more markup on its own. It is a clear About page stating what the company does and where it is based, Organization markup with a stable @id and links to its real profiles, consistent descriptions on review sites, and content answering the questions its own buyers ask. Structured data ties these pieces together. The content and outside profiles do most of the work.

How Keboost helps

Keboost plans include prompt tracking across major AI assistants, competitor visibility, and focused content created and distributed for you. Starter is €29 per month and Professional is €99 per month, cancel anytime. We cannot promise any assistant will mention you, but we can show you clearly where you stand and what to improve. Run a free AI visibility check to start.