Most AI systems fall back on whatever they can find. If your information is scattered, contradictory, or not machine-readable, they build a distorted picture. We create a clear technical foundation so your company can be understood more consistently. No black box. No illusions. Just clean information architecture.
Enter one public URL. The checker fetches the page directly on the server and evaluates how well the raw source already exposes the signals that matter for machine-readable AI visibility.
Frontend chats such as ChatGPT, Claude, or Kimi often work under security, rendering, and retrieval constraints. That means they frequently do not see a page the way a direct source fetch does.
Frontend chats do not see everything Many frontend prompts do not render a page like a normal browser. JavaScript, lazy-loaded content, or client-side JSON-LD may be missed entirely.
Always one URL only The checker intentionally evaluates exactly one page per run. If you want to assess more pages, submit each URL separately.
Direct source inspection The score looks at raw HTML, metadata, structured data, headings, consistency, and a few basic crawl signals.
The result is a heuristic score from 0 to 10. It shows how well the page is prepared for machine-readable AI visibility in its raw source state.
The starting point is not a simulated frontend chat answer, but what is actually present in the direct server response.
Title, description, canonical, HTML language, JSON-LD, and relevant schema types all contribute relatable to the score.
What matters is not one isolated trick, but whether headings, canonical, entity signals, and visible text work together.
No additional signals available.
Your website has grown, but not in a structurally consistent way. Content exists, but machines cannot interpret it clearly. And no one really knows how AI systems currently represent your company. The problem is rarely content. The problem is structure.
More content does not help. More JSON-LD on its own does not help either. If information does not fit together, additional signals only amplify the ambiguity. AI visibility does not fail because of missing tools. It fails because of missing information architecture.
When sources drift apart, adding more content often just increases the number of competing signals.
JSON-LD is useful, but not when the underlying information is unclear, inconsistent, or outdated.
AI visibility rarely breaks because one product is missing. It breaks because information sources do not work together cleanly.
Only a controllable information architecture reduces interpretation space for external systems in a lasting way.
We treat AI visibility as a technical system. Not as an SEO trick. Not as prompt optimization. But as a controllable structure.
A defined source for all central information.
Machine-readable facts without drifting away from visible content.
Clear relationships between the organization, products, and topics.
APIs and feeds that deliver consistent data.
Visibility into how external systems interpret your company.
No massive project. No theory phase. We work against your real weak points.
Where do contradictions emerge? Where is structure missing?
What has the biggest impact on clarity?
Integrate structured data, interfaces, and models cleanly.
Is the representation changing? Is it becoming more consistent?
You will not control external AI. But you can influence what those systems find consistently. That is where the difference is made.
We show where your information structure breaks today and which next steps make technical sense.
Most misunderstandings happen when AI visibility is confused with a single markup standard or with guaranteed control over citations.
No. JSON-LD matters, but it is only one layer. Without consistent core information, clear entities, interfaces, and maintenance processes, the effect stays limited.
Control, no. Improve what external systems can access and what they find consistently, yes. That is where the realistic value sits.
Not necessarily. A sensible start can already happen with clearly modeled data structures, JSON-LD, and simple API endpoints. The architecture can deepen later.
Almost never in the first schema, but in ownership, maintenance, and freshness. The decisive part is keeping the information base clean over time.
The first improvements usually show up internally: clearer ownership, more consistent core statements, and cleaner machine-readable signals. How quickly external systems react depends on their indexing, retrieval, and refresh logic.
No. A phased approach is usually the sensible one. We prioritize the most important entities, pages, and interfaces first and improve the information architecture where contradictions and ambiguity currently cause the most damage.