UI vs API
Genwolf collects AI responses using official model APIs.
This is a deliberate choice focused on measuring the core signals that actually determine AI visibility.
The layers behind an AI answer
An AI chat response is not a single system.
It's a stack of layers:
Model knowledge
What the model has learned and internalized.
External sources & citations
Which documents, domains, or references are pulled into the answer.
Chat software & personalization
UI logic, conversation history, location, user profiling.
The lower the layer, the more fundamental and stable it is.
What Genwolf measures
Genwolf focuses on the two most important layers:
1. Model-level knowledge & recognition
Whether the model:
- knows your brand
- associates it with the right category
- considers it relevant to a given question
This is the foundation of AI visibility.
If your brand is not present here, no UI layer can reliably surface it.
2. Source-level signals
Which sources are cited or referenced:
Sources strongly influence:
- which brands appear
- how they are framed
- who is compared against whom
Tracking sources reveals why visibility happens, not just that it happens.
Why the UI layer matters less for measurement
Chat interfaces add:
These layers affect how an answer is shown — but not whether a brand is fundamentally recognized.
They introduce noise without changing the underlying signal.
For measurement purposes, this layer is:
- volatile
- user-specific
- hard to normalize
Why API-based data is sufficient
API access allows Genwolf to:
While API responses are not a 1:1 user experience, they capture the heart of AI visibility:
what the model knows and what it chooses to reference.
Why UI scraping doesn't solve realism
UI scraping:
- still lacks real user context
- often runs in clean or private sessions
- repeats prompts mechanically
It attempts to reach the UI layer, but still misses true personalization.
UI scraping is also often unstable and can violate platform terms of service.
The result is higher complexity without access to more fundamental signals.
Trade-offs & limitations
What Genwolf captures well
- model recognition
- brand associations
- source-driven visibility
- relative competitive presence
What Genwolf does not capture
- individual personalization
- UI-specific ordering
- session-based behavior
These limits are explicit and intentional.
Summary
AI visibility is determined bottom-up:
Genwolf measures the layers that matter most.
Not the surface. The core.