Native Score (AI Native Knowledge)

What is the Native Score?

The Native Score is an AI Labs Audit metric that measures what an AI model (LLM) answers about a brand with no external web retrieval enabled in the test protocol (native mode).

It is an operational indicator of the model's internal associations — not proof of presence in training data. It reflects what the model can answer on its own at a given point in time.

How is the Native Score Calculated?

AI Labs Audit queries each AI model (ChatGPT, Claude, Gemini, Perplexity) with web search disabled. Questions cover:

• Brand and sector recognition
• Product and service knowledge
• Reputation perception
• Positioning relative to competitors

Responses are analyzed and scored on a 0-100 scale based on accuracy, completeness, and mention sentiment.

Native Score vs Web Score

The Native Score and Web Score form a complementary pair:

• Native Score: what the model answers without web retrieval (native mode)
• Web Score: what the model finds via web search / grounding

A significant gap between the two scores indicates an opportunity: if the Web Score is much higher than the Native Score, the brand is visible online but not yet embedded in model knowledge. The reverse suggests a historically known brand whose current web presence is insufficient.

Why the Native Score is Strategic

Users don't always enable web search when querying an AI. In those cases, only the model's native answer counts — which is why measuring this mode separately matters.

Improving Your Native Score

The Native Score is the hardest to influence in the short term as it reflects the associations internalized by the model. However, medium-term strategies exist:

• Strengthen presence on sources that LLMs ingest (Wikipedia, Wikidata, press, academic publications)
• Increase the volume and quality of online mentions
• Build E-E-A-T signals for the next training update

For a detailed analysis, read our article Native Score vs Web Score in GEO Audit.