Native Score (AI Native Knowledge)

What is the Native Score?

The Native Score is an exclusive AI Labs Audit metric that evaluates what an AI model (LLM) knows about a brand solely from its training data. No web search is performed: the model responds from its internal "memory."

This score reveals whether your brand is part of the model's training corpus — whether it was mentioned frequently enough in the texts the AI ingested for it to recognize it spontaneously.

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 AI knows from memory (training data)
• Web Score: what the AI finds in real time (web search, RAG)

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 native knowledge counts. A brand absent from training data is simply invisible to these users.

Improving Your Native Score

The Native Score is the hardest to influence as it depends on training data — frozen at the model's cutoff date. 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.