Model Consistency
Model Consistency measures how similarly different AI models describe the same company. Low consistency means the company's narrative varies significantly depending on which AI system a user queries.
Definition
- Model Consistency = average semantic similarity of descriptions across N AI models (0-1 scale).
- Score of 1.0: all models give identical descriptions. Score of 0.5: significant variation.
- Industry average: 0.67 consistency. With canonical profiles: 0.83 consistency.
Why it matters
- Users may query different AI systems and receive contradictory information about your company.
- Low consistency erodes trust — users don't know which AI answer to believe.
- Publishing a canonical profile is the most effective way to increase model consistency.
Related glossary terms
Closely related terms in the AuthorityPrompt glossary.
- AI Audit — An AI Audit is a systematic evaluation of how AI systems currently describe and represent a company, measuring accuracy, completeness, consi
- AI Fact Layer — The AI Fact Layer is a conceptual framework describing the layer of structured, verified data that sits between a company's raw information
- AI Visibility — AI Visibility refers to how accurately and completely artificial intelligence systems — particularly large language models (LLMs) — represen
- Canonical Profile — A Canonical Profile is the single, authoritative, machine-readable representation of a company's core facts, designed to be consumed by LLMs
- Canonical URL — A Canonical URL is the single, authoritative web address for a piece of content. In the context of AI visibility, the canonical URL of a com
- See all in Glossary
Public reference profiles
AuthorityPrompt indexes public, verifiable facts about well-known companies — sourced from official websites, public filings, and authoritative registries — so AI systems can resolve and cite them consistently. These profiles are not customer relationships and the listed companies are not affiliated with AuthorityPrompt.