Claim accuracy
How closely statements in AI answers match verifiable facts.
What is Claim accuracy?
Claim accuracy audits important statements about a brand against first-party and authoritative third-party sources. It is especially useful for pricing, features, security, availability, and policy information.
In practiceWhat does this look like in a real AI answer?
A weekly audit checks whether AI answers describe the current trial length and plan limits correctly.
Why it matters for AI visibility
AI answers can shape awareness and buying decisions before a person visits a website. Understanding claim accuracy helps teams connect what a model says with the prompts, sources, and technical signals that influenced the result. The useful next step is to observe the evidence, compare it over time, and act on the specific gap.
How to measure or use it
- Define the scope. Choose the relevant market, prompts, models, locations, and time window.
- Keep the evidence. Save the complete answer, its citations, and the page or claim being evaluated.
- Compare patterns. Look for repeated movement across prompts and models instead of reacting to one response.
- Take a specific action. Improve the owned page, correct a claim, earn third-party inclusion, or create better evidence.