About

The AI Clarity Index Methodology

A rigorous, repeatable framework for measuring how AI language models understand, describe, and recommend your business.

Why AI Visibility Matters

AI assistants — ChatGPT, Gemini, Claude, Perplexity — are increasingly the first point of contact between customers and your business. Unlike search engines that show links, AI provides direct answers. When someone asks “Who’s the best dentist in Boulder?” the AI names specific businesses.

The problem: most businesses have no idea what these models say about them. They might be misrepresented, confused with competitors, or missing entirely. That’s what the AI Clarity Index measures.

How We Score Your AI Visibility

Step 1

Multi-Model Querying

We query 4 major AI models with 5 question types: Direct, Category, Recommendation, Comparison, and Authority — 20 total queries per audit.

Step 2

Per-Model Scoring

Each model’s responses are scored on 3 dimensions: Entity Clarity (ECS), Source Alignment (SAS), and Narrative Consistency (NCS).

Step 3

Ensemble Analysis

Cross-model patterns analyzed via 3 ensemble metrics: Cross-Model Variance (CMV), Consensus Confidence (CCI), and Drift Risk (DRI).

Step 4

Composite Score

The 6 metrics combine into one ACI Score (0-100). Weights: ECS 20%, SAS 20%, NCS 15%, CMV 15%, CCI 15%, DRI 15%.

Score Tiers

90-100

Excellent

70-89

Good

50-69

Fair

0-49

Poor

Built by an Automation Engineer

The AI Clarity Index was created by Trevor Cusworth, an industrial automation specialist with over a decade building measurement and control systems. The same principles that govern process control — precision measurement, repeatable scoring, and actionable diagnostics — are applied here.

Based in Boulder, Colorado. Built with rigor. Delivered with clarity.

Measure Your AI Visibility

Get your AI Clarity Index score and see exactly what AI tells your customers.

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