AI Visibility Scoring Methodology

This methodology is based on TrendAxis research and implemented within Axis Suite.

How Axis Suite measures AI visibility — inputs, methods, limitations, and update cadence.

Reference: Axis Suite AI Visibility Framework (2026)

This framework defines how AI visibility is measured, benchmarked, and improved across AI systems.

What Is Measured

The AI Visibility Score (0–100) quantifies how frequently and prominently a brand appears in AI assistant responses. It aggregates signals across four major platforms:

ChatGPT (OpenAI API)
Claude (Anthropic API)
Gemini (Google API)
Perplexity (API, when available)

Inputs

Brand mention rate: Whether the brand name appears in the AI response
Citation positioning: Where in the response the brand is mentioned (early, mid, late)
Recommendation strength: Whether the AI explicitly recommends, lists, or merely mentions the brand
Multi-prompt coverage: Performance across a library of industry-specific prompts
Competitor comparison: Relative visibility vs. tracked competitors
Custom prompts: User-defined queries with placeholder substitution for business, industry, location, and service

Data Collection Method

Results are collected via official AI platform APIs (OpenAI, Anthropic, Google). Each prompt is tested once per scan. API responses may differ slightly from what users see in the web chat interface.

This is industry standard for automated tracking. Web interface results can vary by session, location, and user history — API provides more consistent, reproducible measurements.

What Is NOT Measured

The following are explicitly excluded from the AI Visibility Score:

Google search rankings: SEO position is not a factor in the AI Visibility Score
Website traffic: Page views, bounce rates, and session data are not inputs
Social media metrics: Follower counts, engagement rates, and impressions are not measured
AI search volume: There is no reliable public metric for "how many people asked AI about X" — we do not fabricate this data
Model internals: We do not claim to know how AI models rank sources internally

Update Frequency

On-demand scans: Users can trigger scans at any time (subject to plan limits)
Automated monitoring: Available on paid plans; frequency depends on plan tier
Prompt library updates: Industry prompt libraries are reviewed and updated quarterly

Limitations

AI Visibility scores are influenced by:

Model training data differences across platforms
Prompt variation and phrasing sensitivity
Regional differences in model behavior
Time-based changes as models are updated
Single-run measurement (each prompt tested once per scan)

Scores should be interpreted directionally, not absolutely. They indicate trends and relative positioning, not guaranteed real-world outcomes.

Score Composition

The overall AI Visibility Score is a weighted composite of:

Mention Rate (40%): How often the brand is mentioned across all prompts and platforms
Recommendation Strength (25%): Whether mentions are explicit recommendations vs. passive listings
Positional Authority (20%): Where the brand appears in the response (first mention vs. later)
Cross-Platform Consistency (15%): Visibility stability across multiple AI platforms

Axis Suite is the platform that applies these concepts in practice.