AI Visibility Knowledge Base
Definitions, methodology, and explanations behind AI visibility diagnosis, repair, and verification.
This is not product documentation. It is the reference layer behind AI visibility diagnosis & repair.
Reference Sections
Core concepts and methodology behind AI visibility diagnosis & repair
AI Answer Reality™
How AI systems actually respond to real user questions, why answers differ across models, and how observed AI behavior becomes the foundation for diagnosis.
Truth layer. No fixes. No optimization.
eXAIndex Benchmark
How structural readiness is diagnosed, why it differs from live AI behavior, and how these gaps create visibility problems.
Interpretation & Confidence
How diagnostic conclusions are formed, why confidence levels matter, and how uncertainty is communicated honestly.
Temporal Drift & Stability
Why AI behavior changes over time, how fixes can decay, and how stability is verified through re-runs.
Methodology & Trust
Execution principles, reproducibility guarantees, and safeguards ensuring diagnostics are explainable and auditable.
Use Cases & Scenarios
How teams, agencies, and enterprises use diagnostics to explain AI behavior, justify fixes, and verify outcomes in real-world contexts.
Getting Started
Essential guides for understanding AI visibility diagnostics and taking your first steps.
Understanding AI Visibility
Learn the core concepts behind how AI systems discover, interpret, and cite your brand.
- What AI visibility means (and doesn't mean)
- Difference between indexing and recommendation
- Why traditional SEO metrics don't predict AI citations
Running Your First Diagnostic
Step-by-step guide to creating a project and interpreting your first GEO-RUN results.
- Creating a project and setting up tracking
- Understanding the 12 diagnostic modules
- Reading your overall GEO score and module breakdowns
Interpreting Diagnostic Results
How to read module scores, identify priority issues, and understand confidence levels.
- What each module score measures
- How to prioritize fixes based on scores
- Understanding 'pass', 'warn', and 'fail' thresholds
Taking Action on Findings
Translating diagnostic insights into concrete optimization steps.
- Common fixes for low-scoring modules
- How to verify improvements with re-runs
- When to expect measurable changes
Common Topics
Frequently referenced concepts organized by category for quick access.
Diagnostic Foundations
Key Concepts
Measurement & Tracking
Best Practices
Proven strategies for effective AI visibility diagnostics and optimization.
Establish a Baseline Before Changes
Always run a diagnostic before making significant content or technical changes. This baseline lets you measure impact accurately.
- Document current scores across all 12 modules
- Note specific failure points and warnings
- Save results for comparison after optimization
Prioritize Based on Impact, Not Score
Low scores aren't always the highest priority. Focus on modules that block AI discovery or citation.
- CoverageBreadth and EntityRecall are foundational
- Authority modules matter more for competitive categories
- Technical modules can often wait if content issues exist
Wait 7-14 Days After Changes
AI systems don't update instantly. Give changes time to propagate before re-running diagnostics.
- Major content updates: wait 7-10 days
- Technical/structural changes: wait 10-14 days
- Authority signals: may take weeks to months
Track Trends, Not Single Runs
Individual runs can show variance. Look for consistent trends across multiple diagnostics.
- Run quarterly diagnostics minimum
- Compare same time periods (avoid seasonal variance)
- Document external factors (algorithm updates, competitors)
Use API for Automated Tracking
Pro and Enterprise plans can automate regular diagnostics and integrate with BI tools.
- Schedule monthly runs via API
- Set up webhook notifications for completion
- Export data to dashboards for trending
Frequently Asked Questions
Quick answers to common questions about AI visibility knowledge and diagnostics.
Canonical Answers
Definitions and explanations for AI visibility diagnosis concepts
AI Visibility Academy
Educational resources explaining how AI visibility works, why diagnosis and verification matter, and how to interpret AI-driven market signals responsibly.
Educational content designed to build understanding, not sell features.