Articles
Guides and analysis on aijelen.hu
Twelve in-depth articles by Zoltán Varga. Each one answers a single question, starting with the short answer.
AI Visibility Is a Distribution: How Many Runs You Need
Treating AI visibility as a statistical distribution requires minimum sample sizes and repeated testing to distinguish signal from platform noise.
Why AI Keeps Repeating Outdated Company Facts
AI systems blend fast retrieval with slow model memory, causing outdated facts to persist even after website updates.
How Model Updates Break Your Visibility Trend Lines
Model version changes invalidate historical data, requiring segmented trend analysis and distribution-based sampling to detect real shifts in AI visibility.
Did Visibility Change Because of You or the Model?
Stop guessing if your AI ranking shift is your success or the algorithm's drift; use rigorous attribution instrumentation to prove causality.
Which AI Visibility Gains Last and Which Fade?
Optimization tricks fade as models update; authority-building creates lasting citation patterns.
Checklist: What to Log for Every Tracked AI Query
A rigorous logging checklist ensures your AI visibility data remains reproducible, auditable, and resistant to the noise of model drift and platform updates.
Raw Mention vs Verified Mention: A Deep Dive into AI Visibility
Raw mention counts often mask hallucinations and false attributions; verified mentions provide the only reliable baseline for tracking AI visibility.
The Four Visibility States a Brand Can Be In
Brand presence in AI is not a single percentage but a dynamic distribution across four distinct states defined by consistency and context.
What a Recurring AI Presence Report Should Contain
Move beyond one-off snapshots to a structured cadence that tracks verified facts, model lag, and attribution risks over time.
AI Presence Tracking FAQ: Frequency, Sampling, and Change Detection
A practical guide to measuring AI visibility with statistical rigor rather than single-point estimates.