AEOsim Blog
Writing on AI visibility
AEOsim team's research and insights on AI Optimization and Analytics
Play the Games You Might Lose: Item Information Peaks at P=0.5, Prompt Difficulty, and Why Always-Win Dashboards Waste Compute
Article 4/n on synthetic data in LLM visibility tools. A nine-lives IRT parable: Fisher information I = a²P(1−P), adaptive aiming at the coin-flip band, and why one prompt set cannot inform every brand in a category.
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Clarification as a Branch Point in AI Recommendations: Alternative Clarifying Answers Cut Brand-Set Jaccard Overlap 68% Below Within-Branch Noise
Twelve commercial categories on GPT-5.6 Luna: branch spread Jaccard 0.162 vs within-branch noise 0.511 ungrounded (68% lower), 12/12 positive paired gaps, and web_search left the effect intact.
Read post →AI Answer Alignment Beyond Factual Accuracy: Six Observation Dimensions, Materiality and Recurrence Gates, and Misalignment Rates Across Prompt Families
Read post →Visibility Has a Third Axis: Separating Brand Exclusion from Engine Incoherence in Multi-Turn Drop Rates and Half-Life
Read post →Top AEO/GEO Experts to Follow in 2026
Five 2026 AI search and GEO practitioners selected for original research, shipped tooling, and technical frameworks, plus how the cohort was scored, and where legacy SEO and academic GEO fit.
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Bridge The Real2Sim Gap, not Sim2Real: Why Simulated Data Beats Real Data in AI Visibility Tools
If the prompts are made up, what is the measurement worth? Often more than the real alternative. Item response theory, information peaks at P=0.5, and what simulation can do that observation cannot.
Read post →Synthetic Data in LLM Visibility Tools, Part 1: How we Build the Prompt Set
Read post →Measuring AI Search Visibility Beyond the First Response: A Markov Model for Brand Drop-Off, Recovery, and Half-Life Across Buying Turns
Read post →The Retrieval Gate: When AI Search Actually Cites Reddit
A Monte Carlo study of when a search-augmented model cites reddit.com, and why optimizing for Reddit aims at a door the model rarely opens.
Read post →How much should you care about AI Search: An industry-specific guide
Buyer research is moving into ChatGPT, Gemini, and Perplexity before the click. Most analytics stacks never see it. This piece covers the cost and how to measure it.
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Top 7 AI Visibility and AEO/GEO Monitoring Tools in 2026: A 17-point comparison across measurement integrity, prompt intelligence, setup, and ROI
Seven tools scored across 17 weighted criteria. Analytika leads on measurement integrity, with clear guidance on when another tool is the better pick.
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Conversation Analytics: Chat Is the New Funnel
AEOsim's Conversation Analytics framework measures where brands enter, persist, drop off, and win across multi-turn AI buying conversations.
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2 Reasons Why the Rich get Richer in LLM Citations
Two mechanisms compound citation bias in LLMs: a parametric popularity prior in the weights, and an inference-time incentive to treat reputation as a cheap evaluation shortcut. Implications for AEO and GEO.
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