AEOsim Blog
Writing on AI visibility
Research and notes on measuring how brands appear, persist, and win recommendations inside AI conversations.
Synthetic Data in LLM Visibility Tools, Part 1: How we Build the Prompt Set
How to build an intentional, auditable prompt corpus for LLM visibility evaluation: variation types, 60/20/20 mix, syntheticity as a spectrum, and grounding when there is no AI query log.
Measuring AI Search Visibility Beyond the First Response: A Markov Model for Brand Drop-Off, Recovery, and Half-Life Across Buying Turns
AI visibility research settled on repeated sampling for run-to-run variance. Brand presence also drifts across turns. A two-state Markov chain gives GEO teams drop rate, recovery rate, and visibility half-life.
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.
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. Here's what that costs, and how to measure it.
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.
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.