Daimonia's AI answer visibility (GEO) methodology is our core asset: an evidence-driven, AI-native, continuously evolving, dual-market framework for optimizing brand AI visibility.
How We Break the Problem Down
How does an AI search engine decide which brand to recommend? This isn’t a mystery: it’s a traceable engineering problem. We break it down into key dimensions covering the entire chain from technical foundation to content quality, from structured data to authority signals. Each dimension has explicit scoring criteria and an execution path, so the same framework runs against any brand and every step can be audited afterwards.
Four Core Principles
Evidence-Driven
Every optimization recommendation is based on verifiable data and standards. No guesswork. Two numbers check the work: for clients we have served, the AI recommendation rate measures 86% and fact citation 72%, on an anonymous stress-test basis.
AI-Native
The entire methodology is executed by AI, from content production and multi-platform distribution to data analysis and performance monitoring. End-to-end automation.
Continuously Evolving
AI assistants can change their crawl rules and recommendation logic with every model update. We track those changes continuously: when a model or a platform rule shifts, the scoring criteria and execution path for the affected dimension shift with it. This isn’t a static manual.
Dual-Market Depth
China’s AI search market has its own underlying logic: each major model has different search backends, content ecosystems, and recommendation mechanisms. Global AI answer visibility (GEO) theory can’t be directly applied. We go deep in both China and global AI ecosystems; every new client makes the methodology thicker.
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