Agent Readiness Optimization (GEO)

Problem: AI agents increasingly decide how a professional is discovered and described, but they misread sites built only for humans.

This very site is the reference implementation. As AI assistants increasingly mediate how people find and judge professionals, a site built only for humans gets misread. So this portfolio was engineered as a four-layer, agent-native stack from a single source of truth: an llms.txt discovery file and an expanded llms-full.txt knowledge document, a schema.org JSON-LD entity graph (Person, ProfilePage, Book, and every project cross-linked by @id), and an evidence layer of stable per-project Markdown URLs. It runs as a measured N=1 case study: an identical 25-prompt benchmark is scored across five AI surfaces (Google AI Mode, ChatGPT, Perplexity, Gemini, Claude) before and after the changes are crawled, on discovery, accuracy, completeness, citation authority, and whether the model can reconstruct the full evidence chain from person to production system to measurable result to source.