geo-optimizer

GEO Optimizer (Generative Engine Optimization)

What It Does

Optimizes content for AI-powered search engines (ChatGPT, Perplexity, Gemini, Copilot) — the emerging "answer engine" paradigm. Goes beyond traditional SEO by optimizing for LLM consumption: structured data markup, entity optimization, citation-worthiness scoring, and AI search ranking monitoring. The goal is to be the source that AI engines cite in their answers.

Iron Laws (NEVER violate)

1. Citeable structure — Every claim must be backed by data in an easily extractable format. AI engines cite what they can parse.
2. Entity clarity — Entities (people, products, companies, concepts) must be unambiguously defined with schema markup.
3. Freshness signal — Content must show last-updated timestamps. AI engines heavily weight recency.
4. No cloaking — Content served to AI crawlers must match what humans see. Cloaking = black hat = delisting.

Red Flags (STOP immediately)

• Hallucination bait — Vague or contradictory statements that LLMs might misinterpret → rewrite for precision
• Unstructured data — Key facts buried in prose without schema markup → invisible to AI extractors
• Duplicate entity confusion — Same entity referenced with different names across pages → normalize
• Citation drift — AI engine cites your content but misrepresents the claim → file correction

Common Rationalizations (self-deception)

• "Good SEO is enough for AI search" → AI engines don't use PageRank. They use entity understanding and citation graphs.
• "AI crawlers will figure it out" → Without structured data, your content is invisible to LLM extractors.
• "GEO is just a buzzword" → ChatGPT alone drives 100M+ weekly queries. Ignoring it is ignoring a search engine.

When To Use

• Content that should appear in AI-generated answers (how-to, definitions, comparisons)
• User wants to track AI search visibility for their brand/topics
• Optimizing existing content for LLM consumption
• Checking if competitors are being cited by AI engines more than you
• Setting up structured data (schema.org) for entity optimization

Human Partner Signals (escalate to human)

• Black-hat temptation — User suggests keyword stuffing or cloaking for AI → educate on risks
• Brand misrepresentation — AI engine consistently misattributes claims to your brand → PR issue
• Competitive vulnerability — Competitor dominates AI citations in your category → strategic threat
• Technical implementation — Schema markup requires CMS/dev changes → coordinate with engineering

Pipeline

1. Audit: check current AI search visibility (what do ChatGPT/Perplexity say about your topics?)
2. Analyze: identify citation gaps — where should you be cited but aren't?
3. Structure: add schema.org markup (Article, FAQ, HowTo, Product, Organization)
4. Optimize: rewrite content for extractability — clear claims, data-backed, unambiguous entities
5. Monitor: track AI citation rates, entity recognition accuracy, ranking changes
6. Iterate: A/B test content structures, double down on cited formats

Verification Checklist

• All key pages have schema.org structured data (Article, FAQ, or HowTo)
• Entity references are consistent across all pages (same name, same schema)
• Every factual claim has an inline citation or data source
• Last-updated timestamps present and accurate on all content pages
• AI citation monitoring set up for top 10 target keywords
• Competitor AI citation analysis completed for main competitors

Related Skills

• content-strategist — GEO builds on SEO content strategy foundations
• growth-engineer — AI search traffic is a growth channel to measure and optimize
• social-media-manager — AI engines increasingly index social content for citations
• competitive-agent-research — Monitor competitors' AI search presence