What is GEO? Generative engine optimization, explained

When a buyer asks ChatGPT "what's the best option for X?", the answer is already written before you ever get a chance to pitch. Generative engine optimization (GEO) is the practice of improving what AI assistants say and cite about your company — the same way SEO improves where you rank in search results.

GEO vs SEO: different question, different levers

SEO competes for a position on a results page; the buyer still clicks through and reads you in your own words. With an AI assistant there is no list of ten links — there is one synthesized answer, in the assistant's words, built from whatever it learned about you. If that answer carries a wrong claim, ranks you behind a competitor, or skips you entirely, the buyer often never finds out more.

The two disciplines overlap (assistants read much of the same web that search engines index), but GEO has its own failure modes: fabricated or outdated claims, invisibility when buyers ask for recommendations, quiet deferrals where the model picks a competitor using strengths you actually have, and tone — how you're framed when you do appear.

How assistants build answers about you

Models combine two sources: what they absorbed in training, and what their crawlers and search integrations can fetch now. You can't edit the training set, but the fetchable layer is yours to control — and it's the part that updates on their next crawl. Three things determine it:

• Access. If robots.txt blocks GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot or Google-Extended, you're invisible to that assistant's retrieval no matter how good your content is.
• A canonical fact source. An llms.txt file at your site root gives crawlers one authoritative summary — your verified facts and differentiators — instead of leaving them to infer from scattered pages.
• Machine-readable structure. schema.org JSON-LD, clear FAQ content and consistent business profiles give retrieval something unambiguous to quote.

Measure first, then fix

GEO without measurement is guesswork. The baseline is simple to describe: ask each assistant the questions a real buyer would ask, several times (answers vary run to run), and grade every claim against your actual facts. That tells you which wrong claims, gaps and deferrals exist and how severe they are — so you fix what moves answers instead of polishing what was already fine.

Doing that by hand across models and repeat runs gets tedious fast; we wrote up the manual method in how to check what AI says about your company, and it's exactly what a ReputationIQ scan automates — every buyer question, three runs per model across ChatGPT, Claude and Gemini, each answer graded against your verified facts, ending in a severity-ranked fix plan.

Where to start this week

1. Check your robots.txt isn't blocking AI crawlers.
2. Publish an llms.txt — our free generator produces one in the format our fix plans ship.
3. Add Organization and FAQ structured data to your key pages.
4. Baseline what the models currently say, fix the worst claims first, and re-check after their next crawl.