What the term really means
Generative Engine Optimization describes work intended to increase the chance that reliable content is used in AI generated answers.
How it works in practice
GEO strengthens content that a generative system can cite as a concrete, reliable answer without replacing usefulness for people.
The decision to make before implementation
Build recognisable entities, consistent facts, source data and current pages that answer real audience questions.
How to verify that it works
Track citations, brand presence in answers, accuracy of cited information and actions that follow exposure to an AI answer. Compare results with an agreed baseline and review routine cases, difficult exceptions and human hand-offs separately. A practical GEO test should have an owner, a review date and a recorded example of an outcome the team will not accept.
The PAR HOUSE Agency approach
We approach GEO from the workflow rather than a tool demonstration. Build recognisable entities, consistent facts, source data and current pages that answer real audience questions. We then build a small measurable scope, record assumptions and expand only after quality review.
