Generative Engine Optimization (GEO) is the practice of building the brand signals that cause large language models to mention and recommend your business inside AI-generated responses, not just cite your content as a source.
The difference between AEO and GEO
AEO focuses on getting your content cited in direct answers: when someone asks a specific question, your page is the quoted source. GEO focuses on getting your brand recommended in generative responses: when someone asks an AI to suggest vendors, agencies or products in a category, your name appears in the output.
Both matter. AEO captures query-level intent. GEO captures category-level awareness. A complete visibility strategy in 2026 runs both.
How generative models learn to recommend brands
Large language models build their understanding of which brands exist in a category from the sources they are trained on and, for retrieval-augmented models, from the sources they crawl in real time. The brands that appear consistently across trusted publications, industry directories, review platforms, comparison articles and authoritative third-party mentions are the ones models learn to associate with a category.
GEO is the systematic process of building that footprint.
What we build for GEO
We identify the sources your category's buyers and AI models trust, build your presence on those sources, create the comparison and category content that positions your brand in the right conversations, and track how frequently generative models mention you versus your competitors across a defined set of category queries.