Generative Engine Optimization (GEO) is the practice of optimizing your brand's visibility in AI-powered search engines. As buyers increasingly use AI tools to discover products, GEO ensures your company appears in those recommendations.
Traditional SEO focuses on ranking in search engine results pages. GEO targets AI models that synthesize information from multiple sources, requiring a strategy built on brand mentions, UGC, and multi-platform presence.
Our GEO framework is built on the formula: GEO = UGC + Mentions. We engineer visibility across user-generated content platforms and authoritative sources that AI models reference when making recommendations.
AI search engines are rapidly changing how buyers discover software. If your product isn't visible to AI recommendation systems, you risk becoming invisible to an increasing share of potential customers.
GEO spans multiple formats and platforms including text content, videos, owned websites, external publications, and UGC platforms like Reddit. The goal is multi-platform relevancy that AI models can reference.
Our GEO work has driven results like 100% organic traffic increases, 500+ product tutorials, 85% sales growth for PDF Reader Pro, and 250K+ monthly organic sessions for Linearity.
AI models analyze brand mentions, user reviews, authoritative content, and community discussions across the web. Companies with strong, authentic presence across these signals get recommended more frequently.
Search Everywhere Optimization expands beyond Google to include AI chatbots, voice assistants, and agentic search tools. It means optimizing your presence wherever your buyers are searching and asking questions.
Absolutely. GEO complements traditional SEO by adding AI-specific optimization layers. Together, they ensure visibility across both conventional search results and AI-generated recommendations.
GEO is a compounding strategy. Initial momentum typically builds within 3-6 months, with significant results emerging over 12+ months as AI models increasingly reference your brand across their training data.