Written by Roman ChornovolGenerative engine optimization is the practice of shaping the narratives, sources, and frames that AI models draw on when constructing answers, so a brand's story, not just its name, becomes the basis of the model's recommendation.
Also known as: GEO, generative engine optimisation
What Generative Engine Optimization Is
Generative engine optimization (GEO) is the discipline of influencing how AI-powered answer engines, ChatGPT, Gemini, Perplexity, Google AI Overviews, Copilot, and their peers, represent a brand, category, or claim when generating a response. Unlike traditional SEO, which targets ranked links, GEO targets the upstream layer: the sources, consensus signals, and narrative frames the model has learned to treat as authoritative. When a model answers a category question, it isn’t retrieving a page. It’s reconstructing a story it absorbed during training and retrieval. GEO is the work of making your story the one it reconstructs.
The practical scope of GEO covers which sources are cited, whose framing of a problem the model adopts, and which comparative claims survive into the generated answer. It also covers whether the brand appears as a protagonist in the category narrative or as an afterthought. This is why a brand can be frequently mentioned in AI answers and still lose: mentions are a downstream symptom, and the frame is the cause.
Why It Matters for AI Search
AI doesn’t rank, it recommends. That distinction changes what optimization means. A ranked result competes on relevance signals. A recommendation competes on narrative trust. The model has already formed a view of your category from the human-market consensus it was exposed to. By the time a user prompt arrives, the recommendation is largely pre-decided by the frame the model internalized. GEO is the practice of intervening at that upstream point: seeding, correcting, and reinforcing the sources and narratives that feed the model’s learned view, rather than reacting to the answers it has already been trained to produce.
This makes GEO a strategy, not a tactic. AEO (answer engine optimization) and narrower visibility plays are tactical responses to answers already being generated. GEO asks the prior question: whose frame is the answer built on, and how do we own that frame before the model catches up?
How Mavel Approaches GEO
Most tools treat GEO as a presence problem. They track whether you appear, count your citations, score your visibility. Mavel treats it as a narrative problem. Appearing in an AI answer isn’t the same as winning it. What wins is narrative share: whose story the model tells about your category. Mavel’s frame is that AI is downstream of consensus. The model recommends what the accumulated human-market narrative has already decided is true. That means the lever isn’t the answer itself but the sources, frames, and consensus signals that precede it. Acting on those inputs, rather than staring at a score, is what GEO looks like when it’s working.