For two decades, the discovery question for B2B SaaS was simple: where do we rank on Google? That question is no longer the whole story. A growing share of buyers now open ChatGPT, Claude, Gemini or Perplexity and ask, in plain language, “what’s the best tool for X?” — and they act on the shortlist the assistant gives back.
Generative Engine Optimization (GEO) is the practice of influencing those answers: making sure that when an AI assistant recommends tools in your category, your product is on the list, named accurately, and described in a way that wins the click.
Why GEO is not just SEO with a new name
SEO optimizes for a ranked list of links. The user still does the synthesizing — they scan ten blue links and decide. GEO optimizes for the synthesis itself. The model has already read the sources, weighed them, and is handing the buyer a small, opinionated shortlist.
That changes the game in three ways:
- There is no page two. An assistant typically names three to five tools. You are either in that set or invisible — there is no “ranked #11” consolation traffic.
- Context travels with the mention. Being named isn’t enough. How you’re described — “best for enterprise,” “cheapest,” “hard to set up” — ships alongside the recommendation and shapes intent before the buyer ever reaches your site.
- The answer is non-deterministic. Ask the same question twice and the shortlist can shift. Visibility is a distribution, not a fixed rank, so it has to be measured across many runs.
What actually influences an AI recommendation
No one outside the labs has the exact recipe, but the inputs are observable and consistent:
- Breadth and quality of third-party sources. Review sites, comparison articles, forums, documentation, and reputable editorial coverage are the raw material models draw on. Tools that show up across many independent sources get recommended more reliably.
- Clarity of positioning. Models reward products that are unambiguous about who they’re for and what category they belong to. Fuzzy positioning gets paraphrased into someone else’s category.
- Corroboration over claims. A model trusts “three review sites and a Reddit thread agree this is good for startups” far more than your own marketing copy.
- Freshness and consensus. When the web’s consensus about your category shifts, the recommendations shift with it — often within weeks of new content landing.
Where to start
GEO work tends to follow a loop:
- Measure first. Pick the buyer questions that matter — your category, your competitors, your top use cases — and check what the assistants actually say today. You cannot improve a shortlist you’ve never read.
- Find the gaps. Note where a competitor is recommended and you aren’t, and why the model gives the reason it does.
- Close them with corroboration. Earn mentions in the third-party sources the models lean on. Make your positioning legible. Get specific, comparable, and quotable.
- Re-measure. Because answers drift, GEO is a tracking discipline, not a one-time fix.
Where Cypress fits
This is the loop Cypress is built for. It benchmarks hundreds of real, buyer-style prompts across the major assistants, then scores whether you appear, how strongly you’re recommended, which competitors win when you don’t, and which topics you’re missing — so the “measure” and “find the gaps” steps stop being guesswork.
GEO is early, which is exactly why it’s worth doing now. The brands that learn how AI assistants talk about their category today will be the defaults those assistants reach for tomorrow.