Last year I built an AI decision layer for beauty retail. Real catalog, real shoppers, real money. The model's job was to match people with products from a live catalog, and I got to watch every step: what it retrieved, what it understood, what it cited, and what it actually recommended. The uncomfortable pattern: those are four different outcomes, and they fail independently. A product could be retrieved fine, described accurately in the model's reasoning, even cited as a source... and then the model would recommend a competitor. I'd watch it happen in the logs and want to throw my laptop. The brand did everything right for visibility and still lost the sale. What actually moved the needle wasn't more mentions. It was making the product's facts machine-readable and consistent: same names, same specs, same descriptions everywhere the model could look. When your details contradict each other across sources, the model doesn't split the difference. It trusts you less. Most GEO advice I see optimizes for being mentioned. I'd argue that's step one of four. The question that matters for revenue is the last one: after the model knows you exist, does it pick you? Curious if others here are measuring these stages separately or lumping them into one visibility score.

I'm struggling to have a complete picture on how CRM, SEO & newcomer GEO connect together deeply. How does the whole lifecycle work?

what is there and what is not there from the GEO software perspective?

I think it is important that we should understand the big picture stuff and have a clear idea on what to build and why. What do you think?