We run the experiments, then build what they point to

Omnipresence started from a problem with the tools. Every AI visibility platform answers the same question: are you mentioned, and how often. None of them answer the one that comes right after it, which is what you should actually change.

That gap isn’t a missing feature. It comes from measuring the wrong thing. A mention count treats the model like a black box that either says your name or doesn’t. But what actually happens is the model runs a series of searches, reads a set of pages, and decides from those. The pages are the lever. If your tool never shows them, it can’t tell you what to do.

So we built the measurement we wanted first. Prompts run over and over with search left un-forced, so the model behaves the way it would for a real person. Results come back as a rate with a confidence band instead of a yes or no. And control prompts sit alongside the tracked ones and never get worked, so you can attribute a lift instead of assuming it.

Then we connected the rest of it. The graph shows you which sources decide each answer, and the Agent MCP takes that graph into whichever tool you already work in and does the work against it.