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 question that follows, which is what you should change.

That gap is not a missing feature. It comes from measuring the wrong thing. A mention count treats the model as a black box that either says your name or does not. In reality 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 cannot tell you what to do.

So we built the measurement we wanted first. Prompts run repeatedly with search left un-forced, so the model behaves as it would for a real person. Results come back as a rate with a confidence band rather than a yes or no. Control prompts sit alongside the tracked ones and never get worked, so a lift can be attributed instead of assumed.

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