About AI+ Local

For a local business, the most important recommendation it gets may be one it never hears. When a customer asks ChatGPT, Gemini, Claude, Perplexity, or Grok who to hire, the assistant offers a name, the customer acts on it, and the business on the other end has no idea whether it was named or skipped.

That blind spot is the early shape of a discipline the industry is only beginning to name: generative engine optimization, or GEO. Search engine optimization was about earning a position in a ranked list of links. GEO is about whether a generative model names you in its answer at all, how confidently it does so, and which sources it trusts to get there. The surface is different, the signals are noisier, and the feedback loop runs through systems that do not publish how they decide.

AI+ Local is the measurement layer for local GEO. We watch how the major assistants answer real local demand and turn it into a signal an operator can act on:

• Which businesses the models recommend, and where each one sits in the combined answer.
• How strongly the engines agree, since a name four assistants repeat is a different signal from one that surfaces once.
• The sources those recommendations rest on, the pages a model cites to justify a pick.
• The direction each of these is moving, week over week.

What each of these means precisely is set out in the glossary, and the procedure that produces them is the methodology. For most operators this is a channel with no instrumentation at all, and that is the gap we are built to close.

Why It's Mission-Critical

As assistants become the default way people find local services, their answers turn into a primary acquisition channel, and one of the few a business cannot see into. A name absent from the shortlist is not ranked low; it is outside the consideration set entirely, and the customer never learns it existed.

Treating that as something to measure, rather than assume, is the difference between managing the channel and being managed by it. Placement is never for sale; which models we read, how often, and what a ranking does and does not claim are taken up in the FAQ.

Why It Compounds

GEO is not a one-time score. The reviews, citations, listings, and mentions a model draws on accumulate, and each answer is a function of everything that came before it. That makes the signal path-dependent: its worth is not in any single reading but in the trajectory, which only resolves when you track it over time.

An operator who can see what moves the model can compound small, deliberate gains; one who cannot is left reacting to a number with no way to explain it. For a single business, that continuous read is what the Pro Report provides.