A method built to connect brand, search and machine understanding.
The MAPS Method grew out of a simple problem: AI visibility conversations kept collapsing into the last thing we could see. Usually that meant a citation, a mention or a share-of-voice score. Useful outputs, but not the whole system.
The idea
AI systems can know a brand from learned patterns, retrieve current evidence, compare sources, extract passages and assemble an answer before a user ever visits the site. That changes the optimization problem.
The brand has to be understandable. The claims have to be credible. The evidence has to exist where systems can retrieve it. And the information has to be usable by machines.
Those became Meaning, Authority, Presence and Signals, the four conditions in the MAPS Framework. The MAPS Method adds the work required to define, prove, distribute and enable those conditions.
MAPS is not an attempt to invent four new marketing disciplines. It is a way to see how familiar work contributes to the conditions behind AI visibility.
Grant Simmons
I work at the intersection of search, information retrieval, semantics, content and marketing technology. Much of my work is about translating technical systems into models marketing teams can actually use.
The MAPS Method is intended to do that for AI visibility without pretending that the underlying systems are fully observable.
What the framework is for
- Brand and entity audits
- AI search and SEO strategy
- Content planning and prioritization
- Source and corroboration analysis
- Technical/entity implementation
- Measurement and validation
Use it, test it, challenge it.
The method should earn its keep in real work. If a pillar stops explaining a meaningful condition or a better model emerges, it should change.
Talk to me about MAPS