A method built to connect brand, market, search and machine understanding.
The MAPS Method grew out of AI-visibility conversations that kept collapsing into the last thing we could see: a citation, a mention, a share-of-voice score. Useful outputs, but not the whole system.
Understood, trusted, found and usable were never only AI requirements.
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. Working on them made something obvious: they were what every marketing plan was already trying to achieve, with the machine added as a third reader beside the buyer and the market.
MAPS is not an attempt to invent four new marketing disciplines. It is a way to see how familiar work, from positioning and PR to media and technical SEO, contributes to the conditions a brand needs to meet.
What the framework is for
- Brand strategy and messaging
- ICP and go-to-market planning
- Channel and media planning
- Content planning and prioritization
- Brand, entity and source audits
- AI search and SEO strategy
- Technical, feed and agent readiness
- Measurement and validation
Grant Simmons
I work at the intersection of search, information retrieval, semantics, content and marketing technology. Much of my work is translating technical systems into models marketing teams can actually use. The MAPS Method is intended to do that for brand and AI visibility without pretending the underlying systems are fully observable.
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 →