The part of AI that changes your job

AI visibility across LLMs

Your buyers ask a model instead of a search engine, and the model either names you or it doesn’t. Generative engine optimization is the work of being the answer: content structured so a model can parse it, claims sourced so it can cite them, and presence in the places these systems draw from. Measurable, early, and in most categories nobody has started.

Typical engagement: an audit of how you surface across the major models, a prioritized fix list, and the content architecture to hold it.

Intelligent content systems

Stop producing more content. The challenge is producing more content that still sounds like your brand. Every team can now make ten times as much, almost none can make ten times as much that holds the voice, and that gap is where the reputational damage happens.

The fix is a system: a brand definition explicit enough for a model to follow, retrieval over your real material, and a review step that catches drift before publication.

Typical engagement: building that system with your content team, then handing it over.

Agents in marketing operations

Where agents belong in the marketing stack today, where they don’t yet, and what has to be true about your data, processes, and approvals before either answer holds. Frequently the most valuable output is a clear no on two of the four things you were about to fund.

Typical engagement: an operations review and a sequenced roadmap.

Executive enablement and workshops

AI adoption requires more than tools. Coaching for marketing leaders who want a thinking partner on this rather than a vendor, and team workshops run as single-day tactical sessions or multi-session executive engagements. Includes the Prompt Playbook and Claude Cowork for Marketers.

The test for a workshop is whether the team can do the work the following Monday.