Resources

A practical library for the AI discovery shift

Playbooks, checklists, and explainers on how AI systems understand, verify, and recommend businesses.

If you want the fast path, start with the stack and the monthly workflow.

Start here

The stack, in one diagram

Recommendation is a three step gate. Learn the model first, then use the workflow to improve it month over month.

1) Understand

Can AI parse what you do, who you serve, and what you’re best at?

GEO

2) Verify

Do trusted public sources corroborate your identity and claims?

GEM

3) Choose

Would the model recommend you with confidence for the user’s intent?

Perception

Playbooks

These cover the mental model, the monthly operating cadence, and how to translate insights into deployable fixes.

A framework for getting discovered in the AI era: understand, verify, get chosen. Includes the AI Visibility Stack and an implementation checklist.

A repeatable cadence: scan, diagnose, apply fixes, benchmark, improve. Designed for operators and agencies.

Concepts

Use these to explain the shift to teams, clients, and stakeholders who are still thinking in classic SEO terms.

How AI systems decide which businesses to describe and recommend beyond rankings, keywords, and traffic.

How scores stay defensible: observable signals, transparent scan reliability, and methods teams can audit.

Research notes

Short reads you can forward to a teammate to establish context fast.

AI assistants do not return lists. They return conclusions driven by clarity, verification, and model confidence.

A map of the structural, external, and perception signals AI systems rely on to recommend businesses.

Start with access. We will scan, score, and produce Fix Packs you can deploy.