KEMUZI INSIGHTS

Practices on AI decisions, evidence, and content growth.

The first pieces focus on GEO methods, buyer decisions, brand facts, and the reuse of real footage.

TOPIC SERIES

First practical topics

Each topic starts from real project questions and shows how to verify the answers.

Why simulated personas are not demographic labels

Age, gender, and income do not add up to a real buyer. We define a persona as a decision task plus its constraints — the same brand earns very different judgments under different tasks.

  • Why demographic labels mislead
  • How decision tasks and constraints are composed
  • How one persona set covers real buying paths

Series 01 · In preparation

How multi-factor analysis prevents GEO misjudgments

The same question yields different answers in Germany vs. Southeast Asia, first purchase vs. replacement, loose vs. tight budgets. Watching only “were you mentioned” leads to the wrong conclusion.

  • What the six public analysis factors each change
  • How to attribute differences to executable causes
  • When a same-condition retest is required

Series 02 · In preparation

What does one AI citation actually support?

Being mentioned by AI is not being recommended. Citation presence, factual accuracy, and recommendation rationale are three separate things — verified separately, repaired separately.

  • Three forms of citation presence and their credibility
  • Common sources of factual errors
  • Which evidence most shapes recommendation rationale

Series 03 · In preparation

From real footage to reusable video assets

The value of footage is not the first publish but every reuse after it. Structured scripts, shot tagging, and version records let one batch of footage keep producing new content.

  • How to tag shot assets so they stay searchable
  • How scripts map to shots
  • How version records cut rework cost

Series 04 · In preparation