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Implementation guide · SEO & GEO

A non-commodity content workflow for AI discovery

A durable page begins with a reader decision and ends with evidence a generic prompt cannot reproduce.

Andrew KulykLead Editor, Marketing AI Geek

Published
Updated
Reading
8 min read
Editorial disclosure

AI may assist research organization and drafting. A human editor reviews every published page, checks material claims against the cited sources and owns the final decision. No company paid for placement in this article.

AI use policy

Agent-ready brief

AI takeaways

Keep the key points here, or take a source-aware text brief into Claude, ChatGPT or another AI workspace.
  1. 01Start with the operating decision, not the AI feature.
  2. 02Separate tested evidence, primary sources and company-supplied claims.
  3. 03Keep the responsible human, change record and commercial relationship visible.
Includes summary, takeaways, sources and a use note.

01 / Market shift

The category is changing at the workflow layer.

A durable page begins with a reader decision and ends with evidence a generic prompt cannot reproduce. That makes interface screenshots insufficient evidence. The useful question is how information moves through the marketing workflow, which action the system can take and which decision still belongs to a person.
For a marketing team, the cost of a weak handoff can exceed the time saved by generation. Product evaluation therefore needs the full operating record rather than a feature checklist.

Reference architecture

A non-commodity content workflow for AI discovery

A reference architecture to adapt and test—not a universal prescription.
  1. 01Define the reader decision

    Scope and source

    Pass evidence, decision and owner forward
  2. 02Build a primary source set

    Controlled workflow

    Pass evidence, decision and owner forward
  3. 03Add a test, dataset or point of view

    Controlled workflow

    Pass evidence, decision and owner forward
  4. 04Run human fact and conflict review

    Controlled workflow

    Pass evidence, decision and owner forward
  5. 05Publish citations and a change trigger

    System of record

    Record outcome and trigger review

Reference pattern, not a universal prescription. Validate privacy, compliance, integration and operating requirements for your own context.

02 / Evidence

What a buyer should ask to see.

Ask for the source of the input, the transformation applied to it, the exception path, the approval right, the system of record and a result that can be compared against a baseline.
  • A real workflow with representative inputs
  • Original captures or source-linked records
  • Known limitations and failure conditions
  • A dated change and correction route

03 / Editorial view

The record should come before the verdict.

Marketing AI Geek will publish structured records before composite rankings. A product may be strong for one workflow and weak for another; paid participation cannot change that evidence state.
The launch edition is a research framework, not a claim that every product has already been independently tested. Named evaluations will state the exact access and work completed.

Research note

Methodology

  1. 01Define the buyer decision and page scope.
  2. 02Prefer primary sources; label vendor-published survey or product material.
  3. 03Add an original analytical or structured evidence layer.
  4. 04Run human fact, conflict and boundary review.
  5. 05Publish dates, sources, disclosure and correction route.
Read the full methodology

Source ledger

Sources & editorial notes

  1. 01
    Optimizing your website for generative AI features

    Google Search Central · Official guidance emphasizing useful, original, non-commodity content and established SEO foundations.

  2. 02
    Marketing AI Geek editorial methodology

    Marketing AI Geek · Evidence states, first-hand proof requirements, freshness and correction protocol.

  3. 03
    Marketing AI Geek AI use policy

    Marketing AI Geek · Human review, permitted assistance and prohibited automation practices.

Corrections or primary material: contact the corrections desk.

About the author

Andrew Kulyk

AI-focused CMO and B2B marketing systems operatorAndrew Kulyk is an AI-focused CMO and the lead editor of Marketing AI Geek. He works across SEO, AI discovery, B2B demand generation, content operations, automation and performance measurement.Coverage is informed by the author’s professional practice and follows the publication’s sourcing, disclosure and correction standards.View author profile LinkedIn

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