AI native marketing | On-demand course: Build your AEO research pipeline
Build the system that tracks what’s shifting in SEO and AEO/GEO.
Set up a validation framework for AEO tactics, three agents that apply it, and a PXL-scored experiment backlog ready to run.
- Build a validation framework for AEO and AI search tactics specifically
- Monitor the authors and sources tracking what works in organic AI search
- Validate any AEO tactic against your framework before it enters your log
- Cross-reference logged tactics and surface patterns strong enough to test
- Score identified patterns using PXL to build a ranked experiment backlog
We are working on an on-demand version of this workshop.
It will be available soon as part of the AI Native curriculum.
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Meet your instructors:
4x’d efficiency & leads CXL’s growth, experimentation & product. Trained 25k+ marketers with 200+ courses, 4000+ blogs & playbooks.
SEO and AEO advice is everywhere. Almost none of it is validated. The gap is a framework that tells you which tactics are worth your time before you act.
AI search is not stable enough to rely on last quarter’s playbook. What earns citations in AI Overviews changes. What Google surfaces in SGE shifts. Teams optimising against yesterday’s signals are not just behind. They are building in the wrong direction entirely.
The problem is not a shortage of takes on what to do. It is that most of them are unverified opinion dressed up as practitioner insight. Separating the two requires a consistent method for evaluating source authority and evidence quality, applied before anything makes it into your strategy.
Part 01: What a research system looks like when it works
Before you build anything, you see the full architecture. Five components, how they connect, and what the outputs look like at each stage. Most research systems fail at the same point: tactics get collected but never validated, or validated but never cross-referenced into patterns.
- The five components of a research-to-action pipeline
- Where most research systems break down and why
- Outcome: A clear picture of what you are building before you write a line of code
Part 02: Build your validation framework
This is the most important artifact in the system. Everything else evaluates against it. You define what makes a tactic worth acting on: source authority, evidence quality, sample relevance, recency, and applicability to your context.
- What makes a tactic credible versus noise
- How to write scoring criteria tight enough for an agent to apply consistently
- Outcome: A validation framework saved as a markdown file, ready to use in every subsequent step
Part 03: Build the author monitoring agent
You build an agent that checks your list of trusted authors daily, surfaces new content, and proposes new authors to evaluate. The agent applies your validation framework to assess whether a proposed author meets your criteria before surfacing the recommendation.
- How to define and maintain a list of trusted sources
- How to structure the daily output so it takes under two minutes to review
- Outcome: A working author check skill file and a Slack-formatted daily output
Part 04: Build the tactic extraction and validation agent
You build an agent that takes any piece of content, extracts discrete tactics, and validates each one against your framework. Validated tactics get a structured card: one-sentence summary, source quote, validation score per criterion, and confidence level.
- How to extract tactics from dense content without losing nuance
- How to structure validation output so you can accept or reject in seconds
- Outcome: A working tactic extraction skill file you can run on any content you paste in
Part 05: Build the experiment prioritization system
Validated tactics get logged and cross-referenced against everything already collected. Patterns that appear across multiple credible sources get scored using the PXL framework: traffic potential, signal confidence, implementation ease, and speed to data. The output is a ranked experiment backlog, not a list of observations.
- How to cross-reference tactics and identify patterns strong enough to test
- How to score AEO and SEO patterns using PXL to prioritize by impact and ease
- Outcome: A ranked experiment backlog of AEO and SEO tests scored and ready to run
What do you need to make the most of the course?
- A free Miro account
- A free GitHub account
- Pro account on Claude preferable (alternatively: Codex)
- CXL will provide access to any additional tools needed for the session
THIS TRAINING IS PART OF THE AI NATIVE MARKETER SUBSCRIPTION:
Become the AI native marketer who ships the work of three.
Every two weeks, CXL helps you build one AI native workflow you can actually use at work. You only prep what matches your level, then build live with a top 1% operator.
AI ASSISTED
AI makes you faster at tasks you already did. The work itself has not changed.
You can tell because: you use AI for drafts and research, then hand off exactly as before.
AI INTEGRATED
You have rebuilt whole workflows around AI and ship work that used to need a specialist.
You can tell because: you no longer wait on a designer, a writer, or an analyst for your core work.
AI NATIVE
You build systems other people run and cover scope that used to take two or three people.
You can tell because: your team runs on workflows you designed.
There are 3 types of marketers. Which one are you?
Learning AI is a second job for most marketers. Join this program. 2hrs per week. CXL does the heavy lifting.
Here’s why marketers choose CXL
Need some more convincing?
Listen to this agency owner explain why he trains his team at CXL
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