In July 2026, as part of our AI Native Marketer program, we ran a live workshop on building an AI-powered AEO and GEO research pipeline.
There’s increasing pressure on marketers to keep up with AI search. Follow the right people, read the latest studies, monitor LinkedIn, subscribe to every newsletter, and spot the next tactic before everyone else does.
But here’s the problem: marketers are also expected to ship more than ever before. How are you supposed to move faster when you’re still spending hours every week manually scanning articles, case studies, and social posts just to figure out what’s actually worth paying attention to?
So during the workshop, we built a system to do that scanning for us: an AI research engine that monitors the sources we trust, extracts new AEO and GEO tactics, scores them against a validation framework, and only surfaces the ones worth our attention.
TL;DR
- We built an AI research engine using n8n and Claude Code to monitor trusted sources, extract new AEO and GEO tactics, validate them, and turn promising ideas into experiments.
- The workflow uses three agents for extraction, validation, and source discovery, with human approval built into the process.
- The build took about two days and 22 test runs, 17 of which failed. Nine failures were silent and only surfaced during manual review.
- We adapted CXL’s PXL framework into eight evidence-based questions to prioritize tactics. The engine logged 58 tactics in its first two weeks.
- The result is a repeatable research-to-experiment workflow. The full AEO research pipeline workshop covers how to build the components yourself.
Table of contents
How did we map the workflow before automating it?
Before writing a single prompt, we mapped the process as it happens manually, end to end: track trusted authors, check their posts for new tactics, validate the results, cross-reference against official guidelines, log the tactic as valid or not, then decide whether to turn it into an experiment.
We used a swim lane diagram to lay this out, one lane per actor (a content marketer, a social search, an AI agent) so it’s clear who or what owns each step.
This step matters more than it looks. Skip it and you end up automating a workflow you never actually defined, which is how you get an AI agent doing three jobs badly instead of one job well. Map it first, then decide which steps are repetitive enough to hand off and which need a human.
How did we structure the AEO and GEO research engine?
Once the workflow was mapped, we split it into two pieces.
n8n runs the parts that repeat on a schedule: scraping sources, running the validation framework, logging results. Claude Code runs the parts that need judgment and iteration: turning a validated tactic into a prioritized, documented experiment.
What did the three research agents do?
Inside the n8n workflow, three agents do the work:
- An extraction agent pulls tactics from trusted sources using Firecrawl (a free-tier scraper) and Apify (for LinkedIn, where API access is otherwise restricted).
- A validation agent scores each tactic against the framework.
- A reference scout suggests new authors and sources worth adding to the list.
Critically, the review points aren’t at the end. They’re built into the flow: a curated seed list of trusted sources up front, a human-defined validation framework in the middle, and a Slack message at the end where every suggested tactic and source needs a yes or no before it gets added.
The build wasn’t clean, and that’s normal
This took about 2 days of on-and-off building and 22 test runs, 17 of which failed. 9 of those failures were silent (a suggestion would just look slightly off) and only surfaced on manual review.
Building an agentic workflow like this isn’t a five-minute prompt. It’s iterative, and most of the value comes from the failed runs, not the first draft.
Why did we use PXL instead of ICE to prioritize AEO and GEO tactics?
We went with PXL because we needed a way to prioritize tactics based on actual evidence rather than subjective scores. So we adapted CXL’s framework into eight yes-or-no questions specifically for AEO and GEO.
The engine can easily surface more tactics than you can act on. In our own build, it had already logged 58 in the first two weeks. The question isn’t whether a tactic is interesting, it’s whether it’s worth the engineering time to test.
Most teams reach for ICE (Impact, Confidence, Ease) here. It’s simple, and that’s also its weakness: all three scores are opinion. There’s no evidence requirement built in, so ICE tends to reward whoever pitches an idea most confidently.
Instead, we adapted CXL’s PXL framework, originally built for CRO experiment prioritization, into 8 evidence-based questions for AEO and GEO tactics. Each tactic gets scored 0 or 1 on every question, and the questions themselves force you to check for proof rather than assume it:
- Does it align with our existing strategy, and can it scale if it works?
- Is there credible evidence this could work (multiple studies, real sample size)?
- Has it been observed working in the wild, by more than one source?
- Can it be implemented in under a day?
- Does it require minimal engineering effort?
- Is it easy to roll back if it doesn’t work?
- Can success be measured within 2 to 4 weeks with clear KPIs?
- Can the impact be isolated from other changes?
How did we turn validated tactics into experiment briefs?
The last piece turns a validated, prioritized tactic into something you can actually hand to a team. Because the prompt for this is basically the same every time, it didn’t need to be a full agent. It’s a Claude Code custom slash command: type /tactics, and Claude pulls the logged tactics from Airtable, scores them against the PXL framework, and returns a ranked list with the reasoning for each score.
From there, you can ask it to go deeper on any single tactic, and it drafts a full experiment brief: background, hypothesis, method, success metrics, implementation plan, effort, risk, even which of our own blog pages to test first, based on our actual traffic data.
Frequently asked questions
How can you automate AEO and GEO research with AI?
You can automate AEO and GEO research by building a workflow that monitors trusted sources, extracts new optimization tactics, validates the evidence, and organizes promising ideas into experiments. Tools like n8n can handle scheduled monitoring and data collection, while AI agents analyze and score the findings. Keep human approval in the process so unsupported tactics don’t automatically make it into your backlog.
What tools do you need to build an automated AEO and GEO research pipeline?
An automated AEO and GEO research pipeline can combine n8n for workflow automation, Firecrawl and Apify for collecting research, Airtable for storing findings, and Claude Code for prioritizing experiments. Slack can handle human approvals. You don’t necessarily need every tool, but you do need a way to collect information, validate it, store the results, and turn useful findings into actions.
How do you know if an AEO or GEO optimization tactic actually works?
Start by checking the evidence behind the claim. Look for credible research, real sample sizes, and results observed by more than one source. Then test the tactic against your own website using clear success metrics, such as changes in AI citations or brand visibility. A promising tactic isn’t automatically a proven one, which is why validation should happen before implementation.
How should you prioritize AEO and GEO experiments?
Use an evidence-based framework like CXL’s PXL rather than relying entirely on subjective Impact, Confidence, and Ease (ICE) scores. Evaluate whether a tactic has credible supporting research, aligns with your strategy, can be implemented efficiently, and has measurable results. Scoring these criteria helps separate experiments worth testing from interesting ideas that aren’t ready for implementation.
How do you turn AEO and GEO research into actionable experiments?
Start with a validated tactic, then create an experiment brief that defines the hypothesis, implementation steps, success metrics, effort, and risks. You can use AI to generate these briefs from a prioritized research backlog. The important part is connecting every proposed experiment to the original evidence and deciding which pages or content assets to test first.
What to do next
Want to build this yourself? The original Build your AEO research pipeline workshop is now available on demand. It takes you through setting up the validation framework, building the research and validation agents, and turning the findings into a PXL-scored experiment backlog. You’ll go beyond seeing how we built it and actually put the system together.
