If you work in marketing long enough, you start collecting odd habits. Some people obsess over UTMs. Others treat keyword spreadsheets like treasured family heirlooms. And lately, more and more marketers confess something like this:
“Okay, but… what does ChatGPT think about my product?”
If you’ve had this thought at least once this year, welcome. You’re officially a participant in the new era of AI search, where your buyers get answers from engines that don’t look like Google’s ten blue links anymore.
This isn’t science fiction. It’s already reshaping the way people discover vendors, evaluate products, and shortlist suppliers. And if you want new business, you need your content to show up in the places AI engines pull from: results panels, answer boxes, summaries, citations, and product snapshots.
That’s where GEO comes in.
What is GEO?
GEO stands for Generative Engine Optimization. Think of it as the natural evolution of SEO for a world where search answers are drafted by large language models that read the whole web at once.
Traditional SEO is about helping search engines crawl pages. GEO is about helping AI engines understand them, factually, structurally, and contextually. If SEO helped Google spot you, GEO helps AI quote you.
And yes, the difference between being indexed and being cited is enormous. One brings traffic. The other influences decisions.
We’ve spent the past two years studying how AI engines “read” content across B2B categories. The results have shaped the GEO framework we teach inside the B2B Academy, used in projects for clients like LS Building Products, Webflow, and SmartRent.
And today, I want to show you the exact four steps you can apply yourself.
Ready? Take a breath. No jargon. No buzzwords. Just a practical map for making sure AI engines treat your content as the source of truth.
1. Make Content AI-Readable
Here’s a question: if an AI model reads your site, does it find answers faster than a human does? If the answer is “maybe,” then we have work to do.
AI engines love content that’s easy to scan, anchored in facts, and structured around real customer questions. This means your pages need to behave more like product sheets than marketing poetry.
Your goal: reduce friction for both humans and machines.
Try:
- TLDR sections at the top of your pages: short summaries that say, plainly, what something is, who it’s for, and why it matters.
- Checklists and spec tables: these give AI engines stable, structured facts, the kind they quote
- Pages organized by real buyer questions: not what you want to say, but rather what your buyer needs to know before they trust you.
- Schema markup: FAQ, HowTo, Product, Review, all of these help AI understand context.
When LS Building Products rebuilt their content architecture this way, something interesting happened: They didn’t just gain more organic traffic (though +67% is nice). They were quoted in Google’s AI Overviews, 540% more often.
Ask yourself: If someone asked Perplexity to compare you with a competitor, would your page give it enough facts to speak confidently?
If not, the fix starts here.
2. Treat Reviews as Ranking Fuel
You already know reviews matter for conversions. What most marketers haven’t fully realized is that reviews now influence visibility in AI engines too.
Perplexity, ChatGPT, and Google’s AI snapshots increasingly pull insights from product-level reviews to answer queries like:
- “Which [category] performs best for [use case]?”
- “What do customers dislike about [product name]?”
- “Best solutions for [industry] with [specific requirement]”
If you don’t have detailed, steady review volume at the SKU or feature level, AI engines have very little to work with. And when they have little to work with, they quote someone else.
Treat your review program as a ranking system, not a testimonial system.
Here’s what we see working:
- Ask for reviews tied to specific products or features, not your brand in general.
- Encourage detailed responses, not just star ratings.
- One-sentence reviews are basically empty calories.
- Respond publicly, because responses become part of your data footprint.
- Use Q&A sections, which AI models pull from frequently.
- Highlight reviews inside your own content, reinforcing the signals.
Webflow built an AEO/GEO program around this. After months of collecting structured review insights and tracking AI referrals, ~8% of new signups came from LLMs like ChatGPT and Perplexity. And those signups? They quadrupled in six months.
Now think about your own buyers:
If someone asked an AI engine “What’s it like to work with this vendor,” what data does the engine have to answer confidently? If the answer is “not much,” you’ve found your priority.
3. Optimize for Answer Surfaces
Here’s a fun exercise. Open Perplexity, type your product category, and ask: “Which companies are known for reliable solutions in [industry]?”
Now check:
- Are you mentioned?
- Are your competitors mentioned?
- Who is the engine pulling from?
AI search has dozens of “answer surfaces”, essentially the places your brand can appear:
- ChatGPT answers
- Google AI Overviews
- Perplexity citations
- SGE panels
- Copilot snapshots
- Comparison summaries
- Product recommendation lists
These surfaces behave like new digital real estate. And like real estate, the best spots go to whoever has something useful to offer. AI engines cite content that helps users make a decision. So, build content assets that AI engines consistently favor:
- Calculators and estimators: AI engines love referencing tools with clear inputs and outputs.
- Detailed comparison guides: straightforward, factual comparisons (yes, even with competitors).
- Spec-driven product pages: full dimensions, features, compatibility notes.
- “How it works” breakdowns: with numbered steps that can be quoted.
- Scenario-based guides: answers tied to specific industries or use cases.
SmartRent applied this playbook and saw something striking: Within six weeks, 32% of SQLs originated from ChatGPT and Perplexity citations.
So think about this:
What would someone have to publish so that AI engines start quoting them instead of you?
Then publish it first.
4. Measure GEO Impact
Here’s where things get fun. You can actually track GEO’s influence, not perfectly, but well enough to optimize. Most marketers still look at classic SEO dashboards, see no dramatic change in keyword rankings, and assume nothing is happening. Meanwhile, AI engines may already be sending traffic, leads, and citations.
You need a measurement setup made for this era. Start with these basics:
- UTMs designed for AI engines: label traffic from ChatGPT, Perplexity, SGE panels, or other AI surfaces.
- Tag assisted sessions “from AI”: some sessions won’t show a referrer; label these clusters and check patterns.
- Monitor citations: search for your brand and product names inside ChatGPT or Perplexity summaries.
- Track AI-assisted leads: whether through UTM discovery or through “How did you hear about us?” fields.
- Compare lead quality: in many B2B categories, AI-assisted leads convert better because they’ve already read your value proposition through an independent source.
How many of your leads this quarter were influenced by content you didn’t even know was being quoted by AI?
Chances are, more than zero.
You don’t need heroic redesigns or thirty-page strategies to win in AI search. You need clarity, structure, and content that’s grounded in real customer questions.
The companies showing up in AI answers aren’t the loudest. They’re the clearest.
- They publish facts.
- They publish details.
- They publish reviews.
- They publish tools.
- They publish content that helps someone decide.
And AI engines reward them for it.
So here’s your final question: If an AI engine introduced your brand to a buyer tomorrow, would it have enough to say? If the answer is “not yet,” great. You now have your four steps. And you’re ahead of most of your market.
