Answer Engine Optimization: A Practical Guide to Thriving in AI Platforms
- Chris McLellan
- 3 hours ago
- 6 min read
This guide created by Chris McLellan, Principal marketer at Friends Electric outlines the trends reshaping digital commerce and the specific actions required to increase your visibility inside AI platforms like ChatGPT, Gemini, Claude, and Perplexity.
Are competitors cited in AI answers but you are not?
Is organic search traffic declining despite rankings?
Are AI summaries reducing click-through?
Are buyers arriving pre-informed?
TL;DR
AI platforms like ChatGPT, Gemini, Claude, and Perplexity are becoming a primary discovery layer for buyers. Search results are increasingly summarized by AI. Buyer journeys are compressing. Comparisons and evaluations are happening inside LLMs before users visit your site. In some cases, transactions are beginning to shift into AI environments themselves.
If your products cannot be clearly evaluated, cited, or integrated into these systems, your visibility will decline.
Tactics to stay visible and earn in AI Platforms:
Map buyer questions by persona and journey stage
Use fan-out research to identify the sub-questions AI systems generate
Build structured, extractable landing pages with standalone sections and FAQs
Apply accurate schema
Strengthen off-site mentions where AI models look for authority
Test AI-native ads where available
Explore direct integrations (e.g., ChatGPT apps) where commercially viable
Track AI visibility, citations, and share of voice
What performs best now is clarity, structure, and credibility, not broad educational content.
For many buyers, AI platforms offer a faster and more convenient experience than the traditional web, and convenience tends to win.
Key Terms: AEO, GEO, & Fan Out
What is Answer Engine Optimization?
The process associated with getting products and services mentioned in the AI summaries of traditional search tools like Google Search, as well as newer AI search tools like Perplexity.
What is Generative Engine Optimization?
The process associated with getting brands, products, services, and thought leaders mentioned in the answers generated by chatbots like ChatGPT, Claude, and Gemini.
What is "Fan Out"?
The process by which Large Language Models break search queries e.g. "What are the best hotels in Toronto" into many sub-questions in order to synthesize final answers from multiple sources.
Key Trends: How AI is Re-wiring Discovery, Experience, & Commerce
We recently wrote about AI’s impact on email marketing. But Generative AI and AI-powered search are also reshaping buyer experiences and altering patterns in online commerce.
Evolving Discovery
Answers increasingly appear before links. Inclusion in AI-generated summaries now matters as much as traditional page ranking. AI Overviews and generative responses are absorbing informational queries and materially reducing click-through rates, shifting visibility from blue links to citations.
Compressed Journeys
Buyers are comparing and evaluating options inside AI platforms before visiting your site. Much of the discovery, comparison, and early qualification that once happened across multiple websites and tools is now happening in a single session in a single tool.
Composable Experiences
On-site and in-app bots are bypassing traditional UI and enabling dynamic, conversational experiences. Interfaces are becoming modular and personalized, allowing users to assemble information in real time rather than navigating fixed web pages or standard e-commerce search.
Transactions Shifting
The centre of online commerce is shifting. As AI platforms increasingly mediate discovery and evaluation, websites may evolve into validation layers rather than primary destinations for many brands.
Key Tactics: How To Appear in Large Language Models
Tactic: Generate Backlinks & Mentions
Mentions and backlinks will increasingly determine whether your brand, products, or subject matter experts are included in LLM conversations at all, so the key is to start by building brand mentions where LLM crawlers already "hunt for the truth":
Reddit
LinkedIn
Quora
Industry blogs
Podcast episode notes
Review platforms such as G2, Capterra, and TrustRadius
YouTube creator videos
Newsletters
Building authority inside LLMs requires sustained Digital PR and third-party validation.
Tactic: Create Fan-Out Driven Page Strategy
The goal of this tactic is to create a high-intent inventory of questions that buyers ask about your products, and LLM engines attempt to answer.
Pick one thing you sell:
Identify 3 to 5 common buyer questions using:
Sales calls
Support tickets
Search tools
Reddit, Quora, YouTube, Instagram (via Meltwater or Common Room if available)
Expand question list:
Enter each base question into a search demand tool such as Ahrefs or Semrush to generate a list of related search terms and question variations.
Enter the same base questions into Dejan' Fanout Tool (or an LLM) to generate additional AI-style question variations.
Add all results into one master list for the product.
Clean question list:
Use an LLM to remove duplicates, standardize wording, keep buyer language
Create product content spreadsheet:
Create one spreadsheet per product
Personas down the left
Awareness → Consideration → Evaluation → Decision across the top (columns)
Map questions into spreadsheet:
Place each question in the correct cell
Group similar questions in each cell
Each cluster represents one clear intent at that stage
Decide what each question cluster becomes:
Hub page:
Broad commercial intent
Core product or primary solution
Central navigation destination
Spoke page:
Narrow but still meaningful intent.
A specific use case, audience, comparison, or outcome.
Can stand alone and rank independently.
Section (within a page):
Sub-topic that supports a hub or spoke.
Needs explanation but not its own URL.
FAQ (individual question):
Clarification, objection, edge case.
Short answer.
Only makes sense in context of the main page.
Repeat this process for your top-performing products, strategic growth bets, or core categories. If you support many related products, run it at the category or solution level.
Some larger teams may choose to apply this at the feature level, but that requires significant content and operational capacity.
Tactic: Build Extractable Landing Pages
Almost any company can build (or re-build) landing pages to meet the demands of LLM crawlers. However, unlike traditional SEO landing pages, the design objective of Answer Engine Optimization landing pages is more about clean extraction of information as opposed to general education.
Below are a few practical tips for designing LLM-friendly landing pages:
Page layout:
Add "Last updated" date
Start with a one sentence direct answer
Add two or three short paragraphs of deeper explanation.
Write each section so it stands alone when extracted out of context
Use bullets, short paragraphs, and tables instead of long text blocks
Connect each idea back to your product to teach models when you are relevant
Break down a unique process when applicable e.g. "Combustible Commerce is our approach to..."
Add “fact blocks” that include a stat, brief explanation, and source link e.g. case study
Embed dynamic data e.g. embedded polls
Add FAQ (see next)
Page FAQs:
Add FAQs that support the specific sub-questions generated during fan out.
Include 5-7 FAQs per page.
Each FAQ should have an H3 or H4 heading, a one sentence direct answer, and two or three sentences of context.
Add "Last updated" date to FAQ section
Page Schema:
HowTo schema for step by step workflows
Product schema for feature and pricing information
Article schema for explainer
FAQPage schema for landing page FAQs
Tactic: Buy OpenAI ads
Just like in the old days of Google Search, brands can now skip the line and buy their way into OpenAI by paying for OpenAI ads.
As of writing, OpenAI ads are only available in the US market. But it's safe to say that they will be available in most major markets by the end of 2026.
Tactic: Build ChatGPT app
For larger ecommerce or data-rich brands, submitting their app to ChatGPT for direct integration can enable direct transactional experiences inside the AI environment.
Key Metrics to Track Answer Engine Optimization
Expect traditional site traffic to fluctuate as discovery shifts into AI platforms.
AI visibility: Are you appearing in AI summaries and answers
Branded query lift: AI exposure increasing brand searches
Share of Voice: Relative presence vs. competitors in AI responses
Citation frequency: How often your brand is referenced as a source
LLM-driven conversions: Purchases, bookings, or leads initiated inside AI environments
Paid AI campaign performance: If running AI-native ads
Tools to Measure AEO & GEO Results
HubSpot’s AEO Grader: free tool that provides a high level visibility snapshot.
Xfunnel: paid app to see which questions you appear for, which you do not, and which external sources AI uses instead. Recently acquired by HubSpot.
AI Search Is Replacing the Web. It's Critical To Stay Visible
Generative AI is disrupting any human activity that involves a keyboard or touch screen. This includes how buyers discover, evaluate, and choose products and services.
Large Language Models reward clarity, structure, and credibility. They surface brands that provide direct answers, appear consistently in trusted sources, and publish content that is easy to extract and cite. Brands can now also accelerate visibility through ads or direct integrations inside AI platforms.
Does this mean your web or mobile UI is becoming less central, and that the future is more about core capabilities and results?
Yes.
Does this mean you need to rethink the SEO work and landing pages you’ve invested in over the past decade?
Yes.
For many buyers, AI platforms already offer a better experience than the traditional web. And if the past 20 years has taught us anything, it’s that convenience wins.
Teams that start building visibility inside Large Language Models now will compound advantage as these platforms mature.
Teams that wait will eventually wonder where their online business went.
If you'd like to discuss what this means for your brand, please get in touch.
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