Answer Engine Optimization: How to Get AI to Recommend Your Product

People are changing how they find software, and most companies haven’t caught up yet. Instead of typing queries into Google and scanning ten blue links, buyers are going straight to ChatGPT, Perplexity, Claude, and Gemini: “What’s the best treasury management platform?” or “How do I manage FX risk across multiple countries?”

The numbers are hard to ignore. ChatGPT handles over 5 billion visits per month. Perplexity processes more than 500 million queries. The traffic from AI recommendations converts at 11.2% vs. 2.8% for traditional Google organic. That’s 4x.

Gartner predicts 25% of organic search volume will shift to AI-generated answers by the end of 2026. If your product doesn’t show up in those answers, a growing share of your buyers will never know you exist.

What is answer engine optimization?

Answer Engine Optimization (AEO) is the practice of structuring your content and web presence so AI models can find, understand, and cite your product. It’s adjacent to SEO but works differently.

SEO optimizes for Google’s ranking algorithm. You target keywords, build backlinks, optimize page speed, compete for position. AEO optimizes for how language models build answers. They don’t rank pages. They pull from multiple sources, weigh authority signals, and construct a response. You don’t need to outrank competitors on a results page. You need to be the source that AI models trust when they’re assembling their answer.

The 7-layer system

A case study that made the rounds recently documented an ecommerce brand that went from zero AI visibility to a 41/50 recommendation score and $400K/month in AI-referred revenue. Seven layers, about 90 minutes per week to maintain once it’s running. I’ve been adapting it for B2B SaaS, and here’s how each layer maps.

1. Answer intent mapping

Before you optimize anything, you need to know where you stand. Ask AI assistants 50+ questions your potential customers would ask. Log every recommendation.

For treasury management, I tested questions like:

• “What’s the best treasury management software for mid-market companies?”
• “How do I get real-time cash visibility across multiple banks?”
• “What are the alternatives to Kyriba?”
• “How should I manage FX risk for international payments?”

Test across ChatGPT, Perplexity, Claude, Gemini, and Copilot. Record which products get mentioned, how they’re described, and where yours shows up (or doesn’t). This tells you exactly which gaps to close.

2. The answer hub page

This is the highest-leverage asset in the system. One comparison guide drives roughly 60% of all AI citations. It needs to read like a genuine resource, not a sales page with a thin veneer of objectivity.

What works:

• Neutral headline (“Best Treasury Management Platforms in 2026”)
• Honest TL;DR at the top
• Ranked product list that includes competitors with fair descriptions
• Feature comparison table with actual data
• FAQ section addressing buyer questions
• External citations to third-party reviews

AI models reward comprehensiveness and neutrality. A page that acknowledges where competitors are strong, while clearly explaining your own differentiation, gets cited far more than one that pretends alternatives don’t exist. I keep coming back to this: the best marketing to an AI is the same as the best marketing to a smart, skeptical human. Be honest, be thorough.

3. Brand-facts page

Think of this as a Wikipedia entry for your company, hosted on your own domain. AI models look for canonical sources of company information, and a brand-facts page gives them exactly that.

Include a structured facts table (founded, HQ, team size, funding, category), company narrative, product overview, leadership bios, and links to third-party profiles like Crunchbase and LinkedIn. The goal: when someone asks “What is [your company]?”, this page is what the AI pulls from.

4. Machine-readable data

AI agents are starting to fetch structured data directly. A /.well-known/brand-facts.json endpoint serves your company and product metadata in a format machines can parse without scraping HTML.

llms.txt files (inspired by robots.txt) give language models a structured overview of your site’s content. Both are low effort to set up. They signal to AI systems that your site was built with them in mind.

5. Schema markup

Structured data tells search engines and AI models what your content actually represents. The schemas that matter:

• ItemList + FAQPage on your comparison/answer hub page
• Organization on your brand-facts page
• BlogPosting on articles and blog posts
• Product with AggregateRating on product pages

Pages with proper schema appear 60% more often in AI-generated answers. It’s just JSON-LD in your page’s <head>. Not complicated.

6. Third-party citations

AI models weigh external mentions heavily. If the only place your company gets described is your own website, you’re at a disadvantage against companies that show up on G2, Capterra, Crunchbase, Wikipedia, and industry forums.

What to do:

• Create and maintain profiles on G2, Capterra, TrustRadius
• Submit your company to Wikidata (feeds into multiple AI training sources)
• Build a press page linking to coverage
• Create “Your Product vs Competitor X” comparison pages
• Show up in industry communities on Reddit, Quora, and niche forums

7. GPT shopping and review eligibility

For ecommerce, this means Google Merchant Center integration and clean product feeds. For B2B SaaS, the equivalent is maintaining accurate, review-rich profiles on software marketplaces. AI shopping features are expanding fast. Structured, verified product data will matter more over time, not less.

Where to start

If you’re building this from scratch, here’s the order I’d prioritize:

1. Answer Intent Map (1-2 hours) - know your baseline
2. Brand-Facts page + JSON endpoint (half a day) - canonical company data for AI
3. Schema markup on existing pages (a few hours) - low effort, high signal
4. Answer Hub comparison guide (1-2 days) - the single highest-leverage content asset
5. Third-party citations (ongoing) - build authority over time

Once the foundation is in place, maintenance is genuinely light. The case study that inspired this reported about 90 minutes per week, mostly updating the answer hub and monitoring AI-referred traffic.

Why now

Most companies haven’t started doing this. That’s the opportunity. Early movers get disproportionate citation share because there’s less competition for the “trusted source” slot in AI-generated answers.

The assets you build now (brand-facts pages, schema markup, comparison guides) compound. They don’t decay like paid ads. And the quality of AI-referred traffic is genuinely different. When a buyer asks ChatGPT for a recommendation and gets your name, they arrive with intent that’s already been filtered and validated by the AI. That 4.4x conversion rate advantage isn’t theoretical.

For B2B specifically, where deals are large and research cycles are long, showing up during the AI-assisted research phase can be the difference between making the shortlist and never being considered.

Final thoughts

AEO isn’t replacing SEO. It’s a parallel channel that reflects how buyer behavior is actually shifting. The playbook is straightforward: make your company’s information structured, thorough, and easy for machines to verify. The hard part is just doing the work.