AEO and GEO: Optimizing Your Site for AI Search and Generative Engines

If your customers ask ChatGPT, Claude, Perplexity, or Google’s AI Overview a question your business can answer, are you in the response?

If you don’t know, you’re not alone. Most teams are still optimizing for a search world that’s quietly being replaced. The page-of-blue-links is shrinking. The answer engine is growing. The next layer — AI assistants citing sources in their responses — is already shaping buying decisions.

This post covers the two emerging disciplines, the concrete technical changes that matter, and how different types of companies should prioritize the work.

TL;DR

• AEO (Answer Engine Optimization) targets zero-click answers — featured snippets, voice assistants, AI Overviews. Optimize for direct, structured, scannable answers.
• GEO (Generative Engine Optimization) targets citations inside LLM responses — ChatGPT, Claude, Perplexity, Gemini. Optimize for entity clarity, structured data, and machine-readable site maps.
• The technical work is mostly the same: rich JSON-LD schema, breadcrumbs, clean entity graphs, an llms.txt, explicit AI crawler permissions, and content patterns that include direct answers and “last updated” signals.
• B2B and consulting sites benefit most. E-commerce, local services, and SaaS each have a different priority order.

Defining the terms

What is SEO?

Traditional Search Engine Optimization targets ranking on search-result pages. Keywords, backlinks, page speed, content depth. Still relevant — Google still drives most traffic — but it’s no longer sufficient.

What is AEO?

Answer Engine Optimization is the practice of structuring content so search engines can lift a direct answer and present it without a click. Think:

• Google’s “People Also Ask” boxes
• Featured snippets at position zero
• Google AI Overview (“AI-generated answers” at the top of results)
• Bing’s chat-style answers
• Voice assistant responses (Alexa, Siri, Google Assistant)

If a search returns “About 2.3 million results” but the user gets the answer from a quoted box at the top, that quoted box won. AEO is the discipline of being the quoted box.

What is GEO?

Generative Engine Optimization is newer and more strategic. It’s the practice of becoming a cited source inside generative AI responses — the named references LLMs surface when answering user prompts.

When Perplexity generates an answer with footnotes, those footnotes are GEO outcomes. When ChatGPT browses the web mid-response and quotes a paragraph, that’s a GEO outcome. When Claude says “according to Howling Global’s case study on AEM migrations…”, that’s a GEO outcome.

Unlike SEO, GEO doesn’t necessarily produce a click. It produces a mention in front of someone making a decision. For consulting, B2B services, and authority-driven businesses, that mention is often more valuable than a click.

Why both matter now

Three things changed in the last 24 months:

1. AI Overviews shipped to general availability in Google Search. Click-through rates on the standard organic results dropped meaningfully on informational queries.
2. ChatGPT, Claude, and Perplexity became real research tools. Procurement, legal, marketing, and engineering teams use them daily as a first stop.
3. AI crawlers have explicit identities. GPTBot, ClaudeBot, PerplexityBot, Google-Extended — they’re separate from traditional search crawlers, which means you control AI visibility independently from Google indexing.

If you’re not addressing AEO and GEO, you’re invisible to a layer of decision-makers that’s growing every month.

The technical foundation

The good news: most of the AEO and GEO work is the same underlying work, and it compounds with classic SEO.

1. Structured data (Schema.org JSON-LD)

The single highest-leverage change. Search engines and LLMs parse JSON-LD to understand entities, relationships, and content types.

Don’t just add an Organization block and call it done. Use a graph:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://example.com/#organization",
      "name": "Acme Consulting",
      "sameAs": [
        "https://www.linkedin.com/company/acme",
        "https://github.com/acme"
      ]
    },
    {
      "@type": "Person",
      "@id": "https://example.com/#founder",
      "name": "Jane Doe",
      "worksFor": { "@id": "https://example.com/#organization" },
      "knowsAbout": ["Cloud Architecture", "AEM", "DevOps"]
    },
    {
      "@type": "WebSite",
      "@id": "https://example.com/#website",
      "publisher": { "@id": "https://example.com/#organization" }
    }
  ]
}
</script>

The @id references let crawlers and LLMs link entities together cleanly. This is what’s called an entity graph, and it’s a major signal that you actually understand what your site is about.

Per-template schemas to add:

Page type	Schema type
Blog post	BlogPosting or TechArticle
Case study	TechArticle or CreativeWork
Service page	Service + OfferCatalog
About page	AboutPage with mainEntity linking to Person
Contact page	ContactPage with ContactPoint
Product page	Product + Offer + AggregateRating
FAQ	FAQPage with Question and Answer entries
Recipe	Recipe
Software/tool	SoftwareApplication

Every one of these has been validated by Google’s Rich Results Test and is parsed by major LLMs.

2. Breadcrumbs (visible + structured)

BreadcrumbList JSON-LD gives crawlers the site hierarchy. AI engines use this to understand what’s a “section” versus a “leaf” page, which affects how they cite content.

Always pair the structured data with visible breadcrumbs in the UI. Both signals reinforce each other.

3. Article metadata that AI engines actually use

Three fields move the needle disproportionately:

• datePublished — required for Article schemas to be valid
• dateModified — AI engines deprioritize stale content. Even minor edits should bump this.
• author with linked Person schema — provides E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness)

Include a visible “Last updated” date and a real author bio block at the end of every article. The bio should link to a /about/[author] page that has its own Person schema with sameAs to LinkedIn, GitHub, conference talks, or published works. This is the single biggest E-E-A-T signal you can give.

4. The llms.txt standard

Newer than the rest. The llms.txt spec proposes a markdown file at /llms.txt that gives LLMs a curated map of your most important content — like robots.txt for crawler permissions, or sitemap.xml for crawler discovery, but for LLM consumption.

Adopted by Anthropic, Mintlify, Vercel, Cursor, and a growing list of dev-tools and consultancies. Format:

# Acme Consulting

> Acme is a cloud-native consultancy specializing in AEM,
> DevOps, and AI implementation for enterprise clients.

## Services
- [Cloud migration](https://acme.com/services/cloud): ...
- [AEM consulting](https://acme.com/services/aem): ...

## Case studies
- [Toyota AEM Migration](https://acme.com/case-studies/toyota): ...
- [Abbott Platform Consolidation](https://acme.com/case-studies/abbott): ...

Generate it dynamically from your content collections so it stays accurate as you publish.

5. Explicit AI crawler permissions

Default User-agent: * rules apply to AI crawlers, but you can — and should — be explicit. AI bots respect their own user-agent strings:

# OpenAI
User-agent: GPTBot
Allow: /
User-agent: ChatGPT-User
Allow: /

# Anthropic
User-agent: ClaudeBot
Allow: /
User-agent: anthropic-ai
Allow: /

# Perplexity
User-agent: PerplexityBot
Allow: /

# Google Gemini (separate from Googlebot)
User-agent: Google-Extended
Allow: /

# Apple Intelligence
User-agent: Applebot-Extended
Allow: /

# Common Crawl (training corpus for many LLMs)
User-agent: CCBot
Allow: /

Explicit allows are a positive signal. They also let you selectively block AI training while still allowing AI search (block GPTBot but allow OAI-SearchBot, for example).

6. Content patterns

Schema is the wrapper. Content is what gets quoted. The patterns that matter:

• Lead with the answer. A “TL;DR” or summary block at the top of an article is what AI engines pull into snippets and quotes.
• Question-style headings. “What is solution architecture?” is more citable than “Solution Architecture.”
• Lists and tables. LLMs favor structured information. A 5-row comparison table will be cited far more often than the same content in prose.
• Cite your numbers. Statistics with sources make content quotable. AI engines prefer claims they can verify.
• Define key terms early. A glossary or definitions block helps LLMs disambiguate your domain language.

Use cases by company type

The technical work above applies broadly, but the priority order changes based on what you sell.

B2B and consulting firms

Highest leverage. AEO and GEO are nearly free lead generation.

Procurement teams, technical buyers, and decision-makers heavily use AI assistants for vendor research. If a CTO asks Claude “who are good consultancies for an AEM upgrade in North America,” you want to be in the answer.

Priority:

1. Person schema with knowsAbout for every senior consultant, with sameAs to LinkedIn and conference talks. This is your E-E-A-T moat.
2. Service + OfferCatalog schema on services pages, listing every distinct service.
3. TechArticle schema on case studies with detailed technologies, datePublished, dateModified.
4. Detailed llms.txt that maps your services, case studies, and team expertise.
5. Glossary content for your domain — “What is headless commerce?”, “What is a content lake?” — these become referenced answers.

SaaS companies

Documentation is your AEO/GEO surface area.

Most SaaS searches are how-do-I questions. AI engines lift answers from documentation more aggressively than marketing pages.

Priority:

1. HowTo and FAQPage schema in docs — Mintlify and ReadMe do this by default. If you’re rolling your own docs, add them manually.
2. SoftwareApplication schema on the homepage and feature pages, including applicationCategory, operatingSystem, and pricing.
3. llms.txt with a dedicated section linking every doc page, plus a llms-full.txt containing your entire docs corpus in markdown for LLM ingestion.
4. Versioned content — dateModified on every doc page tied to the actual release date.
5. Pricing transparency — Offer schema with explicit prices. AI engines avoid quoting “contact us for pricing” pages.

E-commerce

Product schema is non-negotiable; AI shopping is the new frontier.

Google AI Overview already surfaces product comparisons. ChatGPT can browse and recommend. Tools like Perplexity Shopping are formalizing AI-driven purchasing.

Priority:

1. Product + Offer + AggregateRating on every PDP. Include gtin, mpn, brand, and shipping/return details.
2. BreadcrumbList for category navigation.
3. FAQPage for product Q&A — this is what gets cited in “is product X compatible with Y?” prompts.
4. Real-time availability in your Offer schema. Stale inventory data is worse than none.
5. Comparison tables in content, with consistent structured headings — these are heavily lifted by AI engines.

Local services (legal, medical, trades, real estate)

LocalBusiness schema + Google Business Profile + reviews.

Voice search is huge here. “Find me a plumber near me” still routes to local results. AI assistants increasingly aggregate Google reviews into recommendations.

Priority:

1. LocalBusiness subtype schema (Plumber, LegalService, MedicalBusiness, etc.) with geo, openingHoursSpecification, priceRange.
2. AggregateRating sourced from real reviews.
3. Service entries for every distinct offering with a service-area definition.
4. FAQPage answering common local questions (“How quickly can you respond to an emergency call?”). These get pulled into voice responses.
5. Conversational landing pages for common questions — “best emergency plumber in Calgary” — written in answer-first format.

Publishers and media

Article schema, author authority, and freshness signals.

If you publish for a living, this is existential. AI engines are increasingly the front door for news and analysis.

Priority:

1. NewsArticle schema with full author, publisher, and section metadata.
2. Author pages with strong Person schema including sameAs to professional profiles and bibliographies.
3. dateModified that reflects real updates — AI engines penalize stale content aggressively.
4. isPartOf links to a parent Periodical or Publication entity.
5. Strict canonical URLs to prevent AI engines from confusing originals with syndications.

The validation checklist

After implementing, validate with these tools:

Tool	Tests for
Google Rich Results Test	Schema validity for Google features
Schema.org Validator	Generic schema correctness
Bing Webmaster Tools	Indexing for Bing/ChatGPT search
PageSpeed Insights	Core Web Vitals (still indirectly affects AI ranking)
Manual: ask ChatGPT, Claude, Perplexity about your topic	Are you cited?

The last one matters most. Run the queries your customers actually ask. See if you’re in the response. If not, the technical foundation isn’t enough — you also need content that answers those exact questions.

What we built for this site

We recently rolled out AEO and GEO improvements for howlingglobal.com — partly to walk our own talk, partly because the consulting practice depends on being discoverable to technical buyers.

What shipped:

• Entity graph linking Organization, Person, and WebSite with proper @id references
• Article schema on blog posts and case studies with datePublished and dateModified
• Service + OfferCatalog on the services page enumerating every consulting offering
• AboutPage + ContactPage with structured contact points
• BreadcrumbList schema and visible breadcrumbs across all tech and blog pages
• Author bio block with linked Person reference at the end of every article
• /llms.txt auto-generated from current content collections
• Explicit AI crawler allows in robots.txt for GPTBot, ClaudeBot, PerplexityBot, Google-Extended, Applebot-Extended, CCBot, and others

The implementation is live now. Run a query like “Adobe Experience Manager consultant in Calgary” through Perplexity in a month and see if we show up. We will report back.

The bottom line

AEO and GEO are not new disciplines layered on top of SEO. They’re the natural evolution of search-friendly content for an era when the engine doing the answering is generative.

Three takeaways:

1. Schema is the foundation. A clean entity graph with linked Organization, Person, and content schemas is the single highest-leverage change.
2. Content patterns matter as much as markup. TL;DR blocks, question-style headings, lists, and tables are what AI engines actually quote.
3. Identity is destiny. AI engines build mental models of who you are based on your sameAs, knowsAbout, and citation patterns. Investing in author authority and entity clarity pays dividends across every AI surface.

If you’d like help auditing your AEO/GEO posture or implementing the changes — particularly schema architecture, content pattern overhauls, or llms.txt generation pipelines — get in touch.