What is AEO and GEO and how you can get benefit with Scrunch in Sitecore.
Nowadays, people do not enter queries on Google, but ask ChatGPT, Gemini, Perplexity, and Claude directly to provide them with the answers, and these systems are determining which brands to include, and which don't. For developers building on Sitecore, this isn't just a marketing conversation anymore. It's becoming a content architecture and technical implementation problem, and that's exactly where AEO, GEO, and Sitecore's newly acquired platform, Scrunch - come in.
In this post, we'll break down what AEO and GEO actually mean, why they matter to the people building the site and not just the people writing the copy, and how Scrunch- now part of the Sitecore ecosystem gives developers concrete ways to make content machine-readable without disrupting the human experience.
By the end, you'll understand the moving pieces well enough to have an informed conversation with your content and marketing teams, and know where your own implementation fits in.
Section 1: What is AEO (Answer Engine Optimization)?
Use case: Your team ships a well-structured product page. A user asks ChatGPT "what's the best headless CMS for a mid-size retailer" and your brand never comes up even though your content answers that question perfectly on the page.
Overview
AEO is an optimization methodology whereby the content is made to be extracted by AI-based search and chat interfaces directly as part of their answer generation, instead of simply being ranked in blue links. Traditional SEO used to focus on keywords and backlinks. AEO focuses on the ability of an AI engine to find an exact and confident answer in your content and use it as its source.
This means the need for more efficient structuring: proper headings, answers near the start of each section, correct use of schema markup, and content that reads properly and makes sense as an answer rather than five paragraphs you must read in order to comprehend the answer.
This is an example of structured data that will enable answer engines to understand the content of your webpage:
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is Answer Engine Optimization?",
"acceptedAnswer": {
"@type": "Answer",
"text": "AEO is the practice of structuring content so AI-powered search tools can extract and cite it directly in generated answers."
}
}]
}
Benefits of getting AEO right:
- Higher chance of being the cited source inside an AI-generated answer
- Reduced reliance on click-through traffic as the only measure of visibility
- Forces cleaner, more scannable content structure, which also improves human UX
- Positions your brand as an authoritative source AI systems trust and reuse
Section 2: What is GEO (Generative Engine Optimization)?
Use case: A prospective client asks Gemini to compare three DXP vendors. The generative engine synthesizes an answer from dozens of sources and your competitor's content shapes the summary because it was easier for the model to parse and trust.
Overview
GEO is the broader discipline sitting alongside AEO. Where AEO focuses on being the direct answer to a specific question, GEO is about optimizing your entire content footprint so generative AI models represent your brand accurately and favorably whenever they synthesize information even when your content isn't the single cited source.
This is an example of the trend towards shifting from old-fashioned SEO to AI-powered answer creation and is changing the way agencies and content creators approach their visibility strategy. From a developer perspective, GEO means choices around semantic HTML, structured data for entire content types (not only FAQs), sitemap and robots.txt settings for AI crawlers, and API-level content availability for the AI systems to consume it.
An elementary tweak to the robots.txt file that developers are often requested to make to regulate access for AI crawlers:
User-agent: GPTBot
Allow: /products/
Allow: /blog/
User-agent: PerplexityBot
Allow: /products/
Allow: /blog/
Benefits of getting GEO right:
- Consistent, accurate brand representation across multiple AI platforms, not just one
- Better long-term content ROI as generative answers become a primary discovery channel
- Cleaner separation between what's crawlable by AI systems and what isn't
- A content model that scales as new answer engines enter the market
Section 3: This is a Developer Issue, Not Just a Marketing Problem
Use case: Marketing queries why the brand doesn’t appear in AI responses. The true answer is not always “the content is bad” but "the content is stuck in the DAM, PDF, or component that is never surfaced as machine-readable markup."
Overview
Good AEO and GEO work assumes the content already exists the real bottleneck is discoverability, not content volume. Valuable information often sits in digital asset management systems, PDFs, or internal repositories that AI crawlers simply can't parse. That's a content architecture problem, and content architecture is developer territory.
This is where things like component-level schema, headless content APIs, and clean data modeling in Sitecore start to matter for AI visibility, not just for site performance or SEO.
A quick example of exposing a Sitecore content item with structured markup at the component level:
public class ProductSchemaViewModel
{
public string Name { get; set; }
public string Description { get; set; }
public string SchemaType => "Product";
public string ToJsonLd()
{
return JsonSerializer.Serialize(new
{
context = "https://schema.org",
type = SchemaType,
name = Name,
description = Description
});
}
}
Benefits of treating this as a dev responsibility:
- Structured data gets baked into the component library instead of bolted on later
- Content authors don't need to manually manage schema for every page
- AI crawler access can be controlled at the route or template level
- One structured content model can serve web, AI answers, and future channels
Section 4: What Scrunch Actually Does
Use case: Your team wants to know, concretely, where your brand is losing visibility in AI answers before spending months restructuring content.
Overview
Scrunch gives visibility into buyer queries, brand representation, and competitive positioning across large language models like ChatGPT, Gemini, and Perplexity. It runs content gap analysis to identify where a brand lacks visibility for important prompts, and checks whether the missing information already exists somewhere in the organization's content repositories.
Sitecore acquired Scrunch in mid-2026, folding generative-engine-optimization tooling directly into its enterprise content platform. Practically, that means Scrunch isn't a bolt-on reporting tool, it's designed to plug into the content operations teams are already running in Sitecore, surfacing where structured content is missing or underperforming.
Benefits for developers working alongside Scrunch:
- Clear, prioritized signal on which content types need structured data work first
- Content gap reports that point back to specific repositories or content models
- Fewer guesswork-driven schema or crawler-access decisions
- A feedback loop between what AI engines are citing and what gets built next
Section 5: How Scrunch Fits into a Sitecore Implementation
Use case: You're planning a new Sitecore build or a redesign and want AI visibility considered from day one instead of retrofitted after launch.
Overview
Scrunch goes beyond monitoring, it can reformat content for AI systems automatically, without changing what human visitors see. For a developer, that translates into a few practical implementation touchpoints: structured content types that render both a human-facing view and a machine-readable payload, crawler-access rules managed alongside your existing routing, and a content model flexible enough to support the FAQ, product, and how-to schema types AI engines rely on most.
None of this requires reinventing your Sitecore architecture. It's mostly about being deliberate: modeling content so structured data is a byproduct of good component design, not a separate project.
Benefits of building this in from the start:
- Avoids a costly retrofit of schema and structured data later
- Keeps content authors focused on writing, while structure is handled at the template level
- Gives marketing and engineering a shared source of truth for AI visibility performance
- Future-proofs the build as new answer engines and AI crawlers emerge
Best Practices for AEO and GEO in a Sitecore Build
- Bake schema markup into component templates rather than adding it page-by-page
- Keep answers to common questions concise and near the top of the relevant section — don't bury them in paragraph four
- Explicitly manage AI crawler access in robots.txt rather than leaving it to defaults
- Treat your DAM and internal repositories as content sources that need exposing, not just storing
- Review content gap reports regularly instead of treating AEO/GEO as a one-time setup
- Keep structured data and human-readable content in sync, don't let one drift from the other
Conclusion
AEO and GEO aren't passing trends, they reflect a real shift in how people find information, and that shift is happening at the infrastructure level, not just the content level. Answer engines reward brands that make their content easy to parse, trust, and cite, and that's a technical challenge as much as a strategic one.
Scrunch's move into the Sitecore ecosystem gives developers a much more direct line between "what AI engines are asking for" and "what we actually build." Instead of guessing at schema requirements or crawler rules, teams get real signal on where structured content is missing and can prioritize accordingly.
If you're planning a new build or a redesign, this is the moment to fold AI visibility into your content architecture decisions, not something to retrofit six months after launch.
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