The 3C Framework - Code: The Technical Foundation for AI Search Visibility

Part 2 of the 3C Framework Series

Can AI find your website? Can it understand your content? Can it parse your entities and relationships? If the answer to any of these is no, nothing else matters – not your brilliant content, not your industry reputation.

The Code pillar is about machine-readability. It's the technical foundation that determines whether AI systems can even access, process, and cite your content. Without it, you're invisible by default.

Why Is Schema Markup the Most Important Technical Factor?

Schema.org structured data is the "language" machines understand best. It transforms your website from human-readable text into machine-processable entities with defined relationships.

Research on Retrieval-Augmented Generation (RAG) directly supports this. When information is structured as entities with relationships, AI systems can retrieve and reason about it more effectively [1][2]. Your website should function as an "API for AI systems" [3].

The most important schema types for AI visibility include Organization (establishes your entity identity), Article (structures blog posts and guides), Product (for e-commerce), FAQPage (creates extractable Q&A pairs), and Person (for author identification and E-E-A-T signals).

Research from King's College London and the University of Oxford on schema generation confirms the ongoing importance of structured data for machine understanding [4]. The Semantic Web research community has consistently shown that explicit entity definitions improve automated reasoning [5].

What Role Does Technical SEO Play?

Technical SEO covers the foundational elements that ensure AI systems can properly identify and parse your pages. This includes title tags, meta descriptions, heading hierarchies, canonical URLs, and language declarations.

They're logical prerequisites: a page without a proper title or with conflicting canonical signals creates confusion for any automated system trying to index and cite content.

Think of technical SEO as hygiene factors. A single H1 per page, a logical heading hierarchy (H1 → H2 → H3), proper language tags, and clean canonical URLs don't guarantee AI visibility – but their absence can prevent it.

How Do Core Web Vitals Affect AI Visibility?

Here's an honest assessment: no direct GEO research has measured the impact of Core Web Vitals on AI visibility. The connection is indirect but logical.

Fast, stable sites tend to be crawled more completely. AI systems that use web crawling for their retrieval pipelines may process faster-loading pages more thoroughly. And Google's own AI Overviews inherit quality signals from the traditional ranking system, where Core Web Vitals are a factor.

The key metrics remain: LCP (Largest Contentful Paint) ≤ 2.5 seconds, CLS (Cumulative Layout Shift) ≤ 0.1, FCP (First Contentful Paint) ≤ 1.8 seconds, TTFB (Time to First Byte) ≤ 800ms.

Aim for "good" scores, but don't expect dramatic AI visibility changes from performance alone.

Why Does Crawlability Matter for AI Systems?

AI systems can only cite content they can access. Crawlability ensures your pages are discoverable and indexable by AI crawlers – not just Googlebot, but also GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, and others.

This means checking your robots.txt for AI crawler access, maintaining a current XML sitemap, ensuring proper indexing directives, and verifying that important pages aren't accidentally blocked.

No direct GEO evidence here either, but the logic is straightforward: a page that can't be crawled can't be cited. Period.

What About Accessibility and Security?

These two areas deserve an honest assessment. They're included in the framework as grouping labels for related checkpoints – not because there's direct evidence for their AI impact.

Accessibility has one important exception worth noting. Readability, measured by Flesch score for example, actually has direct GEO evidence – "Fluency Optimization" boosts visibility by 15-30% [6]. Other accessibility checkpoints work differently. Alt texts provide context for images. Semantic landmarks improve structure recognition. Plausible benefits, but indirect.

Security evidence is even thinner. HTTPS functions as a general trust signal. CSP and HSTS headers are established best practices, but nobody has measured their AI connection. These checkpoints signal technical diligence. Nothing more.

We've included these areas because they belong in a complete technical audit. But expectations should match: here you're optimizing for technical quality, not direct AI visibility gains.

How Should You Prioritize Code Optimizations?

Remember: this series covers the AI Readiness Lens. Through this lens, schema markup ranks highest because RAG research directly supports its impact. Core Web Vitals rank lower – important for user experience, but with only indirect AI evidence.

A Classic SEO Lens would flip these priorities. Core Web Vitals and title optimization would rank high. Schema markup would still matter, but less dominantly.

The checkpoints themselves are constant. What changes is how you weight them based on your goals. In enhancely.ai, you can switch lenses or customize weights to match your priorities.

Schema markup automation offers the fastest ROI with manageable investment. It's where Code optimization often starts.

Summary for the Code Pillar

The Code pillar is the technical foundation for AI visibility. Schema markup has the strongest evidence – that's where effort pays off most. Technical SEO, performance, and crawlability are necessary hygiene factors. Accessibility and Security round out the picture but don't have directly measurable AI impact.

The good news: Code optimizations are often quick to implement and scalable. Schema markup can be automated, performance has clear metrics, technical audits are well-documented.

In the next part, we cover the Content pillar – where the strongest direct GEO evidence lies and the largest visibility gains have been measured.

FAQ

    • What is schema markup and why does it matter for AI search?

      Schema markup is structured data that tells AI systems what your content means, not just what it says. Using Schema.org vocabulary in JSON-LD format, you define entities (Organization, Person, Product, Article) and their relationships. This helps AI systems understand your content as structured information rather than just text.

    • Why are Accessibility and Security included in the 3C framework if they don't directly affect AI visibility?

      Through the AI Readiness Lens, they're best-practice groupings with low weights. One exception: Readability has direct GEO evidence (+15-30% visibility). Other checkpoints signal technical quality. Through an Accessibility Lens, these same checkpoints would rank high. The checkpoints exist – the lens determines their priority.

    • Which schema types are most important for AI visibility?

      Start with Organization schema to establish your entity identity, then add content-type schemas matching your pages: Article for blog posts, Product for e-commerce, FAQPage for Q&A content, Person for author profiles. The more schema types you implement correctly, the better AI can process your site.

    • How quickly can I implement Code optimizations?

      Schema markup can easily automated via enhancely (even for your complete composable stack )and show effects within weeks once AI crawlers capture updated pages. Technical SEO fixes are usually straightforward. Performance optimization varies – simple fixes are quick, but major improvements might require infrastructure changes.

    • Do I need perfect Core Web Vitals for AI search?

      No direct GEO research has measured Core Web Vitals impact on AI visibility. However, fast, stable sites tend to be crawled more completely and signal technical quality. Aim for "good" scores (LCP ≤2.5s, CLS ≤0.1, FCP ≤1.8s).

    • Can enhancely.ai help with Code optimization?

      Yes. enhancely.ai automates schema markup generation across multiple CMS platforms and technologies.

The Complete Series

  1. Introduction to the 3C Framework – What, why, and the three pillars overview
  2. Code: Technical Foundation – Schema, Technical SEO, Performance, Crawlability
  3. Content: AI-Ready Information – GEO Methods, Statistics, Knowledge Graph
  4. Credibility: Building Trust – Person Signals, Organization Signals, Earned Media

Sources

[1] Peng, B., Zhu, Y., Liu, Y., et al. (2024). Graph Retrieval-Augmented Generation: A Survey. Peking University, Zhejiang University. https://arxiv.org/abs/2408.08921

[2] Gao, Y., Xiong, Y., et al. (2024). Retrieval-Augmented Generation for Large Language Models: A Survey. Tongji University, Fudan University. https://arxiv.org/abs/2312.10997

[3] Generative Engine Optimization: How to Dominate AI Search (2025). https://arxiv.org/abs/2509.08919

[4] Zhang, B., He, Y., Pintscher, L., Meroño Peñuela, A., & Simperl, E. (2025). Schema Generation for Large Knowledge Graphs Using Large Language Models. King's College London, University of Oxford. https://arxiv.org/abs/2506.04512

[5] Scherp, A., Groener, G., Škoda, P., Hose, K., & Vidal, M.-E. (2024). Semantic Web: Past, Present, and Future. Ulm University, TU Wien. https://arxiv.org/abs/2412.17159

[6] Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization. IIT Delhi, Princeton University. KDD '24. https://arxiv.org/abs/2311.09735