Schema, Structured Data, and Technical GEO
AI systems cannot cite content they cannot read.
The most aggressive content strategy collapses if AI crawlers cannot reach the content. Roughly 73% of websites have crawlability issues that block at least one major AI crawler. The brands solving the technical layer first see step-function citation lift before any content optimization.
Schema markup is the highest-leverage structural change a content team can make. FAQ schema produces a 350% citation increase across AI platforms, the largest single-format effect in the citation literature. HowTo, Article, and Organization schemas activate rich result eligibility on Google and improve entity grounding for AI systems across the board.
Beyond schema, the newer file conventions (llms.txt and llms-full.txt) give AI crawlers a curated guide to your most important content, and structured data audits surface the gaps between what AI systems can see and what your site actually publishes. This pillar covers all of it.
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Should You Remove FAQPage Schema From Your Site?
Google killed FAQ rich results on May 7. The FAQPage schema itself is not deprecated, and it is now a primary AI citation signal across every major LLM.
How to Run an HTML Parity Audit for AI Retrieval on JavaScript-Heavy Sites
A page can look perfect in the browser and still fail AI retrieval if the answer, proof, links, or schema only show up after hydration. This guide shows you how to run the HTML parity audit that catches the gap.
A Brand With Zero Domain Authority Ranked Top-3 in ChatGPT in 14 Days. Here Is the Playbook.
Otterly.ai built a fictional GEO agency with 7 pages, no schema, and zero domain authority. Then they built 16 off-page citations across 14 days. The result was rank #7 against established competitors, top-3 in ChatGPT, position 2 in Google AI Overviews, and 90 brand mentions. Here is the exact placement mix, the platform-by-platform breakdown, and what the 74% prompt-specificity finding means for content teams.
How to Build a GEO Release Checklist for Template Changes, Schema Parity, and Prompt QA
Most teams QA page releases for rendering and rankings. Fewer QA whether template, schema, and content changes quietly break AI retrieval. This guide shows you how to build the release checklist that catches those failures before and after launch.
How to Build a GEO Schema Deployment Matrix for Service, Pricing, Comparison, and Expert Pages
Most schema advice stops at validation. Strong GEO teams need a deployment matrix that maps each page type to the right markup, visible proof, and QA checks before structured data ships at scale.
How to Build a GEO Evidence Ledger That Keeps AI-Cited Pages Fresh
Most teams know when an AI-cited page slips. Fewer know exactly which proof asset expired, who owns it, and where it needs to be updated. This guide shows you how to build a GEO evidence ledger that keeps answer blocks, pricing pages, case studies, and expert pages credible week after week.
How to Run a GEO Internal Linking Audit That Supports AI Citation and Conversion Pages
Most GEO teams audit prompts, pages, and schema. Fewer audit the links that connect proof assets to money pages. This guide shows you how to fix that with a practical internal-link workflow.
How to Protect AI Retrieval During a Site Migration: Redirects, Canonicals, and Prompt QA
Most site migration checklists stop at rankings and broken links. This guide shows you how to preserve AI retrieval during a migration by protecting page purpose, redirect logic, canonical control, proof assets, and post-launch prompt QA.
How to Run an AEO Schema Audit That Aligns Entities, Answers, and Proof
Most schema work still stops at validation. This guide shows you how to audit schema for answer-engine performance by checking entity clarity, visible-answer parity, proof support, and page-type markup across the pages that drive AI visibility.
Google Just Defined a Second AEO. Here's What 'Agentic Engine Optimization' Means for Your Content.
Addy Osmani, Google Cloud AI's Director of Engineering, published a formal framework defining 'Agentic Engine Optimization' as distinct from Answer Engine Optimization. It covers five pillars, specific token budgets, and file-based discovery methods like llms.txt and AGENTS.md. Here is what B2B SaaS content teams should do about it.
How to Run a GEO Crawlability Audit That Improves AI Retrieval
A lot of teams keep publishing answer-engine content on top of weak technical foundations. This guide shows you how to audit crawlability, canonicals, internal links, sitemaps, and structured context so the right pages can actually be retrieved and reused by AI systems.
FAQ Schema Boosts AI Citations by 350%: What Otterly's 1 Million Citation Study Found
Otterly analyzed 1 million AI citations and found FAQ schema markup produces a 350% citation increase. The bigger finding: 73% of websites have crawlability issues that prevent AI systems from reading their content at all.
llms.txt: What It Is, What It Does, and Whether Your Site Actually Needs It
llms.txt is getting a lot of attention in GEO and AEO circles, but most of the commentary swings between hype and dismissal. Here's what the file actually does, what it cannot do, and when it is worth implementing.
Pillar FAQ
Common questions on Schema, Structured Data, and Technical GEO
The questions buyers ask AI before they evaluate vendors. Each answer is structured to be cited.
- How much does FAQ schema actually lift AI citations?
- Otterly's analysis of one million AI citations found that FAQ schema produces a 350% citation increase compared to unstructured content. It is the largest single-format structural change a content team can make for AI visibility, and it requires no content rewrites, only adding the schema markup to existing FAQ sections.
- Do AI systems actually read llms.txt?
- Yes, increasingly. Major AI crawlers including OpenAI, Anthropic, and Perplexity respect llms.txt as a content discovery signal even though it is not yet a formal standard. The companion file llms-full.txt, which contains the full text of key pages in plain Markdown, lets AI systems ingest your knowledge base in a single fetch instead of crawling individual URLs.
- What technical issues most often block AI citations?
- JavaScript-rendered content that AI crawlers cannot execute, missing or stale sitemaps, robots.txt blocks against specific AI crawlers (often inherited from older configurations), and pages with no semantic HTML structure are the most common technical citation blockers. Fixing these is usually the highest-ROI work in the first month of any GEO program.
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