Placeholder index report for assessing whether major websites are technically legible to AI crawlers and answer engines.
Key findings
Crawl access is only the first gate.
The final index should distinguish pages that are technically accessible from pages that are actually clear, sourced, structured, and usable as answer material.
Important content often hides behind interaction.
Expandable panels, scripts, blocked assets, and thin source context can make otherwise strong pages less useful to AI crawlers and retrieval systems.
Readiness should be scored at page and site level.
A single blocked template can affect hundreds of pages, so the methodology should separate template-level issues from isolated content gaps.
Index purpose
The finished index should give technical, content, and executive teams a shared way to evaluate whether high-value pages are ready to be crawled, parsed, understood, and cited by modern answer systems.
The HTML detail page is the canonical version because it can expose methodology, definitions, and internal links directly to crawlers. The PDF can package the same findings for stakeholders who prefer a document.
Recommended scoring dimensions
Recommended dimensions include robots access, render dependency, important text availability, schema coverage, author/source clarity, internal-link context, canonical hygiene, and whether key answers are self-contained on the page.
The final report should avoid treating the score as a black box. Each dimension needs a short definition, a visible example, and a practical improvement path.
Technical use cases
This report can support technical SEO audits, CMS migration planning, AI-readiness reviews, and executive conversations about why crawlability still matters in an answer-engine world.
Once real index data is added, this page can become a natural citation target for articles about AI crawl readiness and technical SEO in 2026.
Methodology placeholder
- Sample representative home, service, article, and resource templates.
- Check crawler access, canonical state, render dependency, and visible source context.
- Separate sitewide template issues from individual page issues.
- Publish a scoring rubric that technical teams can reproduce.