Content Depth Score: What AI Engines Actually Want to Read

Content depth score measures whether your pages treat their claimed topics with the thoroughness AI engines expect from a primary source. This isn't a word count metric. It isn't a readability score. It's an AI-evaluated judgment: "Would this satisfy someone who actually needs to understand this topic?"

Here's how the evaluation works. Your page content gets fed into an AI model alongside the topic it claims to cover. The model assesses three dimensions -breadth (how many relevant subtopics you address), depth (how far each subtopic goes beyond surface-level treatment), and completeness (whether critical aspects are missing). A page titled "Complete Guide to Live Chat Software" that only covers three features and skips pricing, integration, and support comparisons? It'll score poorly regardless of how well it's written.

Content depth and content length are different animals. A 3,000-word article stuffed with filler scores lower than a focused 1,200-word piece that covers every essential dimension with specific data points. AI engines have been trained on millions of documents across every subject area. They've got strong implicit expectations about what thorough coverage looks like. When your content falls short, they treat it as a secondary source.

The scoring also calibrates for topic scope. A page about "What is live chat?" has narrower expected depth than "Complete Live Chat Software Comparison 2026." The AI evaluation adjusts its expectations to match what your content promises in its title, headings, and opening paragraphs.

Traditional content audits measure surface-level proxies for depth: word count, heading count, image count, reading time. None of these actually measure whether the content covers its topic. A 5,000-word article can be shallow if it circles the same three points repeatedly. A tight 800-word piece can be remarkably deep on a narrow topic.

AI-level testing matters because it mirrors exactly what happens when ChatGPT or Claude decides whether to cite your page. These models don't count words. They evaluate whether your content adds meaningful information to the answer they're constructing. When a user asks "What live chat software is best for small businesses?" -the AI compares your content against everything it knows. If your page covers the same ground as dozens of other pages without going deeper, it's got no reason to cite you specifically.

The depth gap between competing sources is often the deciding factor in AI citation. Two pages about patient advocacy services -one lists five benefits in generic bullet points, the other explains each benefit with a real scenario, quantifies the impact, and addresses common objections. The second page gets cited because it gives the AI something concrete to extract. The first is redundant with information the AI already has.

Here's what this looks like with real data. Tidio (63) has strong schema markup but may find that AI engines only cite it 20% of the time for queries where it should appear. The technical markup gets Tidio discovered, but content depth determines whether the AI actually uses what it finds. This gap between discoverability and citability -that's precisely what the content depth score reveals.

The Intelligence Report evaluates content depth through a multi-stage AI pipeline. First, it identifies the core topic of each page by analyzing title, H1, meta description, and opening paragraphs. This establishes expected scope -what the page promises to cover.

Next, the system constructs a topic model. It asks an AI engine to list the key subtopics, dimensions, and questions that a thorough treatment should address. For a page about "live chat software comparison," the expected dimensions might include feature comparison, pricing tiers, integration capabilities, support quality, scalability, ease of setup, mobile support, and real user feedback. This becomes the benchmark your actual content gets measured against.

Stage three: your full page content gets fed to the AI alongside the topic model. For each expected dimension, the AI assesses whether your content addresses it, how deeply it goes, and whether it provides specific evidence -data points, examples, comparisons -or just surface-level mentions. The result is a granular map showing which dimensions are well-covered, which are mentioned but shallow, and which are missing entirely.

Finally, scores get normalized into a 0-100 scale accounting for topic scope. A narrow-scope page (single question, specific how-to) is scored against narrow expectations. A broad-scope page (full comparison, ultimate guide) is scored against broad expectations. This prevents unfair penalization of pages that intentionally focus on a single aspect.

The report includes specific dimensions where your content falls short, with recommendations for what to add. These aren't generic suggestions -they're AI-identified gaps between what your page promises and what it delivers.

Above 80: your page covers its claimed topic thoroughly enough that AI engines treat it as a primary source. Scores in this range typically correlate with higher citation rates -the AI has enough substance to extract for its answers.

Between 50 and 80: partial coverage. Your page addresses the topic but leaves significant dimensions unexplored. These pages may get cited when the AI has no better option, but they lose out to deeper competitors. The report identifies the specific missing dimensions -and often, adding 2-3 paragraphs on the gaps can push a 65 to an 85.

Below 50: substantially thinner than what AI engines expect. This typically happens with landing pages masquerading as guides, listicles that scratch the surface of complex topics, or pages spending most of their word count on promotional copy. These pages rarely earn AI citations regardless of technical markup quality.

Pay particular attention to the gap between your depth score and your technical AEO score. A page with a technical score of 75 (good schema, clean HTML, proper headings) but a depth score of 40 has a structural problem -the AI can find and parse your content easily, but once it reads it, there's not enough substance to cite. Flip side: a page with depth 90 but technical 30 has rich content that AI engines struggle to discover. The Intelligence Report highlights these mismatches because they represent the highest-leverage optimization opportunities.

When reviewing depth scores across your site, look for patterns. Blog posts consistently scoring 70+ but product pages below 40? The issue is likely that product pages are written for conversion rather than information. Adding FAQ sections, detailed specs, and comparison content to product pages can close this gap without changing their commercial intent.