AI Overviews now appear in 43% of Google searches, up from 15% a year earlier. When they show up, click-through rates drop by nearly 60%. The content that survives this compression carries enough trust signals that both Google's quality raters and AI citation engines treat it as worth surfacing. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is the framework behind those signals. You can build a checker for it in a single afternoon using Claude Code.
Why E-E-A-T Deserves an Automated Checker
E-E-A-T isn't a score Google hands you. There's no API endpoint, no PageSpeed-style readout. Google publishes a framework; humans (and increasingly, AI systems) apply it. That gap between "framework" and "measurable" is where most B2B SaaS teams lose the thread. They know author bios matter, they know trust pages matter, but nobody audits them systematically. Pages go stale. Author profiles link to people who left the company two quarters ago.
A 2026 study of 15,000 AI citations found eight E-E-A-T signals with statistically significant correlation to AI citation rates. Two of the most actionable: increasing citation density to 8–12 authoritative external sources per article, and using complete Article schema markup. Those are binary checks. Either the page has them or it doesn't. That's the kind of signal an automated checker handles well.
Think of it as an accessibility audit for credibility. Standardized scoring, a prioritized issue list, a retest cycle. Marketing Ops teams already run this pattern for ADA compliance. Same muscle, different rubric.
What the Checker Actually Checks
For B2B SaaS content, the signals worth auditing fall into four buckets:
- Authorship and credentials. Named authors with relevant titles, linked bios, verifiable experience. Does the page say who wrote it and why they're qualified?
- Product-specific evidence. Screenshots, workflows, integration details. Content that could only come from someone who's used the product. (This is the "Experience" in E-E-A-T, and it's the hardest to fake.)
- Third-party validation. Reviews, partner listings, certifications, case study references. External proof that someone besides you vouches for the claim.
- Transparency infrastructure. Contact pages, editorial policies, privacy disclosures, clear service descriptions. The trust plumbing most teams set up once and never revisit.
Building It in Claude Code: The Short Version
The build runs inside Claude Desktop using Claude Code. You need a Claude Pro, Max, Team, or Enterprise subscription (or separate API credits). The workflow:
- Feed Claude the E-E-A-T framework. Point it at Google's quality rater guidelines or your own distilled criteria. Claude processes the unstructured guidance into a scoring rubric. This is where AI genuinely earns its keep: turning a 170-page PDF into a structured checklist.
- Select representative pages. Don't crawl the whole site. Pick 12–15 pages covering your key page types: homepage, about page, author bios, a recent blog post, a guide, contact page, privacy policy, terms of use.
- Launch a headless browser. Claude Code spins up a Chromium-based headless browser through Python (Playwright or Selenium). This captures both raw HTML and rendered content, which matters because schema markup and dynamically loaded author info only show up in the rendered page.
- Run the audit and generate the report. Claude applies the rubric to each page and produces a scored audit. Review it, correct misapplied criteria, feed changes back. Because Claude stores context in local memory, each refinement improves future runs.
- Back up to GitHub. Push memory files, prompts, and scripts to a repo. If your machine fails, cloning the repo restores the project.
Rakuten reported cutting time to market for new features from 24 days to 5 days using Claude Code. League reported 50% shorter development cycles. Sii Poland saw first-pass correctness rise from about 33% to 84%. These are internal tooling builds, not consumer products. The pattern translates directly to content-quality tooling.
What This Won't Do
No checker guarantees improved rankings. E-E-A-T isn't a direct ranking factor; it's a quality framework that informs how Google evaluates content. The checker measures alignment with that framework. Useful benchmark, but a proxy, not a promise.
The other limitation: Claude Code generates plausible but sometimes incorrect output. Treat the output as a draft, not a verdict. Human-defined criteria, testing, and editorial review aren't optional steps you skip once the tool works. They're the governance layer that makes the tool trustworthy.
The hypothesis: if you run this checker against your top 20 pages and fix the flagged gaps (missing author credentials, stale legal pages, absent schema markup), then AI citation rates for those pages will increase within 60 days, because AI systems weight the same trust signals the checker audits. Success = citation appearances in AI Overviews for target queries. Guardrails = no drop in organic CTR for audited pages. Stop-loss = if organic traffic declines more than 10% within 30 days of content changes, revert and diagnose.
Somewhere in your content inventory right now, there's a privacy policy last updated in 2023 and an author bio linking to a LinkedIn profile that no longer exists. The checker will find them. What you do next is the part that matters.
