Analyze EEAT Using AI

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) has become a non-negotiable ranking signal. Yet actually measuring it—page by page, against real-world competitors—still eats up hours of manual audits, spreadsheets, and gut-feel scoring. Our “Analyze EEAT” app flips that workload on its head: drop in a URL (plus up to three competitors) and let Moonlit handle the scraping, topic mapping, gap finding, and 18-point EEAT scoring for you.

How the app is wired up

Step 1 – Collect every word that matters

Four Scrape Webpage nodes run in parallel—one for your page, three for competitors. We pull the complete raw text (no truncated “main content” filters) so the later models can judge depth, originality, and citation quality without missing context. This method shares many similarities with techniques discussed in our auditing website content at scale with AI article.

Step 2 – Map the conversation & surface missed angles

• Topic Table (Claude Haiku - Chat Model) reads all four texts and lists every talking point it can spot—numbered, no omissions. This becomes the master topic inventory.

• Information Gain Ideas (GPT-4o) compares the same material and spits out ten useful angles nobody covered. Perfect prompts for adding first-hand experience or fresh data your rivals forgot.

Step 3 – Quantify coverage and EEAT quality

• Topic Coverage Analysis (GPT-4o Mini) converts the topic list into a JSON table showing which text nails each point (✓) and which misses (X). Instant visual of where your page under-delivers.

• EEAT Analysis (GPT-4o Mini) grades all four texts against 18 criteria pulled straight from Google’s Quality Rater Guidelines—originality, factual accuracy, bylines, etc.—scoring 0-10. Because the node returns strict JSON, you can chart or pipeline the data anywhere.

Scores are based on Google’s Quality Rater Guidelines. Higher is better (max 10).

Step 4 – Action-ready improvement brief

The final Chat Model (GPT-4o) stitches everything together—topic gaps, information-gain ideas, and low-scoring EEAT checks—into a plain-language report outlining exactly what to add, fix, or expand. Each recommendation references a competitor that did it better, so your writers know what “good” looks like. This approach builds on strategies discussed in our building high-quality AI content pipelines post.

Ways to tailor this app

• Add sentiment or readability scoring. Drop a Text Analysis function between Steps 1 and 2 to run sentiment analysis.

• Swap in different models per budget. If you’re auditing 1,000 pages, switch the heavier GPT-4o nodes to LaMMa models to cut cost while keeping structure intact.

• Layer in author data. Pre-scrape author bio pages and pass them as extra inputs so the EEAT scorer can judge credentials more accurately.

• Focus on YMYL sensitivity. Insert a filter that flags medical or financial claims lacking citations, then surface them in the final report.

Running at scale with Bulk Runs

1. Prepare a CSV with columns: Page URL, Competitor 1 URL, Competitor 2 URL, Competitor 3 URL.

2. Open Bulk Runs, choose “Analyze EEAT,” upload the file, and map each column to its matching input.

3. Hit Run. Moonlit queues each row as a job—scrapes, scores, and outputs downloadable JSON tables plus human-readable reports for every page.

4. Export the combined results to CSV or push them directly into Data Studio for portfolio-level EEAT dashboards.