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The Content Health page audits your website — and your competitors’ sites — for the signals that influence whether AI platforms can read, understand, and cite your content. When you open the page, Pharos automatically discovers your site’s pages from its sitemap and runs a full audit on each one. Results appear in the Overview tab, and the Tools tab gives you generators for robots.txt, schema markup, and llms.txt.

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How audits work

When you navigate to the Content Health page, Pharos:
  1. Reads your brand’s domain and the domains of any competitors you have added
  2. Fetches each domain’s sitemap to discover individual pages
  3. Runs a content health audit on each discovered page in sequence
You will see a progress banner at the top of the page while the audit is running. If the audit service is temporarily unavailable, a Retry button lets you restart it without navigating away.
Audits run automatically on your first visit for a new brand or when you add a new competitor with a domain. Subsequent visits use cached results from the previous audit. To re-audit specific pages, use the re-audit button on individual rows in the audit table.

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Tabs

The Overview tab contains the main audit results table and summary metrics.

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Summary metrics

At the top of the tab, four aggregate metrics give you a quick read of your site’s overall AI readiness:
MetricWhat it measures
Average AI Readiness ScoreComposite score across all audited pages, 0–100
Average freshnessHow recently your audited pages were updated, on average
Average RAG scoreHow well your pages are structured for retrieval-augmented generation
URLs blocking AI botsPages where robots.txt prevents AI crawlers from indexing

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Audit table

Each row in the table represents one audited page URL. The columns include:
  • URL — the page that was audited
  • AI Readiness Score — a composite 0–100 score combining freshness, RAG readability, E-E-A-T signals, and technical accessibility
  • Freshness score — how recently the page was updated; stale content (older than 30 days) scores lower
  • RAG score — how well the page’s content can be extracted and used by retrieval-augmented generation systems; influenced by heading hierarchy, extractable summaries, fact density, and structured data
  • E-E-A-T score — signals of expertise, authority, and trustworthiness: author schema, about page, contact page, privacy policy, HTTPS, and organization schema
  • Last modified — the date of the most recent content change detected on the page
  • Status — whether the page is reachable (HTTP 200), redirected, or unreachable
Click any row to open the audit detail sheet for that page.

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Audit detail sheet

The detail sheet for a page shows:
  • All individual signal checks (heading hierarchy validity, extractable summary presence, FAQ schema, organization schema, llms.txt, robots.txt AI bot permissions, sitemap presence, HTTPS)
  • Schema completeness scores for each schema type present on the page, with a list of which required fields are present and which are missing
  • AI improvement suggestions — if an LLM assessment ran successfully, you will see a set of specific, prioritized suggestions for improving this page’s AI discoverability

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Issue alerts

Below the summary metrics, Pharos surfaces grouped issue alerts for common problems:
Pages whose robots.txt disallows known AI crawlers. AI platforms cannot index or cite pages they are blocked from reading. Fix this by updating your robots.txt to allow AI bots, or use the Robots.txt generator in the Tools tab.
Pages that have not been updated in more than 30 days. AI platforms tend to prefer fresh, recently updated content. Review these pages and update any outdated information.
Domains that do not have an llms.txt file. This file tells AI systems which parts of your site are safe to use for training and citation. Use the llms.txt generator in the Tools tab to create one.
Pages with a RAG score below 50. These pages may have unclear heading structures, lack extractable summaries, or have low fact density. The detail sheet for each page lists the specific issues.
Pages where heading tags (H1, H2, H3) are used out of order or skipped. A logical heading hierarchy helps AI systems parse the structure of your content and extract relevant sections.

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Competitor benchmarking

When you have competitors with domains configured in Pharos, their pages are audited alongside yours. The audit table shows both your pages and competitor pages so you can compare AI Readiness Scores and identify specific technical advantages your competitors have.
If a competitor consistently scores higher on the RAG score, check their detail sheet to see which structural elements they have implemented that you have not — FAQ schema and clear extractable summaries are common differentiators.