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First, use Firecrawl to crawl our sitemap at [sitemap URL] and extract every live URL across the site. Then use the Peec MCP to pull the URL report for our domain broken down by date in 30-day windows for the last 90 days. Match every sitemap URL against the Peec data. For each URL, assign a status: Rising if retrieval rate is increasing, Stable if the change is less than 10 percent, Declining if retrieval rate is falling, or Never Retrieved if there is no Peec data for that URL. Output the results as a Google Sheet with columns for: URL, page type, current retrieval rate, 30/60/90-day trend, status flag, and citation count. Include instructions for scheduling a weekly Claude task so the dashboard stays current automatically.
Most teams only monitor the pages they are already paying attention to. The pages they think are important, the ones they recently updated, the ones with the most backlinks. But AI search does not care about any of that. It might be heavily retrieving a 3-year-old support article while your flagship product page is completely invisible. It might be citing a comparison page that uses outdated positioning while your best case study goes unread.
This workflow maps your entire content footprint against actual AI retrieval behavior: every page, every status, updated weekly. For the first time you can see the full picture: what AI is reading, what it is ignoring, and where the most important gaps and opportunities actually are across your entire site.
