Start with the work you need to do, then follow every finding back to the crawl evidence behind it.
Find technical issues, group repeated patterns, and prioritize the fixes that affect the most important parts of a site.
Find thin, overlapping, isolated, stale, or off-topic pages and decide what to improve, merge, redirect, or remove.
Find semantically related but disconnected pages, then generate source-and-target recommendations grounded in page content.
Compare two crawled sites to uncover stronger topics, missing coverage, structural differences, and internal linking opportunities.
Compare crawl history before and after a launch to catch missing pages, broken links, metadata regressions, and structural drift.
Review topical focus, chunk structure, headings, evidence, and the passages an AI retrieval system is most likely to surface.
Create evidence-backed audits, briefs, sheets, and visual summaries without rebuilding crawl context in another tool.
Use semantic coverage to decide which topics need deeper support, which pages should connect, and what content should be created next.
Find systematic technical, structural, and content problems across product, category, article, location, and programmatic page templates.
Crawl a prospect, compare competitors, and turn the strongest opportunities into an evidence-backed opening analysis.
Ask crawl data and ship grounded work.
Semantic clusters with issues layered in.
Persistent renders and scheduled re-scrapes.
Connect crawl context to external agents.
Sync scrapes for MCP and team access.