Turn any documentation site into a searchable AI knowledge base
A SaaS company transformed their public documentation and competitor sites into searchable MCP knowledge bases, enabling Claude to answer customer questions with exact citations and perform competitive analysis autonomously. MCPner.com's website crawler eliminated manual content updates and stale information.
The Problem
Support teams manually searched through 500+ documentation pages to answer customer questions, often referencing outdated content. Competitor research required engineers to read through multiple documentation sites and manually compile feature comparisons. Product teams lacked visibility into what competitors were launching.
How MCPFlow Solved It
They used MCPner.com's website crawler to index their entire documentation site (docs.company.com) and 5 major competitor sites. The crawler automatically detected updates and re-indexed changed pages. An MCP server was deployed with vector search capabilities, enabling Claude to perform semantic search across all crawled content. Support agents connected the MCP endpoint to Claude Desktop, instantly gaining access to searchable knowledge from internal docs and competitor sites.
MCP Stack Used:
Measurable Outcomes
Customer support response time dropped 67% as agents received instant, accurate answers with source citations. The competitive intelligence team discovered 12 new competitor features in the first month through automated monitoring. Documentation accuracy improved as the AI flagged outdated or contradictory content during searches.
Key Metrics:
We went from Ctrl+F warriors to having Claude search our entire docs and competitor sites in seconds. Game changer for support and product intel.
Alex Ruiz
Director of Customer Success, DevTools Inc
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