How We Use Claude to Triage Incoming Sales Inquiries

AI summary · key takeaways

An enterprise deployed a Claude-powered intake bot to qualify sales leads, achieving over 90% accuracy in extracting structured data from unformatted emails and successfully routing inquiries. After three months in production, the team found that automated data extraction and urgency detection worked well, but open-ended question generation created friction with users and was removed after six wee • AI-powered lead qualification can achieve 90%+ accuracy in extracting structured fields from unstructured email content • Automated urgency detection and intelligent routing to appropriate technical resources proved effective in production • Open-ended question generation by AI bots creates user friction and should be avoided in favor of more targeted data extraction • Iterative refinement based on production data is critical—removing unsuccessful features like conversational questioning improved overall performance

We built an internal Claude-powered intake bot to qualify incoming leads. After three months of production we have data on what works and what doesn't.

What worked: extracting structured fields from messy email bodies (90%+ accuracy). Detecting urgency. Routing to the right engineer.

What didn't: open-ended question generation. The bot loved to ask — humans hated to answer. We removed it after week six.

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How We Use Claude to Triage Incoming Sales Inquiries

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