Background
B2B teams often struggle with:
- Volume spikes and scattered inboxes
- Quote requests, partnership interest, and complaints mixed together
- Follow-up timing left to memory
- CRM and email context out of sync
This use case turns email handling into a classify → draft → remind → log pipeline.
Key steps
- Fetch unread emails from the last 24 hours via a connected mailbox or API.
- Classify by urgency: urgent (complaint, contract, payment), important (quote, partnership), general (newsletter, low-priority).
- Generate reply drafts for important emails with personalized context.
- Create follow-up reminders and write results to a log or CRM fields.
Workflow sketch
Read unread mail → Classify (urgent / important / general) → Draft replies for important → Flag human review → Daily summary → Log to CRM
Skill recommendations
openclaw skills install @clawhub/tavily
openclaw skills install @clawhub/summarize
Use a mail-reading skill or API binding if your stack supports it. For first validation, use manual copy-paste with a fixed prompt template.
Expected results
- Urgent emails surfaced with clear owner assignment
- Important emails with ready-to-edit drafts
- General emails summarized and archived
- Follow-up items written to workspace or CRM
Common pitfalls
- Auto-sending sensitive or high-stakes emails without human confirmation
- Letting AI decide contract, refund, or commitment wording
- Running without audit logs when writing back to CRM
- Misclassification when thread context is long or fragmented
Related pages
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Official docs → docs.openclaw.ai
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Issues → GitHub Issues