Extract Fields From Emails with AI
Sender, company, request, deadline — out of emails with quoted replies and signature blocks, using guidance that knows how email is actually read.
Overview
Email is hostile to extraction: the signal sits on top of quoted history, signatures repeat in every reply, and the actual request hides in politeness. This setup extracts sender_name, sender_email, company, subject, request, and deadline with email-specific reading guidance — prefer the newest message over quoted history, mine the signature for sender details — and a faithfulness rule on the request field: the sender's ask in at most two sentences, no interpretation added. Null discipline keeps the record machine-friendly for triage flows.
Workflow
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Paste the whole thread
The reading guidance handles quoted history — you don't need to trim the email first.
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Watch the request field
Faithful to the sender's wording, two sentences max, no interpretation — the field your routing logic can trust.
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Let null mark silence
No deadline stated means deadline: null — never "probably end of week".
Why This Works
- Newest-message-first guidance defeats the quoted-history trap
- Signature mining gets sender details email bodies rarely state outright
- The faithfulness rule keeps the request field evidence, not summary
Best for
- Shared inboxes where every mail needs the same triage fields
- Threads with deep quoted history and repeated signatures
- Routing flows that act on the request field downstream
Not for
- Deciding the email's category or priority bucket — that's classification
- Drafting the reply — extraction feeds that step, it isn't that step
Use cases
- Triaging a shared inbox into structured request records
- Pulling sender details from signatures without grabbing quoted history
- Capturing deadlines as ISO dates for follow-up automation