Incoming mail needs fields extracted for automation, regex or AI?

For mails with fixed templates (confirmations, invoice notifications) regex or an email parser is faster, more predictable and cheaper. For free text (customer mails) an LLM is better, provided you force JSON output.

Try this first

1. 1Fixed template (Booking.com, bank notifications, supplier confirmations): use Mailparser, Parseur, or a Zapier email parser. Map once, low maintenance after.
2. 2Free text, but predictable fields (name, question, urgency): use an LLM (Claude, GPT) with a JSON schema and a short example prompt. Keep temperature low.
3. 3Combine where it makes sense: regex for headers (sender, date), LLM for body. That keeps cost lower than running every mail through an LLM.
4. 4Always validate LLM output: try JSON.parse and fall back to a fallback flow if parsing fails. Don't push through blindly.
5. 5Cost math: 1000 mails per month x 1k tokens x model price. At large volumes, a custom regex or trained classifier eventually pays off.

None of the above fits?

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