I built one workflow that triages inbound Gmail into noise, operational, and meeting paths. Meeting requests are split into clear versus ambiguous before any calendar logic runs. Ambiguous requests get a clarify reply. Clear requests use real calendar availability plus booking caps from Postgres.
Key outcomes
- Prevents duplicate processing with DB-backed dedupe
- Uses confidence thresholds to avoid low-quality sends
- Keeps outbound mail plain text and auditable
- Supports exact-match auto-booking only when conditions are safe
The space between a reply and a client is where most outreach dies. What moves a replier to a booked call for you: a value asset first, or a direct ask?
Processing incoming PDF attachments and logging key email data manually takes hours of tedious, repetitive work. Every time a new invoice, quote, or report lands in your inbox, you have to manually open the email, download the PDF, read through dense pages to extract important details, format the data, and log it into Google Sheets row by row.
Which one is faster, saves more time, and prevents mistakes?
The manual-vs-automated framing is clear, but I'm curious how the workflow copes with PDFs that don't share a layout, like scanned invoices versus quotes. Do you parse them with a fixed template or an extraction step, and what happens to rows it can't read confidently?
Resolve: an AI support agent that answers from a company’s own policies, looks up orders, and escalates weak matches to a human instead of guessing. A human approves replies before they go out.
For the human approval step, I'd show the exact policy passage and its effective date beside the draft. Does Resolve flag a reply for re-review if the policy changes while it waits for approval?