Email-to-Ticket Parser

The problem this kills

Every email in your shared support mailbox starts the same way: you read it twice, hunt for the order number buried under a signature and three layers of quoted reply history, guess at the customer's name, copy-paste it into your ticketing system, pick an issue type, and only then start actually helping. That ritual eats the first ten minutes of every single email. Multiply it across a busy mailbox and you've lost hours a day to clerical copy-paste — work that is boring, error-prone, and beneath your team.

Worse, the manual approach creates quiet mistakes: a fat-fingered order number, the wrong customer matched, a duplicate ticket because two agents grabbed the same forward. Each one costs more time downstream than it saved.

What you'll build

A small, private web app that turns a raw support email into a clean, structured ticket draft — with a human firmly in control.

• Paste an email (subject + body + sender) or forward one in, attachments and all.
• The AI strips out signatures, quoted history, and reply chains, then extracts the customer, order/account number, issue type, and sentiment.
• It matches the sender and the extracted order ID against your list of known customers and orders — using the exact code pattern your business uses (like SO-#####).
• You see a structured draft, fix anything that's off, and approve. Only then is the ticket created — or exported as a CSV row in the exact columns your system expects.
• The original email is stored untouched as the source of truth, so you can always check the parse against reality.

What's inside the Implementation Plan

This isn't a generic template. The plan opens by interviewing you about your business — your mailbox, your ticketing system, the real shape of your order numbers, your typical and peak email volumes, your triage rules, and your messiest edge cases. It reads back a short tailored spec, waits for your thumbs-up, and only then shapes the data model, the matching logic, and every later build step around your answers.

Inside you'll find:

• The full discovery interview, with concrete questions to answer.
• A step-by-step build, where every step ends with a ready-to-paste prompt for your AI coding agent.
• A clean data model for emails, parsed tickets, and your customer/order reference list.
• Parsing logic that handles reply chains, signatures, and quoted history.
• The "No API yet?" fallback: import a Google Sheet / CSV and export approved tickets as CSV — so the tool is fully usable today, even with no integration to your current system.
• A verification checklist so you know it actually works before you trust it.

The governance it includes (this is the point)

The reason this is safe to put in front of customers' data is the governance baked in from step one:

• Login so only your team can open the tool.
• Row-level security so people only ever see their own organization's data.
• A complete audit trail — who parsed what, who approved it, and exactly when.
• A hard human-in-the-loop approval gate — the AI only ever drafts. A person reviews the matched customer and extracted order ID and approves before any ticket is written.
• Duplicate guards — a dedupe key (message-id, or sender + subject + timestamp) means the same forwarded email can't quietly become two tickets.

Who it's for

• Agents working a shared support mailbox who are tired of copy-paste.
• Team leads who want consistent, well-structured tickets without nagging.
• Ops coordinators standing up a lightweight intake process before (or instead of) a heavyweight help desk.

You don't need to be a developer. You need your real support emails, your customer list, and a willingness to answer a few questions about how your team actually works.

You've got this — paste the first prompt and let the interview tailor the tool to your mailbox.