Is any of the data real?

No. Every name, email, subject, and ticket ID is randomly assembled from built-in word banks. Emails use reserved test domains like example.com so they can never reach a real person.

What fields does each ticket include?

Each ticket has an ID, subject, description, requester name, email, priority, category, status, plus created and updated ISO timestamps within the last 30 days.

Can I use this for load or integration testing?

Yes. Generate up to 100 tickets at a time and copy the JSON into your fixtures or seed data. Run it repeatedly to build a larger combined dataset.

Are the timestamps realistic?

Yes. Each createdAt falls within the last 30 days and the matching updatedAt is always equal to or later than it, so chronological ordering stays valid.

Does anything get uploaded?

No. Generation happens entirely in your browser with no network calls, so the data never leaves your device.

Support Ticket Generator

The Support Ticket Generator builds batches of realistic but entirely fake customer support tickets. It is built for engineers, designers, and product teams who need believable seed data to demo a helpdesk, populate a Zendesk-style staging environment, or stress-test a ticket pipeline without touching any real customer records.

How it works

Each ticket is assembled from curated word banks. A sequential, zero-padded ID is generated (for example TKT-10003), then a subject, free-text description, requester name, priority, category, and status are each chosen at random from realistic options. The requester’s email is derived from their name using a reserved test domain, so it can never deliver mail to a real inbox.

Timestamps are generated so they always stay consistent:

createdAt is set to a random moment within the last 30 days.
updatedAt is offset forward from createdAt by 0–5 days.
Both are emitted as ISO 8601 strings, so they sort and parse cleanly.

Click Generate tickets to redraw the whole batch, and Copy JSON to grab the array.

Tips and notes

The output is a plain JSON array, so it pastes directly into seed scripts, fixture files, or mock servers.
Generate up to 100 at a time; run it several times and concatenate the arrays to build a larger dataset.
Priorities, categories, and statuses use common helpdesk vocabulary (Open, Pending, In Progress, Resolved, Closed), so they map onto most ticketing systems.
Everything is local — no API key and no network request — so you can regenerate as often as you like.