Bookings that check out correctly.

Generate guests, properties, rooms and bookings where nights, rates and totals agree, channels and cancellations look real and every reference has the right format.

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The example

Upcoming arrivals across three hotels

What to look for in the screenshot above. These are the details that usually break in hand-made or random test data.

nights is the date difference between check-in and check-out, and total = nights × nightly_rate.
Rates fit the property and room: a Zermatt junior suite costs more than a Prague standard queen.
Channels and statuses follow realistic mixes, including OTA cancellations and a no-show.
Six-character booking references in the PNR style used across travel systems.

Built for Travel & Hospitality teams

Totals that match

Nights, rates, taxes and totals calculated per booking.

Seasonal demand

Occupancy and rates that rise in peak weeks and fall in shoulder season.

Full hospitality schemas

Guests, properties, rooms, bookings, stays, folios and reviews.

Realistic channels

Direct, OTA, corporate and travel-agent bookings with their own cancellation patterns.

How it works

From idea to dataset in three steps

Describe or upload

Describe your data in plain English, or upload your SQL schema for PostgreSQL, MySQL / MariaDB, SQL Server and SQLite.

Review the plan

Check the tables, columns and rules the AI proposes, and adjust anything you like.

Generate and download

Choose how many rows you need and download a ready-to-run SQL script, CSV, Excel (one sheet per table) or JSON.

Questions

Can I generate flights instead of hotels?

Yes. Describe flights, segments and passengers and you get matching tables.

Can I include reviews?

Yes. AI-written reviews can match each stay's room, rating and complaints.

Can rates vary by season?

Yes. Add seasonal rules and nightly rates follow them.