CrossRow generates synthetic datasets that behave like the real thing: ledgers reconcile, foreign keys resolve, state machines walk legal paths, distributions hold. Not a single byte of customer data leaves your perimeter.
Not screenshots. This data is being generated now. Watch the invariants: cities match countries and currencies, running balances foot to the penny, statuses only move along legal transitions. CrossRow verifies every rule after generation and puts the results in your quality report.
Random values in the right format fool nobody, least of all your integration tests. CrossRow generates each row in the context of every other row, then runs an independent verification pass and scores the output.
balance = prev + amount holds on every row, and the closing balance lands where the plan says it should.delivered before it’s picked_up.manager_id and parent_category_id, with realistic org depth and null rates.tax = gross × 0.22, date offsets, string expressions). Numeric columns follow the distribution you asked for (lognormal, Benford, seasonal), and the quality report proves it.Bring a schema, pick an industry template, or describe the dataset in plain English. CrossRow drafts the full generation plan.
Semantic analysis infers types, correlations, and constraints. Automatic plan review catches contradictions before a single row is generated.
Streaming generation with live progress: from a 50-row sample to hundreds of millions of rows, straight to S3, a database, or files.
An independent validator re-checks every invariant and scores the run. Wire the output into CI, demos, load tests, or ML pipelines.
Generate a sample in minutes. Keep the compliance team bored.