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arXiv AI By Benjamin Gruenbaum, Doron Porat, Assaf Natanzon, Roy Zavida, Chen Dinachi, Or Itzahary, Omer Niv

Generating a Consistent Enterprise: Synthesis and Reference-Free Evaluation of Multi-System Business Data

Read the original on arXiv AI →

The paper introduces a synthetic data generator that creates fully consistent, fictional enterprises—complete with workforce, customers, sales, support, and communication records—without relying on any real dataset. It validates realism through a five‑axis scorecard, an adversarial detector, and soundness checks, achieving a mean realism score of 99.1 across 23 generated companies. A second generator produces relational databases from business questions, ensuring qualifying rows and exact labels, and is available as a hosted service and containerized simulators.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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