Synthetic Data Generator-synthetic data generation tool for AI.

Example

A user uploads several CSV files extracted fromSynthetic Data Generator Intro an HR system containing Employees, Departments, and JobHistory tables. The assistant reads the schema, identifies primary and foreign keys, determines column semantics (e.g., job titles, salary bands, locations), and constructs a clear model for synthetic generation.

Scenario

Useful in situations where analysts or engineers receive partial or messy data structures from legacy systems and need a consistent, inferred model before generating synthetic data for testing or demo purposes.

Example

Using PyTorch distributions, the assistant can generate financial transaction amounts that follow log-normal patterns or user behavior metrics that mimic natural clustering. For instance, session durations may be modeled with a gamma distribution, while customer ages may reflect demographic proportions.

Scenario

Applied in machine-learning model testing, fraud-detection pipeline validation, or simulation environments where numerical attributes must behave like real-world data—for example, ensuring that 5% of transactions fall into high-value ranges.

Example

For a healthcare appointment dataset, the assistant uses Faker to generate patient names aligned to gender attributes, realistic appointment reasons (e.g., 'Follow-up consultation after MRI review'), and medically relevant time windows, such as appointments clustering on weekdays.

Scenario

Ideal for creating demo dashboards, user acceptance testing (UAT) datasets, or training environments where textual fields and categorical attributes must feel authentic to the domain—e.g., recruiting platforms, insurance claims workflows, logistics tracking, or CRM systems.