Synthloom produces millions of realistic, relationship-aware records in minutes — so your team can test, prototype, and demo with confidence. No coding required.
Automatic foreign key management ensures every generated relationship is coherent. Customers, Orders, Products — all linked correctly, every time.
A directed acyclic graph engine resolves entity ordering, detects circular dependencies, and unlocks parallel generation across independent layers.
Independent entity groups are generated concurrently, dramatically reducing wall-clock time for complex, multi-entity datasets.
Plug in OpenAI GPT-4 or Anthropic Claude 3 to generate context-aware descriptions, narratives, and realistic text fields — with result caching to control costs.
Export as CSV, JSON, Parquet, or write directly to a SQL database. Parquet output is optimized for big data pipelines; JSON supports per-entity split files.
Define entities, fields, constraints, and relationships in YAML. Rules are reusable, version-controllable, and shareable across teams.
Automated validators check foreign key constraints, uniqueness, value ranges, and custom business logic after every generation run.
Batch-based streaming generation means no dataset is ever fully loaded into memory — enabling billion-record generation on commodity hardware.
Declare your data model — entities, fields, types, and relationships — in YAML or via the visual Rule Editor.
The DAG engine topologically sorts entities and groups independent ones for parallel execution.
Records are produced in memory-efficient batches. IDs are cached to satisfy foreign keys downstream.
Optional LLM pass adds realistic copy, descriptions, and contextual text to marked fields.
Write output as CSV, JSON, or Parquet — ready for any downstream pipeline or analytics tool.
Automated validators confirm referential integrity, uniqueness, and business-rule compliance.
Open the app and start synthesizing data in under a minute.
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