Technology
The Dataset OS
A unified operating layer for synthetic dataset creation, versioning, quality analysis and experiment tracking.
Start FreeWhat it is
Not a data tool. An operating system for datasets.
AI teams need to manage datasets the same way software teams manage code — with versioning, reproducibility, quality gates and experiment tracking. The Dataset OS provides all of this in a unified platform.
Blueprint as schema definition
Define the structure, distributions and constraints of your dataset in a versioned Blueprint.
Dataset versions as artifacts
Every generated dataset is a versioned artifact with full provenance — Blueprint version, seed, quality score.
Experiments as first-class objects
Track which dataset configuration produced which model performance — and reproduce any run exactly.
Core components
Blueprint Engine
The schema definition and generation engine.
- \u219210+ field types, 8 statistical distributions
- \u2192Two-tier constraint system (hard + soft)
- \u2192Computed fields with dependency resolution
- \u2192Versioned with parent-child lineage
Generation Pipeline
A chunk-based streaming pipeline that generates datasets at any scale without memory constraints.
- \u2192CHUNK_SIZE = 10,000 rows
- \u2192CSV, JSONL and Parquet export via streaming writers
- \u2192Peak RAM ≤ 256 MB for 5M+ row runs
- \u2192Two modes: Sandbox and Training
Quality Engine
Automated quality scoring, distribution analysis and gap detection.
- \u2192Composite 0–100 Quality Score
- \u2192Row-level scoring with per-row violation flags
- \u2192Distribution match against Blueprint specification
- \u2192Gap Analysis with actionable recommendations
Experiment Registry
Version-controlled experiment tracking that links dataset versions to model performance metrics.
- \u2192Blueprint version + seed locked per run
- \u2192Quality Score and violation rates recorded
- \u2192Side-by-side comparison across runs
- \u2192Export any run dataset for downstream use