Product
Improve dataset quality iteratively
Use insights and recommendations to refine blueprints, adjust distributions and close coverage gaps — without starting from scratch.
Why it matters
The first dataset is rarely the best
Optimization is the process of iterating on Blueprint parameters based on what the data and model metrics reveal. LiteSeed makes this loop structured and traceable.
Insight-driven iteration
Recommendations are derived from correlation analysis — not guesswork.
Override without rewriting
Apply parameter overrides to a new run without modifying the original Blueprint.
Full history
Every optimization step is recorded as a new run and dataset version.
Core capabilities
Recommendation engine
After running Data Intelligence on three or more experiments, LiteSeed generates specific recommendations for the next dataset version.
- Recommendations linked to specific dataset metrics
- Confidence score based on correlation strength
- Actionable parameter suggestions (row count, distribution adjustments)
Apply recommendation as new run
Apply a recommendation with one click. LiteSeed creates a new run with the recommended parameter overrides applied — without modifying the original Blueprint.
- Override requested_rows, distribution parameters, constraint thresholds
- New Blueprint version created automatically
- Original Blueprint preserved for comparison
Optimization history
Every optimization step is recorded as a new run and dataset version. The full optimization history is visible in the Lineage view.
- Full audit trail of every optimization step
- Before/after metric comparison
- Rollback to any previous Blueprint version
Distribution editor
Manually adjust field distributions in the Blueprint editor and save changes as a new Blueprint version.
- Visual distribution editor with histogram preview
- Save changes as a new Blueprint version
- Dirty state tracking — unsaved changes are clearly indicated