Product
Understand what your data actually contains
Automated quality scoring, distribution analysis and gap detection — so you know exactly what you're training on.
Why it matters
Data quality is model quality
Most teams don't know the actual quality of their training data until the model underperforms. LiteSeed surfaces quality issues before training — at the field level, row level and dataset level.
Catch issues before training
Identify distribution skew, constraint violations and coverage gaps before they affect model performance.
Field-level visibility
See the actual distribution of every field — not just summary statistics.
Actionable recommendations
Gap Analysis surfaces specific recommendations for improving dataset coverage.
Core capabilities
Quality Score
A composite 0–100 score computed from constraint compliance, distribution fidelity and coverage completeness.
- →Hard constraint violation rate (0% target)
- →Soft constraint violation rate (configurable threshold)
- →Distribution match against Blueprint specification
- →Coverage of rare event and edge case scenarios
Row-level scoring
Every generated row receives an individual quality score, enabling filtering, debugging and targeted regeneration.
- →Per-row constraint violation flags
- →Outlier detection for numeric fields
- →Low-quality row filtering before export
- →Score distribution histogram for the full dataset
Gap Analysis
Automated analysis of coverage gaps between the generated dataset and the Blueprint specification.
- →Identifies underrepresented enum values
- →Flags missing rare event scenarios
- →Recommends Blueprint adjustments to close gaps
- →Compares coverage across dataset versions