Improve ML Model Accuracy With Labeled, High-Coverage Synthetic Data
Rockfish generates privacy-safe, statistically consistent, labeled datasets that close both labeling and coverage gaps — enabling ML teams to train and test models with richer patterns, balanced anomalies, and better ground truth.
Why ML teams struggle today
- Too few anomalies in real data
- Manual labeling of spikes and events
- Low coverage of rare or dangerous patterns
- Endless retraining loops
- Weeks/months waiting for new real data
- Privacy barriers to customer datasets
The Data Gaps that hurt Model Performance
Rockfish Delivers the Data Fuel ML Teams Need
Realism
Maintain correlations, seasonality, and temporal coherence
Compliance
Fully privacy-safe synthetic datasets.
Flexibility
Inject anomalies and domain-specific patterns
Coverage
Generate rare events and long-tail behaviors.
Labeled Data, Enhanced Coverage, Better Accuracy
- Accurate labels (normal vs anomaly)
- Normal + abnormal behavior distributions
- Temporal consistency
- Endless retraining loops
- Unlimited data generation (days, weeks, months)
- Works with schema-only or schema+sample
Elevate Your ML Models With Labeled, High-Coverage Synthetic Data
Synthetic data that improves accuracy, reduces false alarms, and accelerates ML deployment.