Where synthetic data unblocks your team
Three concrete problems ML and data teams face, and how Synthpylon solves each.
Use Case 01
ML training data at scale
The problem
Your labeled production dataset has 2,000 fraud transactions. Not enough to train a reliable classifier. Class imbalance pushes recall down to 50-something percent. You can't sample more real data without violating access controls or GDPR's data minimization obligations. Re-labeling is slow and expensive.
Common teams
- Fraud detection
- Risk modeling
- Healthcare AI
- NLP annotation augmentation
How Synthpylon solves it
Generate 50,000 synthetic fraud transactions with the same feature distributions as your real 2,000. Synthpylon fits a correlation-preserving model over your schema, then samples to your target class balance. You get a Parquet file. Your real records stay in the vault. The fidelity report ships with every batch so you can verify the marginal distributions before you train.
Use Case 02
Privacy-safe analytics for shared teams
The problem
Analysts need to run aggregate queries on customer or patient data. GDPR's data minimization principle and HIPAA's minimum necessary rule mean the full dataset can't leave the secure environment.
Common teams
- Product analytics
- Business intelligence
- Compliance reporting
- Academic research
How Synthpylon solves it
Run aggregate queries on synthetic records that preserve population-level trends and pairwise correlations. Findings are reportable. The underlying records never leave the secure environment. One limit to be clear about: synthetic data is not a substitute for individual-level analysis. If your question is "what happened to this specific patient," you need the real record. If your question is "what is the average lab result for this diagnosis code," synthetic data works.
Use Case 03
Realistic dev and test environments
The problem
Staging databases are seeded with hand-crafted dummy data that doesn't match production distributions. Integration tests pass on staging and break in production. Sanitized production copies are a compliance risk.
Common teams
- Platform engineering
- QA teams
- Data contractors
- CI/CD pipelines
How Synthpylon solves it
Seed staging and CI databases with Synthpylon-generated data that matches your production schema and distribution. Tests catch real edge cases. No PII in the pipeline.