Synthetic Data for ML Teams
Generate synthetic training data that keeps real records private
Synthpylon produces statistically faithful datasets that match your production schema, so teams can train and share models without exposing personal information.
Privacy law didn't stall your data. It exposed the gap
- Production databases off-limits for dev and test environments
- HIPAA and GDPR block sharing patient or customer records with ML contractors
- Minority classes too rare to train classifiers reliably
- Analyst teams can't run aggregate queries on personally identifiable data
These aren't edge cases. They stop entire ML pipelines. We built Synthpylon because every workaround we tried (masking, sampling, access controls) still left the real data at risk, or left the model underfed.
The answer isn't a stricter access policy. It's data that was never real to begin with.
From schema to synthetic dataset in minutes
Connect your schema
Point Synthpylon at your database schema or upload a sample CSV (we never store raw records). We learn the statistical fingerprint: distributions, correlations, cardinality.
Set your generation rules
Choose row count, class balance targets, rare-event oversampling, and any column constraints. Your domain, your rules.
Export and ship
Download as CSV, Parquet, or JSON. Or stream directly into your training pipeline via our API. The output dataset contains zero real records.
What makes synthetic data usable
Schema-preserving generation
Output columns match your input schema exactly: same types, same referential constraints. Foreign keys stay coherent. No downstream pipeline breakage. Connect via JDBC, DDL upload, or CSV sample.
Statistical fidelity scoring
Every dataset ships with a fidelity report: marginal distributions, pairwise correlations, and a downstream-model-accuracy delta vs. training on real data.
Privacy proof by design
No real records stored, no reversible transformations. Differential privacy budget configurable. Formal membership-inference attack resistance built in.
Rare-class amplification
Fraud transactions, rare diagnoses, edge-case sensor readings. Generate as many minority-class rows as your model needs, without inventing unrealistic examples.
Where teams use Synthpylon
ML training data at scale
Run training jobs on synthetic datasets that match your production distribution, without touching production.
Learn morePrivacy-safe analytics
Enable analyst teams to query aggregate trends on synthesized records. Share findings without sharing PII.
Learn moreDev and test environments
Seed staging databases with realistic-looking data that passes validation rules. No more sanitized data that breaks integration tests.
Learn moreWhat ML teams say
We had 800 labeled fraud samples in production. Synthpylon got us to 12,000 synthetic rows with matching distribution. Our recall on the minority class jumped from 54% to 81%.
Our HIPAA data couldn't leave the hospital firewall. With Synthpylon, our contractors train on synthetic EHR data outside. The research velocity doubled.