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Platform

The SynthVault Platform

From raw schema to validated synthetic data — one integrated system.

01 Ingestion Engine

Connect anything. Understand it instantly.

The Ingestion Engine reads your source systems and builds a complete structural model — schemas, foreign keys, constraints, and cross-table relationships — automatically. Structured, semi-structured, and time-series data are all first-class citizens.

  • Automatic schema detection — column types, cardinalities, null patterns, and inferred semantics.
  • Relationship mapping — foreign keys and referential constraints discovered and preserved across tables.
  • Read-only by design — the engine never writes to your source systems.

Supported Sources

SnowflakeWarehouse
BigQueryWarehouse
PostgresDatabase
MySQLDatabase
S3Object Store
KafkaStreaming
DatabricksLakehouse

+ CSV, Parquet, JSON, and Avro flat files

02 Synthesis Core
generate.py
from synthvault import Client

sv = Client(api_key="...")

dataset = sv.generate(
    source="production_db.users",
    privacy_budget=1.0,
    model="auto"
)

# Validate before you ship
report = dataset.validate()
print(report.fidelity_score)  # 0.997

The right model, chosen for you.

CTGAN, TVAE, CopulaGAN, or diffusion models — the Synthesis Core profiles your data and auto-selects the optimal architecture, then tunes hyperparameters against fidelity targets. Categorical, numerical, temporal, and free-text columns are handled natively.

CTGANTVAECopulaGANDiffusionTabular Transformers

No ML expertise required. The system selects and tunes the optimal model.

03 Validation Suite

Prove fidelity. Prove privacy. Before anyone touches the data.

Every dataset ships with a validation report: statistical fidelity metrics and adversarial privacy tests, generated automatically and exportable for compliance review.

  • Fidelity metrics — KL divergence, KS tests, and correlation matrix comparison against source.
  • Privacy leakage tests — membership inference, attribute disclosure, and reconstruction attack simulation.
  • Drift detection — continuous monitoring of synthetic output against evolving source distributions.
VALIDATION REPORT — users_synth_v3 PASSED
Fidelity Score
0.997
MI Attack AUC
0.501
Epsilon Spent
ε 1.0
age
0.992
income
0.985
region
0.999
churn_flag
0.971
04 Distribution Layer

SynthVault Feeds

ML Training PipelinesAirflow / Kubeflow
Staging & Dev DatabasesDirect Push
Vendor SandboxesScoped Access
CI/CD Test FixturesREST API

Ship synthetic data anywhere, with governance attached.

Export to Parquet or CSV, push directly to warehouses, or stream through the API. Webhook notifications keep pipelines in sync, and role-based access controls decide exactly who sees what — with a full audit trail behind every export.

  • Formats — Parquet, CSV, JSON, or direct warehouse push.
  • Orchestration — native integrations for Airflow, Prefect, and Kubeflow.
  • Governance — RBAC, scoped API keys, webhook events, immutable audit logs.

Deployment & Compliance

Your environment. Your rules.

SOC 2 Type II

Independently audited controls across security, availability, and confidentiality.

HIPAA BAA + GDPR

Business Associate Agreements and Data Processing Agreements available on Enterprise plans.

Zero Data Retention

Source data never persists in SynthVault infrastructure. Cloud jobs run in ephemeral containers.

Encryption Everywhere

AES-256 at rest, TLS 1.3 in transit. On-premise and VPC deployment options.

Read the full security posture

See the platform on your own schema.

Connect a source, generate a dataset, and review the validation report — in one session.