Train on a twin,
not the original.
Doppelset is the synthetic data lab for AI teams. We learn the shape of your data, then generate billions of rows that behave like real ones — without any of the personal details.
real_customers.csv
| id | name | spend |
|---|---|---|
| EU-1042 | Elena R. | €812.40 |
| EU-1043 | Marc V. | €204.15 |
| EU-1044 | Ada S. | €1,209.00 |
| EU-1045 | Iván P. | €97.80 |
| EU-1046 | Mira K. | €548.10 |
synth_customers.csv
| id | name | spend |
|---|---|---|
| SY-0001 | Lucía O. | €799.12 |
| SY-0002 | Théo B. | €221.04 |
| SY-0003 | Klara F. | €1,118.65 |
| SY-0004 | Mateo C. | €104.20 |
| SY-0005 | Inês A. | €572.84 |
Trusted by data teams at
0
Real records ever stored
by architecture — verifiable
99.4%
Statistical fidelity vs source
median across 1,400 benchmarks
<8 min
From schema to 10M synthetic rows
p50 on standard schemas
How a doppel of your data behaves
We don't anonymize, we don't mask, we don't shuffle. We learn the joint distribution of your dataset and sample fresh records from it.
step 01
Point at your source
Connect a database, drop a CSV, or hand us a schema. Doppelset reads structure and stays within your network the entire time.
step 02
Train a doppel
Our hybrid model (GAN + diffusion + tabular transformer) learns the joint distribution of every column and relationship.
step 03
Sample fresh rows
Generate any number of records that statistically match the original. Adjust privacy ε, drift, balance — on demand.
step 04
Share with confidence
Export to S3, BigQuery, Snowflake, or stream over the API. Every batch ships with a signed quality + privacy report.
Everything an AI team needs to leave production data alone.
Privacy-by-default, schema-aware, and engineered to plug into the pipelines you already run.
Privacy Vault™
Automatic PII detection across 60+ entity types in 32 languages. Direct identifiers are removed before the model ever sees them.
Schema-aware synthesis
Doppelset reads your DDL, infers types, joins and constraints, then samples rows that respect every relationship and check.
Time-series engine
Generate hours, days or years of realistic temporal data with seasonality, drift and anomalies you can dial in.
Relational doppelgängers
Multi-table data with foreign keys, parent/child cardinalities, and referential integrity — out of the box.
Differential privacy
Mathematical privacy guarantees with ε you choose. We report the exact budget spent for every generation run.
Quality reports
Side-by-side distribution plots, correlation deltas, and downstream-model utility scores. Built for compliance review.
Python SDK + REST API
One pip install and three lines of code. Or hit the API from Airflow, dbt, Databricks, or your favorite notebook.
VPC & on-prem
Self-host Doppelset inside your VPC, Kubernetes cluster or air-gapped data center. We never see your real data.
LLM-ready
Generate diverse instruction sets, eval suites, and safe fine-tuning corpora that don't memorise your customers.
Three lines. Any pipeline.
Doppelset ships a fully-typed Python SDK, a Node SDK, a REST API, and connectors for the warehouses and orchestrators you already run.
- Snowflake
- Databricks
- BigQuery
- Redshift
- Postgres
- MySQL
- S3 / GCS
- Airflow
- dbt
- Kafka
- MongoDB
- Parquet/CSV
1from doppelset import Doppelset23client = Doppelset(api_key="ds_live_…")45# 1. learn the shape of your data6twin = client.train(7 source="postgres://prod-replica/customers",8 privacy=client.Privacy(epsilon=1.2),9 schema="auto",10)1112# 2. sample as many rows as you like13synthetic = twin.sample(rows=2_500_000)1415# 3. ship it — with proof16synthetic.to_parquet("s3://safe-bucket/customers_v3.parquet")17print(twin.quality_report().fidelity) # 0.994What teams say after their first doppel
Verified customers across health, finance, telecom, and retail.
We replaced a four-week DSAR process with a 12-minute notebook. Our model performance on the synthetic set is within 0.3 AUC of production.
Dr. Elin Hartmann
Head of Data Science · Northstar Health
Our fraud team finally has a shared dataset everyone — analysts, vendors, the regulator — is allowed to look at. That alone paid for the platform.
Joaquín Salas
VP, Risk Analytics · Norden Bank
Doppelset turned a 'no' from legal into a 'yes, by Friday'. Their differential-privacy report is the cleanest I've ever sent to a regulator.
Priya Nair
Chief Data Officer · Pulse Telecom
Why teams pick a doppel over the alternatives
A summary of how Doppelset's synthetic data compares to anonymisation, masking, and generic generators.
Ship faster. Stop arguing with legal.
Generate your first 100,000 synthetic rows in the next ten minutes. No credit card.