About SETC
Data shouldn't be the limit on AI.
SETC exists so that teams can build better models without being held back by the data they can collect, label, or safely use.
Our mission
Make high-quality training data something you can design.
Most AI projects stall on data, not algorithms. The examples that matter most are rare, labeling is slow and expensive, and the most valuable data is often the most sensitive.
We believe synthetic data changes that equation. When data can be generated to specification — with the right structure, labels, coverage, and privacy properties — teams can spend their time on models instead of on waiting.
Principles
How we work.
Useful over impressive
Synthetic data is only valuable if it makes models better. We measure success by downstream results, not by how realistic a sample looks.
Measure, don't assume
Every dataset should come with evidence. We evaluate against reference data and are candid about where synthetic data helps and where it doesn't.
Privacy is a design constraint
We treat privacy as part of how data is generated, not as a step added at the end.
Fit the team's workflow
Great tools disappear into existing pipelines. We build for the formats, tools, and habits ML teams already have.
Get started
Let's talk about your data.
Whether you're exploring synthetic data for the first time or have a specific model in mind, we'd like to hear what you're working on.