Scale on demand
Generate as many training examples as an experiment needs, without new collection or labeling cycles.
Synthetic data platform
Generate the training data your models are missing.
SETC creates realistic, labeled synthetic datasets — tabular, text, image, and time series — shaped around the scenarios your models need to learn, and checked against your reference data before they reach training.
Why synthetic
The bottleneck in AI isn't the model. It's the data.
Real-world data is slow to collect, expensive to label, uneven in coverage, and often too sensitive to use freely. Synthetic data lets you design the dataset instead of waiting for it.
Cover the long tail
Upsample rare classes and create scenarios your production data barely contains, so models learn the cases that matter.
Privacy by design
Records are generated rather than copied, so teams can work with realistic data while reducing how often real personal data has to move.
Data types
One platform for every modality your models learn from.
Structured data with real-world shape
Generate tables and multi-table datasets that keep the relationships between columns and entities your models depend on.
- Preserves distributions and cross-column correlations
- Keeps keys consistent across related tables
- Rebalance targets and rare segments on request
| customer | plan | tenure | label |
|---|---|---|---|
| c_1042 | pro | 18 mo | retain |
| c_1043 | starter | 2 mo | churn |
| c_1044 | team | 9 mo | upsell |
| c_1045 | pro | 31 mo | retain |
Language data for training and evaluating LLMs
Create instruction pairs, multi-turn conversations, and domain-specific documents tailored to the behavior you want your model to learn.
- Instruction and preference data for fine-tuning
- Evaluation and red-team sets for hard cases
- Controllable tone, domain, length, and difficulty
My invoice shows two charges this month. Why?
It looks like a plan change mid-cycle created a prorated charge. I can break down both line items for you.
Yes please.
Labeled imagery without the labeling queue
Generate image datasets with annotations attached, and vary the conditions — lighting, angle, background, occlusion — your models must handle.
- Bounding boxes, masks, and class labels included
- Controlled variation for robustness testing
- Target scenarios that are rare or unsafe to capture
Sequences with realistic dynamics
Generate time series and event streams with seasonality, trends, bursts, and anomalies for forecasting and detection models.
- Seasonality, trend, and noise that look like the real thing
- Inject labeled anomalies at controlled rates
- Simulate scenarios that haven't happened yet
How it works
From idea to training-ready dataset.
A workflow built around what your model needs to learn, not around what data happens to exist.
01
Define
Start from a sample of your data, a schema, or a description of the scenarios you need covered.
02
Generate
Produce synthetic records with the structure, labels, and variation your task requires.
03
Evaluate
Compare synthetic output against reference data and check it's useful before it reaches training.
04
Deliver
Export in the formats your pipeline already uses and iterate as your model improves.
Solutions
Built for the teams training tomorrow's models.
LLM fine-tuning & evaluation
Domain-specific instruction, dialogue, and evaluation data to teach and test the behavior you want.
Learn moreComputer vision
Annotated imagery across the conditions your model will face.
Learn moreTabular ML
Richer, balanced datasets for classification and prediction.
Learn moreRare events & edge cases
Enough examples of the unusual for models to actually learn it.
Learn moreModel testing
Stress-test models on controlled scenarios before they ship.
Learn moreFAQ
Questions, answered.
What is synthetic data?
Synthetic data is generated by models rather than collected from the real world. Done well, it reflects the patterns and structure of real data, so it can be used to train and test AI systems.
Will models trained on synthetic data work on real data?
It depends on the task, which is why we evaluate synthetic datasets against held-out real data for your specific task, and many teams combine synthetic and real data rather than replacing one with the other.
Do we need our own data to get started?
Not necessarily. You can start from a sample dataset, a schema, or a clear description of the scenarios you need. The more context you share, the closer the output will match your domain.
How is data delivered?
In the formats your pipeline already uses — for example CSV, Parquet, JSONL, or image folders with annotations. We'll agree on the format during scoping.
How do we start?
Get in touch and tell us about your model and data. We'll set up a demo around your use case and data.
From the blog
Notes on training data.
Get started
Ready to build your next dataset?
Tell us what your model needs to learn. We'll walk you through SETC on your use case, your data, and the metrics you care about.