ML engineers
Unblock training runs without waiting on collection or labeling.
Solutions
Synthetic data for every stage of the model.
From fine-tuning to stress-testing, SETC helps AI and ML teams get the data their models need — with more coverage and less dependence on sensitive records.
LLM training
Teach language models the behavior you actually want.
Off-the-shelf data rarely matches your domain, tone, or edge cases. Generate instruction, dialogue, and evaluation data built around your product and your users.
- Instruction and preference data for fine-tuning
- Multi-turn conversations in your domain
- Evaluation and red-team sets for hard or risky prompts
finetune_set.jsonltext
Can I move my annual plan to monthly without losing credits?
Yes — unused credits carry over. The switch takes effect at your next renewal date.
And if I downgrade instead?
Computer vision
Labeled images for the conditions your model will face.
Collecting and annotating images is slow, and the hardest conditions are the rarest. Generate annotated imagery with controlled variation instead.
- Boxes, masks, and class labels generated with the image
- Vary lighting, angle, weather, occlusion, and background
- Cover scenarios that are rare, costly, or unsafe to capture
scenes/batch_07vision
Tabular ML
Richer, balanced data for prediction models.
Churn, credit, fraud, and demand models all suffer from imbalance and thin segments. Generate structured data that fills the gaps while keeping realistic relationships.
- Keep distributions and cross-column correlations
- Rebalance targets and thin customer segments
- Multi-table datasets with consistent keys
accounts_synth.parquettabular
| account | segment | spend | label |
|---|---|---|---|
| a_2201 | smb | $1,240 | retain |
| a_2202 | mid | $8,905 | churn |
| a_2203 | ent | $42,110 | retain |
| a_2204 | smb | $612 | churn |
Edge cases
Enough of the rare stuff for models to learn it.
The events that matter most — fraud, failures, anomalies — are the ones you have least data on. Generate them deliberately, with labels, at the rate you choose.
- Upsample rare classes without duplicating records
- Inject labeled anomalies into realistic sequences
- Simulate scenarios that haven't happened yet
sensor_streamanomaly
Model testing
Stress-test models before they ship.
Hold-out sets tell you how a model does on data like the past. Synthetic test suites tell you how it does on the scenarios you're worried about.
- Targeted test sets for known failure modes
- Controlled variation to measure robustness
- Repeatable suites to compare model versions
test_suiteevaluation
Low-light images✓ pass
Ambiguous user intents✓ pass
Out-of-range inputs✓ pass
Rare-class recall✓ pass
Who it's for
Built for the people who build models.
Data scientists
Explore more segments and scenarios than your raw data allows.
Research teams
Run controlled experiments on data you can shape precisely.
Data owners
Enable more use of data while reducing how often sensitive records move.
Get started
Have a model that's starved for data?
Tell us the task, the gaps, and what good looks like. We'll show you how SETC would approach it.