The data your model learns from.
A model is only as good as what it is trained on. We curate, label, and — where privacy or coverage demands — synthesize the data training needs, then validate it against the real world before a single epoch runs.
From raw source to training-ready set.
Four sources, one clean dataset.
Your systems
Transactions, tickets, documents, and logs — the first-party data that already encodes how your business works.
Expert labeling
Where ground truth needs judgment, we run structured labeling with your experts and measured agreement.
Synthetic generation
When real data is scarce, sensitive, or imbalanced, we generate and validate synthetic examples that match the real distribution.
Public & licensed
Open and licensed corpora to round out coverage — vetted for license terms and quality.
When you can't use the real thing.
Sometimes the real data is too sensitive, too rare, or too imbalanced to train on directly — a fraud pattern with a handful of examples, or records that can't leave your walls. We generate synthetic examples that preserve the statistical shape of the real data without the real identities, validate them against held-out reality, and use them to balance classes and cover the long tail.
Clean, private, and traceable.
Distribution checks
Compare features and labels against the real population, so training data doesn't quietly drift from production.
PII & privacy
Detect, mask, or synthesize sensitive fields — training a model never means leaking customer data.
Dataset versioning
Every dataset is versioned and lineage-tracked, so any model can be traced to exactly what it learned from.
Bias & balance audits
Check for under-represented groups and skewed labels before they become model behavior.
Bring us a hypothesis. Leave with a system.
Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.