Training data for regulated teams
Build classifiers, extractors, copilots, and forecasting systems when production data cannot leave your environment.
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Synthetic Data
Train AI without exposing real data.
We generate realistic text, tabular, time-series, and image data that preserves the patterns your model needs while removing the privacy, access, and compliance bottlenecks that slow projects down.
More training data, faster experiments, and fewer compliance blockers.
0 real personal records needed for training pilots
4 data types covered: text, tables, time-series, images
10x faster iteration when teams are not waiting on sensitive data access
The bottleneck
What this fixes
Most AI projects stall because the useful data is locked behind privacy reviews, sparse edge cases, missing labels, or legal restrictions. Teams either train on too little data or send sensitive records into tools that were never designed for regulated workflows.
Our work
How Tabularis helps
We model the statistical structure, business rules, rare cases, and label distributions your system needs. Then we generate synthetic datasets that can be used for model training, evaluation, red-team testing, demos, and vendor-safe collaboration.
Specific capabilities
Built for real production constraints
Synthetic customer records, transactions, claims, medical notes, support tickets, logs, and domain-specific documents.
Rare-event generation for fraud, failures, anomalies, escalations, safety cases, and underrepresented classes.
Schema-aware tabular data with valid joins, constraints, distributions, and realistic missingness patterns.
Time-series generation for sensor streams, demand curves, financial sequences, and monitoring signals.
LLM-assisted text data with controlled labels, writing styles, languages, and adversarial examples.
Privacy checks, leakage tests, and quality reports before the data enters your training pipeline.
Engagement model
From first dataset to deployed system
01
Profile the real problem
We inspect schemas, label goals, edge cases, privacy constraints, and model failure modes without requiring broad data access upfront.
02
Generate and validate
We create synthetic samples, measure utility and leakage risk, then iterate until the data is useful for your target task.
03
Plug into training
We deliver datasets, generation scripts, evaluation reports, and optional pipelines for continuous synthetic data refreshes.
Where it pays off
Concrete use cases
Edge-case expansion
Generate more examples of rare failures, fraud cases, escalations, or safety-critical scenarios before they happen often in production.
Safe vendor collaboration
Share realistic datasets with external partners without exposing real customers, patients, transactions, or internal documents.
Next step
Bring one workflow, dataset, or model target.
In the first call we map the technical path, data requirements, deployment constraints, and whether a focused pilot makes sense.