ALTDATA Synthetic Data · proven for medtech, healthcare & space
The data you can’t get, engineered.
Privacy-safe synthetic data that behaves like the real thing, so you can train and validate AI where real data is scarce, sensitive or slow to collect.
The problem
Your model is only as good as the data you’re allowed to use.
Real-world data is the bottleneck. The records you need are locked behind privacy law, scattered across systems you can’t legally combine, or simply too rare to collect: the fraud that happens once in a million transactions, the condition that shows up in a handful of scans.
So teams wait months for data access, train on datasets too small or too skewed to trust, or ship models that have never seen the edge cases that matter most.
It shows up in the numbers. In a 2025 survey of data leaders across the US, Europe and Asia, 46% named cybersecurity and privacy compliance a barrier to getting value from their AI work, and two-thirds couldn’t move even half their pilots into production (Informatica, CDO Insights 2025). The models mostly aren’t the problem. Getting lawful, usable data to them is.
You don’t have to wait for the data. You can make it.
What it is
Data that behaves like the real thing, without being it.
Synthetic data is generated, not copied. We learn the structure and statistics of your real data, then generate new datasets that carry the same signal (the same patterns, correlations and edge cases) without containing a single real record.
The trick is fidelity without leakage: data realistic enough to train on, that never memorises or exposes the individuals behind it. We validate both sides: how faithful it is, and how private.
Break the access bottleneck
Generate the dataset you need now, instead of waiting months on approvals.
Cover the edge cases
Amplify rare classes and engineer the scenarios real data barely contains.
Share without exposing
Move and share data across teams, vendors and borders without moving real personal records.
Balance and de-bias
Fix the skewed datasets that quietly wreck model performance.
The compliance angle
Privacy-safe by construction.
Because it holds no real individual’s records, well-made synthetic data changes your privacy exposure: there’s less real personal data to secure, move or breach. That can ease obligations under regimes like the Australian Privacy Act, GDPR and HIPAA, and support safer data sharing and research.
But the guarantee depends on how the data is generated. A careless generator can memorise and leak real records. So we measure re-identification risk explicitly, rather than assume synthetic automatically means safe.
Use cases
Program-specific results are stated only with verified numbers.
Synthetic data for medtech & healthcare
Develop diagnostic and clinical AI where patient data is restricted. Balance datasets for rare conditions, and enable research collaboration without moving real patient records.
Sensor, imagery & scenario data
Generate sensor, imagery and scenario data for situations you can’t safely or affordably capture in the real world, including training sets for computer-vision models grounded in simulation. Builds on ALTDATA’s computer-vision and synthetic-data heritage.
Bootstrap, stress-test, augment
Bootstrap a model before real data exists, stress-test against edge cases, and augment small datasets into ones you can actually train on.
Two engines, one problem
Two ways to beat the data gap.
When the data you need doesn’t exist, you have two options: manufacture it or simulate it. Synthetic data manufactures realistic datasets to train and validate AI. The ALTDATA Digital Twin simulates a physical or biological system so you can run experiments virtually. They compose: a twin can generate synthetic data; synthetic data can seed and validate a twin. Same problem, two engines.
Frequently asked questions
What is synthetic data?
Artificially generated data that mirrors the statistical patterns of real data without containing any real records. It's used to train and validate AI where real data is scarce, sensitive, imbalanced or too slow to collect.
Is synthetic data actually private?
Done properly, yes: it contains no real individual's records, so there's no real person to re-identify. But done properly matters: a careless generator can memorise and leak real data. We measure re-identification risk and privacy explicitly, rather than assume synthetic means safe.
Is synthetic data as good as real data for training AI?
For many tasks, yes, and sometimes better, because you can balance rare classes and engineer edge cases real data misses. The test is fidelity: does it behave like the real thing? We validate that before you train on it.
Does synthetic data help with GDPR, HIPAA or the Privacy Act?
It can. Less real personal data in play means a smaller compliance surface and safer sharing. The specifics depend on your jurisdiction and use case, so we scope it with you rather than make a blanket claim.
What's the difference between synthetic data and a digital twin?
Synthetic data manufactures realistic datasets; a digital twin simulates a system you can run experiments against. Synthetic data answers give me data that behaves like the real thing; a twin answers what would happen if. They work together.
Which industries use ALTDATA's synthetic data?
Medtech and healthcare, space, and research teams generally: anywhere real data is restricted, rare or expensive to gather.
How do we get started?
Start with a conversation about your data problem and the model you're trying to build. Talk to our data team →
The bottom line
Stop waiting for data. Start building with it.
Whether it’s locked behind privacy law, too rare to collect, or years away from existing, the data gap doesn’t have to stall your models. We engineer datasets that behave like the real thing, and prove they’re both faithful and private before you train on them.
Tell us about your data problem.
Start with a conversation about the model you're trying to build and the data standing in your way.