Proven synthetic data for medtech, healthcare and space
Use the signal without sharing more real records.
We create synthetic datasets that preserve the patterns your model needs, without copying the original records into the output. Then we test the dataset for fidelity and privacy before you build with it.
What you bring
A model to build, and data you cannot use as it stands.
The source data may be restricted, scattered across systems, too small for the task or missing the rare cases that matter. The starting point is the model you need to train or test, the evidence you already have and the rules governing how that evidence can be used.
We use that intended purpose to define what the synthetic dataset must preserve, what gaps it must fill and what privacy risks must be checked.
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.
The dataset is designed around the job it needs to do, not generated in isolation.
What you receive
A generated dataset, with evidence attached.
We learn the useful structure of the source data, generate new records and shape the output around the model or research task. The synthetic dataset contains generated examples rather than copies of the original records.
The work does not stop at generation. We check whether the output preserves the relationships the intended model needs, and whether it exposes or memorises source data. A dataset is useful only if it passes both tests.
Intended use defined
Agree what the dataset must help a model learn, test or compare.
Synthetic dataset generated
Create new records that preserve useful patterns and fill agreed gaps.
Fidelity checked
Measure whether the dataset still carries the signal the intended model needs.
Privacy risk checked
Test for memorisation, leakage and re-identification risk before wider use.
Privacy and governance
Less real data in motion can reduce exposure.
A synthetic dataset can reduce how many copies of real personal data need to be secured, moved or shared. That may support obligations under regimes such as the Australian Privacy Act, GDPR and HIPAA, depending on the jurisdiction and use case.
Synthetic does not automatically mean private or compliant. A careless generator can memorise and leak source records. We test re-identification and leakage risk explicitly, and the organisation using the data remains responsible for its legal and governance decisions.
Where teams use it
Each dataset is designed and evaluated for a defined model or research task.
Synthetic data for medtech & healthcare
Train and test diagnostic or clinical models where patient data is restricted, rare conditions are under-represented or research partners cannot freely exchange real records.
Sensor, imagery & scenario data
Create training and test data for conditions that are difficult, expensive or unsafe to capture repeatedly, including sensor and computer-vision scenarios grounded in simulation.
Bootstrap, stress-test, augment
Start model development with limited real data, add rare or missing cases and stress-test performance beyond the easiest examples in the source dataset.
How it fits
A standalone offering that also strengthens the Digital Twin.
A Synthetic Data project gives a team a generated dataset for a defined model or research task, with fidelity and privacy checks attached. Inside the ALTDATA Digital Twin, simulation produces structured synthetic data that trains the fast AI model used for virtual experiments. Different jobs, the same discipline: generate the evidence, then test whether it is fit for purpose.
Frequently asked questions
What is synthetic data?
Generated data that preserves useful patterns from source data without copying the original records into the output. It is used to train and validate AI where real data is scarce, sensitive, imbalanced or slow to collect.
Is synthetic data actually private?
Well-made synthetic data can reduce exposure because the output contains generated records rather than copies of real people. But a poor generator can still memorise source data, so we test privacy risk rather than assume synthetic means safe.
Is synthetic data as good as real data for training AI?
It depends on the task. We test whether the synthetic dataset preserves the relationships the intended model needs, and where possible check performance against real evidence before it is used.
Does synthetic data help with GDPR, HIPAA or the Privacy Act?
It can reduce how much real personal data is copied, moved or shared, which may support privacy obligations. It does not provide a compliance certificate. The answer depends on the jurisdiction, source data, generation method and intended use.
What's the difference between synthetic data and a digital twin?
Synthetic Data is a standalone ALTDATA offering and part of the Digital Twin method. A standalone project produces data for a model to train or test on. Inside the twin, simulated data trains the fast AI model that runs virtual experiments.
Which industries use ALTDATA's synthetic data?
Medtech and healthcare, space, and research teams generally: anywhere useful data is restricted, rare or expensive to gather.
How do we get started?
Start with the model you're building, the data you have, the gaps you need to fill and the privacy or governance constraints around the work. Discuss your data problem →
The bottom line
The dataset has to pass both tests.
It must preserve the signal your model needs, and it must not expose the records it was built from. ALTDATA generates the dataset and checks both sides before you build with it.
Bring us the data problem behind your model.
Start with the model you're building, the source data you have and why it cannot be used as it stands. We'll define what a useful synthetic dataset needs to prove.