Synthetic Data Generation
Generated datasets for training, testing and demonstration where real data is scarce, restricted or too sensitive to move, produced with fidelity and privacy checks rather than taken on trust.
Synthetic data is genuinely useful and routinely oversold. It can unblock a training set, populate a realistic test environment and let a demo happen without a data protection argument. It cannot conjure information that was never present in the seed, and a project built on the assumption that it can will fail late.
Synthetic data generation is the creation of artificial records that preserve the statistical structure, relationships and edge cases of real data without reproducing any real individual's information, produced for model training, software testing, demonstration or sharing where the original data cannot be used.
When synthetic data earns its place
- Scarce training examples. A fine-tuning set with a few dozen genuine cases can be extended into something trainable, as discussed under LLM fine-tuning.
- Rare but critical cases. Fraud patterns, safety incidents and unusual document formats occur too rarely in real data to learn from, and can be generated deliberately.
- Test environments. Realistic, high-volume test data that carries no personal information and can sit in a development environment without a compliance conversation.
- Demonstrations and sales. A convincing dataset that lets a product be shown without exposing a customer's records.
- Cross-boundary work. Development or vendor evaluation that cannot happen against production data for residency or contractual reasons.
And where it does not help: when the real data is plentiful and accessible already, when the model needs to learn a relationship the seed data does not contain, or when the intended use is a regulatory submission that requires evidence from real observations.
How we generate and validate it
Start from structure, not from vibes
We document the schema, the distributions, the relationships between fields and the constraints that must always hold, working from a seed sample or, where none can be shared, from your subject experts. Generation that ignores constraints produces records that look plausible individually and are nonsense as a set.
Generate for the edges deliberately
The value of synthetic data is concentrated in the cases real data is short of. We over-sample the rare, the malformed and the adversarial on purpose, and label them, so a model or a test suite is exercised where it actually breaks rather than where it is already comfortable.
Measure fidelity, not appearance
Fidelity is tested rather than eyeballed: per-field distributions compared to the seed, cross-field relationships preserved, and a discriminator trained to distinguish real from synthetic. If a simple model can separate them easily, the synthetic set will teach a model the wrong thing, and we say so.
Test for privacy leakage
Generated records are checked against the seed for memorised or near-duplicate entries, and tested for the inference risks that matter: whether membership in the original dataset can be deduced, and whether rare combinations of attributes make an individual re-identifiable. Synthetic is not automatically anonymous, and treating it as such is a common and expensive mistake.
Check for contamination
Where synthetic data is used for training, we verify it has not leaked into the evaluation set. A model tested on data derived from its own training material produces excellent scores and no information, which is the quietest way to ship something that does not work.
Synthetic data has a ceiling
It preserves what the seed contained. If a relationship, a failure mode or a demographic is absent from the real data, no amount of generation invents it, and a model trained on the result will carry the same blind spot with more confidence. We state that ceiling explicitly before generation rather than after evaluation.
What we generate
| Type | Typical use | Primary validation |
|---|---|---|
| Tabular records | Test environments, model training, analytics development | Distribution and relationship fidelity |
| Documents and forms | Extraction model training, layout variety | Structural realism, expert review |
| Conversations and tickets | Fine-tuning, assistant testing, routing models | Expert review, task-level scoring |
| Question and answer pairs | Evaluation sets, retrieval testing | Answerability against the real corpus |
| Edge and adversarial cases | Robustness and safety testing | Deliberate coverage, labelled by failure type |
In every case the generated set is delivered with its generation code, so it can be regenerated at a different volume or extended when the schema changes.
How the engagement runs
Fidelity and privacy are tested before the set is used for anything, not after.
Requirements and schema
Intended use, schema, constraints and distributions documented, and the ceiling of what synthetic data can achieve here stated in writing.
Generation pipeline
The generator built, with deliberate coverage of rare and adversarial cases and labelling by case type.
Fidelity testing
Distribution comparison, relationship preservation and discriminator testing against the seed, with results reported honestly.
Privacy and contamination testing
Memorisation, near-duplicate, membership inference and re-identification checks, plus train and evaluation separation.
Downstream validation and handover
The set used for its intended purpose and scored, then delivered with the generation code and documentation.
What you receive
A dataset you can defend, with the code to regenerate it and the tests that show what it is and is not.
Synthetic dataset
Generated records at the agreed volume, labelled by case type including the deliberate edge cases.
Generation pipeline
Code and configuration to regenerate, extend or rebalance the set as the schema changes.
Fidelity report
Distribution and relationship comparison against the seed, with discriminator results.
Privacy assessment
Memorisation, near-duplicate, membership inference and re-identification testing, with residual risk stated.
Contamination check
Evidence that generated training data has not leaked into evaluation sets.
Documented limitations
What the set cannot be used for, and which conclusions it cannot support.
Is this the right engagement?
Worth being direct. Synthetic Data Generation is the wrong spend in some situations, and those are listed rather than buried.
Good fit if
- A training or evaluation set is too small and more real examples are not obtainable in time.
- Rare cases matter and real data barely contains them.
- Development or testing needs realistic data that carries no personal information.
- Data cannot leave a boundary, and a synthetic stand-in would unblock work that is currently stopped.
- You need a demonstration dataset that does not expose a customer.
Choose something else if
- Plentiful real data is available and accessible. Use it.
- The model must learn a relationship the seed data does not contain.
- The output will support a regulatory submission requiring real observations.
- There is no seed and no expert who can specify the structure, so there is nothing to preserve.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
Is synthetic data personal data?
Not automatically, and that assumption causes problems. If generated records memorise real individuals, or if rare attribute combinations make someone re-identifiable, the output can still carry risk. We test for memorisation, near-duplicates, membership inference and re-identification, and report what remains rather than issuing a blanket assurance.
Can a model be trained entirely on synthetic data?
Sometimes, for narrow and well-specified tasks, but it is rarely the best design. The stronger pattern is real data extended with synthetic coverage of the rare cases, with the evaluation set kept entirely real so the score means something.
How do you know the synthetic data is good enough?
Two ways. Statistically, by comparing distributions and relationships against the seed and training a discriminator to try to separate real from synthetic. Practically, by using it for its intended purpose and measuring whether the downstream model or test suite performs as expected. The second test is the one that counts.
Does synthetic data remove the need for a data protection assessment?
It usually reduces the scope of one considerably, and the seed data used to build the generator remains in scope regardless. We produce the fidelity and privacy evidence in a form your privacy team can attach to their own assessment rather than asking them to take it on trust.
Can you generate synthetic documents, not just tables?
Yes. Forms, invoices, contracts and reports with realistic layout variation are among the most useful outputs, particularly for training extraction models where real examples are limited or sensitive. Those pair directly with multimodal AI development.
Often paired with this
Most clients combine two or three engagements from the Generative AI & LLM Engineering pillar. These are the ones that most often run immediately before or after.
LLM Fine-Tuning and Customisation
Fine-tuning when prompting and retrieval have genuinely run out, with curated data, a measured baseline and a clear verdict.
Read more →LLM Evaluation and Benchmarking
Golden sets, calibrated judges and a regression suite in CI, so quality is a number that moves rather than an opinion.
Read more →Multimodal AI Development
Systems that read documents, images, audio and video together, with grounding, verification and per-modality scoring.
Read more →Is this the right engagement?
Tell us what you are trying to build. If a different service fits better, or if you do not need us at all, we will say so.