Synthetic data generation
Many AI projects stall because there is not enough of the right data. The cases that matter most, such as rare failures, unusual customers or edge cases on the road and in document flows, are often the ones with the fewest examples.
We generate realistic synthetic data to fill those gaps and to test models on situations they have not seen yet. It is added to your real data, and we check whether each batch actually improves results.
What synthetic data is for
Synthetic data is generated by software instead of being recorded from real events. The usual sources are statistical models of your existing records, simulations of a physical process and generative models that produce new text and images in a controlled way.
Used carefully, it helps when real examples of a case are too rare to learn from, or when real data is too sensitive to hand to a development team. It is also a way to test a model on situations that have not happened yet.
What we build
Tabular and record data
Synthetic customer, transaction or sensor records that keep the patterns of the original data. They can be used in development and testing or shared with partners.
Rare-case augmentation
Extra examples of rare failures, fraud patterns or unusual cases, enough for a model to learn to tell them apart from normal ones.
Text and conversation data
Realistic questions, documents and dialogues for training and testing language models and assistants, including Turkish.
Images and scenes
Rendered or generated images with labels already attached, for objects, defects or conditions that are hard to photograph in enough variety.
Scenario and simulation data
Simulated sequences of events, such as unusual traffic situations or equipment behavior, for testing how a system reacts before it meets them in operation.
Stress-test scenarios
Difficult and edge cases for finding a model's weak spots during development. The final evaluation stays on real data.
When it's a good fit
Synthetic data helps most when you can say exactly what is missing, for example a known rare case, data too sensitive to share or a situation that needs testing. Something simpler may be enough when:
- more real data can be collected or labeled at a reasonable effort
- the model already performs well on the cases that matter
- the real process is not understood well enough to simulate it believably
How a project runs
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Find the gap
We look at the real data and the model's errors to see which cases are missing or underrepresented, and therefore which synthetic data is worth generating.
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Choose how to generate it
Depending on the gap, we use statistical models, simulation or generative models, and work out with your experts what realistic examples look like.
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Check realism and privacy
Domain experts review samples, and statistical checks compare distributions and relationships with the real data. We also test for near-copies and rare combinations that could reveal real people, and handle personal data under KVKK and, where relevant, GDPR.
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Show that it helps
We train the model with and without the synthetic data and compare the results. If the synthetic data does not improve them, we do not use it.
Keeping synthetic data honest
Bad synthetic data can make a model look better in testing while making it worse in practice.
Tested on real data only
The final test set always consists of real cases. Synthetic data goes into training and is never used to grade the model.
Assumptions are written down
We document what each generator assumes and what it leaves out. Anyone using the data later can see where it can be trusted.
What you receive
- Synthetic datasets for the agreed gaps, with labels where needed
- A report on realism, privacy and model results with and without the synthetic data
- The generators, so new data can be produced later
- Code and documentation, handed over to your team
Frequently asked questions
What do we need to start generating synthetic data?
A sample of your data and a clear idea of which cases are missing or causing errors. We also need some time from people who know what realistic examples look like. If a model is already in use, its mistakes are a good place to start.
How long does a synthetic data project take?
A first phase usually targets one gap and ends by comparing the model's results with and without the new data. How long it takes depends mostly on access to your data and how complex the cases are.
Does our real data have to leave our environment to generate synthetic data?
No. We can build and run the generators on your own servers or private cloud, and the original records stay where they are.
Can we use synthetic data in our existing training pipeline?
Yes. We deliver it in the same formats as your own data, so it fits your existing training, testing and analysis workflows without changes.
Work with us
Tell us in a few lines what you're working on. We'll get back to you.
From our blog
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When should you use synthetic data?
Synthetic data can help when real data is scarce, sensitive, or unavailable. When is it worth using, and how can you tell whether it improves results?
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Turn existing data into useful fine-tuning examples: format, chat templates, cleaning, deduplication, redaction, synthetic data, and pre-training checks.
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How much data do you need to fine-tune an LLM?
Find practical starting ranges for supervised LLM fine-tuning, then estimate dataset size from task scope, case coverage, model capability, and evaluation.
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