AI models fail when they see only common cases. Rare events and edge cases stay underrepresented in real-world datasets. This limits performance and makes evaluation unreliable. Synthetic data lets teams generate the exact situations they need.
Real data is hard to get for rare events
Accidents, fraud, or network intrusions happen far less often than normal transactions. In the US, credit card fraud is about 0.1% of all transactions. To get a statistically meaningful sample of fraud, you need tens of millions of transactions. That’s expensive, and the signal is noisy. Similarly, medical imaging datasets often lack rare conditions, forcing teams to use weak proxies that don’t capture the real complexity.
Control and scale
Synthetic data solves the scale problem by design. You can generate thousands of edge-case examples without waiting for real events. You also control the parameters: you can simulate rare weather, network failures, or UI bugs on demand. One research team created 100,000 synthetic cybersecurity alerts by varying attack vectors and timestamps, then used them to train a classifier that reached 94% F1 on a real test set. The synthetic data focused the model on the rare patterns that real data had not covered.
Tradeoffs you should know
- Realism matters. Synthetic data can misrepresent the distribution if the generation process is not grounded in real data.
- Label quality depends on the generator. If you generate labels automatically, you must validate them on a subset of real data.
- Domain knowledge is required. You need people who understand the system to design realistic scenarios and parameter ranges.
- Computational cost. Generating high-quality synthetic data can be expensive, especially for complex multimodal interactions.
The key is to pair synthetic data with a validation strategy: use real data for baselines and a small but representative slice of synthetic data for fine-tuning.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. These agents interact with applications and websites just like humans, so the captured data reflects realistic workflows and edge situations. The team can use that data to build custom synthetic datasets and trajectories for training and evaluating AI agents and models. Because every use case is different, Coasty’s offering is a custom, contact-led service. You talk to the Coasty data team about your requirements, and they design a solution around your needs.
Don’t let rare events limit your model. Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to explore how synthetic data can strengthen your training and evaluation pipelines.
Want to see this in action?
View Case Studies