Synthetic Training Data for Vision and Screen Understanding Models

Real data is expensive and limited for complex UIs

Building a robust system that understands web and desktop interfaces requires millions of high-quality screenshots, annotated UI elements, and interaction trajectories. Labeling them manually costs tens of thousands of dollars per dataset and introduces human bias. Even then, coverage is sparse, you rarely see every edge case, error state, or niche product. This gap means models underperform in production, especially for rare screens or subtle interaction patterns.

Why synthetic data can close the coverage gap

Synthetic data lets you generate millions of UI screenshots and interaction sequences that cover edge cases, error states, and rare workflows at scale. For example, a synthetic dataset can include 100,000 variations of a checkout flow with different layouts, error messages, and payment methods, something you might never see in production data. Researchers have shown that synthetic images can improve model accuracy on certain vision tasks when the synthetic distribution closely matches real-world statistics. The key is realism: if the synthetic UIs look too clean or follow idealized patterns, models fail to generalize.

Key tradeoffs to understand

• Realism vs control: Synthetic data gives you control over layout, text, and interactions, but poor visual fidelity can lead to degradation in downstream performance.
• Coverage vs cost: Synthetic data dramatically increases coverage of edge cases at a fraction of the labeling cost, but initial setup requires careful design of the generation rules.
• Bias transfer: If synthetic distributions don’t reflect real-world diversity, models may inherit bias or miss important user patterns.
• Evaluation alignment: Synthetic data should mimic the target domain’s visual style, resolution, and noise so evaluation metrics remain meaningful.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data including screenshots, UI layouts, and action sequences. This means the synthetic data we produce reflects actual interface patterns, not idealized designs. Teams can work with the Coasty team to build custom synthetic datasets tailored to their specific models and use cases. This is a custom, contact-led service, no self-serve dashboard, no fixed packages, no public pricing. The right fit depends on your data needs and engineering constraints.