Training an AI agent to navigate a complex web app or desktop tool requires thousands of labeled examples. Every click, keystroke, and error message must be recorded and annotated. Real-world data collection is expensive and slow, and it often exposes sensitive information or violates privacy policies. Organizations need a way to generate large volumes of high-quality, labeled UI interaction data without these risks.
The cost of real UI data at scale
Collecting labeled UI interaction data manually costs a lot. A typical labeling job for a single web application might require hundreds of hours of human effort. At an average hourly rate of $30, that alone is $9,000. Add in data cleaning, validation, and storage, and the price climbs quickly. Real-world data collection also introduces latency, new features are released, workflows change, and the data can become outdated within weeks. This makes it difficult to keep training datasets aligned with the current state of production systems.
Why synthetic UI data works
Synthetic UI interaction data is generated by simulated environments that mimic real applications. These environments replicate layouts, controls, and workflows, then record agent actions as if a human were using the system. The main benefits are speed and scale. A synthetic data pipeline can produce 10,000 labeled interactions in a single day. Costs drop by an order of magnitude compared to manual labeling. Because the data is created in isolation, there is no risk of exposing sensitive information or violating user privacy. Synthetic data also guarantees that every edge case and error path is covered, which is difficult to achieve with real-world data alone.
Key techniques for high-quality synthetic UI data
- Use computer use agents that execute realistic workflows on mirrored applications.
- Control randomization at the right level, propagate known variations but keep consistent context.
- Incorporate simulated UI elements like dynamic dropdowns, modals, and error states to increase robustness.
- Validate synthetic trajectories against ground-truth workflows to ensure logical consistency.
- Combine synthetic data with a small amount of human-validated examples to fine-tune performance.
The key is not just volume, but realism. Synthetic data must behave like real user interactions, not just a random sequence of clicks.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This allows teams to generate synthetic UI interaction datasets that closely match their actual applications. The service is custom and contact-led, meaning you work directly with the Coasty team to define requirements, scope, and deliverables. There is no self-service platform or fixed package, Coasty tailors the solution to your specific use case, whether you need browser automation, desktop interaction, or both.
If you need labeled UI interaction data at scale, synthetic data offers a practical solution. Coasty’s custom service can help you build datasets that align with your applications and workflows. To explore how Coasty can support your project, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.
Want to see this in action?
View Case Studies