How to Generate Labeled UI Interaction Data at Scale with Synthetic Data
Building AI that can navigate and operate software requires labeled UI interaction data. Real-world sessions are rare, dirty, and expensive to collect. Synthetic data offers a way to generate massive, high-quality datasets that match your exact app or workflow.
The real bottleneck: labeled interaction data is hard to get
Most teams hit two hard walls. First, real user sessions are fragmented. A typical web app generates hundreds of events per session, but only a small fraction are relevant to a specific task. Second, labeling that data is slow and error-prone. Manual annotation can cost 30 to 50 dollars per hour, and even then, annotators often miss edge cases or misinterpret intent.
How synthetic interaction data solves the problem
Synthetic interaction data is generated by simulating user behavior on a realistic replica of your interface. You control the task, the environment, and the expected outcome. This gives you three concrete benefits. You can generate millions of labeled examples in days, not months. You can guarantee that every session covers the full range of edge cases. And you do not have to expose real user data or risk leaking sensitive information.
Key techniques for high-quality synthetic interaction data
- ●Task graph design: Map out the user journey from start to finish, including common edge cases and error paths.
- ●Environment simulation: Create a pixel-perfect replica of your UI that behaves like the real thing, including dynamic content and network conditions.
- ●Behavior modeling: Use real-world clickstream and navigation patterns to drive the synthetic agents, making the data feel natural.
- ●Outcome verification: Automatically validate that each synthetic session reaches the intended outcome and log the intermediate steps for training.
The most successful teams combine synthetic data with a small set of real-world sessions to tune their models, dramatically reducing annotation costs while maintaining accuracy.
How Coasty fits into the workflow
Coasty operates computer use agents on real desktops and browsers to capture realistic interaction data. This means the synthetic datasets it produces are grounded in actual user behavior, not idealized scripts. The service is custom and contact-led, so you work with the team to design the exact tasks, environments, and data formats you need. There is no self-serve product or fixed plan. Coasty focuses on delivering high-quality, production-ready synthetic datasets for your specific use case.
If you need labeled UI interaction data at scale, synthetic data is a practical path forward. 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 .