How to Generate Labeled UI Interaction Data at Scale
Most teams building AI agents struggle to get enough labeled UI interaction data. Real data is expensive to collect, hard to label correctly, and often comes with privacy risks. Synthetic data lets you generate millions of realistic interactions on any web or desktop interface, at a fraction of the cost and with zero privacy concerns.
The data gap for AI agents
Modern AI agents rely on interaction data: clicks, scrolls, form fills, navigation, and keyboard inputs. A single complex web app can involve dozens of steps per task. If you need 100,000 labeled trajectories to train a model, you might need to hire a team to manually record and annotate every action. That quickly exceeds budgets and timelines.
Why synthetic data works for UI interactions
Synthetic data is generated by simulating realistic user behavior on a target interface. The key is to capture the variability that real users show: occasional mistakes, edge cases, and diverse workflows. A well-built simulator can produce millions of labeled trajectories in days, not months. The cost per labeled interaction drops from dollars or more (with manual labeling) to a few cents. Privacy is also handled automatically. No real user data ever leaves the environment.
Core tradeoffs you should know
- ●Realism vs. controllability: Synthetic data gives you full control over edge cases and rare scenarios, but you must ensure it mirrors real behavior.
- ●Coverage of workflows: A single simulator can cover many workflows, but you may need multiple models to capture all variations.
- ●Annotation overhead: Even synthetic data needs labels, task completion, error types, and intermediate states. Automated labeling can help.
- ●Validation budgets: Synthetic data must be validated against real data to catch systematic biases or missing behaviors.
The most effective strategy is a hybrid: use synthetic data to cover high-volume, low-risk scenarios, and reserve real data for critical validation and edge cases.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, so it captures realistic interaction data and can produce synthetic datasets and trajectories for training and evaluating agents and models. It is a custom, contact-led service: you work directly with the Coasty data team to define your requirements, workflows, and acceptance criteria. There is no self-serve platform and no fixed pricing. The team builds a tailored solution around your use case. If you are building or evaluating agents that interact with web or desktop interfaces, a custom synthetic data approach can dramatically accelerate your data pipeline.
If you need labeled UI interaction data at scale, the fastest path is to talk to the Coasty data team. Book a data call to explore how synthetic data can support your AI projects: https://cal.com/coasty/coasty-data-call