How to Generate Labeled UI Interaction Data at Scale with Synthetic Data
Building AI that can use software is hard. You need labeled UI interaction data, clicks, scrolls, text inputs, and mouse movements recorded on real apps. But real data is limited. Collecting it manually is slow. Scraping is risky. Some apps block bots. You end up with small, noisy datasets that don't cover the cases you actually need.
Why labeled UI data matters more than ever
Modern agents need to understand complex workflows, not just button clicks. They must handle navigation, forms, dynamic content, and error states. Without labeled examples, your model learns from scratch, leading to high failure rates in production. A 2024 study by a top AI research group showed that models trained on 10,000 realistic UI trajectories achieved 40% higher task success than models trained on 100,000 noisy screenshots. The quality and realism of the data, not just the volume, drove the gain.
The tradeoff between real and synthetic data
Real data captures the messy edge cases of live software. It includes network hiccups, layout shifts, and dynamic content. But it is expensive to gather and label. Synthetic data solves the cost problem by generating controlled, reproducible examples. You can create rare scenarios, like a broken checkout flow or a confusing error message, that rarely happen in production. Synthetic data also lets you control the difficulty level, ensuring your model sees both easy and hard cases. The downside? If the synthetic environment diverges too much from the real app, the model may not generalize. The key is to make the synthetic environment as close as possible to the target app.
Techniques that make synthetic UI data realistic
- ●Use browser or desktop automation to interact with actual apps in a controlled way.
- ●Inject realistic network delays, layout shifts, and dynamic content changes.
- ●Apply data augmentation: vary mouse speeds, add slight jitter, simulate typos.
- ●Combine multiple sources: synthetic trajectories plus a small set of labeled real examples for fine-tuning.
The most effective approach is to build or use agents that can navigate real apps like a human would, then record and label those interactions at scale.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. These agents capture realistic interaction data, including mouse movements, keyboard input, and navigation paths. You can work with the Coasty team to create custom synthetic datasets tailored to your apps and workflows. This is not a self‑service product with fixed plans. The service is custom and contact‑led. You talk to the Coasty data team, specify your requirements, and they design a solution that matches your needs.
If you need large amounts of labeled UI interaction data for training or evaluating AI, synthetic data can get you there faster and safer. To explore what Coasty can do for you, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .