Most AI teams hit the same bottleneck: they need interaction data for computer use agents, but real data is scarce, risky, or expensive. They consider building a synthetic data pipeline from scratch. That approach often drags on for months and blows the budget. The smarter move is to understand what building really costs versus buying a ready-made, custom dataset.
Building a synthetic pipeline in-house
Creating synthetic interaction data from scratch requires a stack: real user environment capture, trajectory simulation, labeling, and refinement. A typical engineering team spends 3 to 6 months just lining up the infrastructure. Then you need experts to design scenarios that realistically mirror real workflows. The hidden cost is the team bandwidth: engineers and data scientists who could be shipping features instead spend time on data plumbing. One midsize engineering team estimated 40% of their first-year budget on a nascent synthetic pipeline, with only a fraction of that effort producing usable labeled trajectories.
Data quality and coverage
A pipeline is only as good as the scenarios it generates. Building high-quality data means iteratively testing, refining, and validating trajectories against real interactions. Synthetic data often fails to capture edge cases, nuanced error flows, or domain-specific workflows. Teams that do build their own pipelines frequently end up patching the data to fix coverage gaps, which adds more engineering time and delays model training. Real-world benchmarks show that synthetic datasets without rigorous validation can degrade model performance by 5 to 15% compared to high-quality real data.
Scalability and maintenance
When a business grows, synthetic data needs to scale across new tools, regions, and workflows. A custom pipeline requires ongoing updates to keep up with changing software UIs, new error paths, and evolving user behaviors. Maintenance includes retraining scenario generators, fixing format drift, and re-optimizing labeling pipelines. Teams that manage a pipeline at scale report a 30% increase in maintenance costs each year, as the system becomes more complex. Synthetic data that isn’t regularly refreshed eventually becomes stale, limiting its usefulness for both training and evaluation.
The real cost of building a synthetic pipeline is not just engineering hours, it is delayed model launches, lower data quality, and escalating maintenance overhead. Many teams find that a custom synthetic dataset from a specialized provider delivers better coverage, higher fidelity, and faster time to value.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data and trajectories. It can produce custom synthetic datasets tailored to your workflows, tools, and evaluation needs. This is a custom, contact-led service: you discuss your requirements with the Coasty data team, and they design a dataset that matches your use case. There is no generic pricing page and no fixed packages, everything is built around your specific project.
If you need high-quality interaction data for training or evaluating AI agents, skip the months of in-house build time. Talk to the Coasty data team to see how a custom synthetic dataset can meet your needs. Book a data call at https://cal.com/coasty/coasty-data-call.
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