AI teams hit a wall when they run out of high-quality, labeled data. Collecting and annotating the real thing is slow, expensive, and often blocked by privacy or safety rules. Synthetic data lets you generate fresh examples on demand, but you cannot simply buy it off the shelf. You need a reliable way to produce it at scale without adding dozens of data labelers to your headcount.
The real cost of labeled data
A 2023 industry survey found that 69% of AI teams cite data acquisition and labeling as their top bottleneck. Typical labeling costs range from $0.30 to $2.00 per data point depending on complexity. For a multimodal agent that needs thousands of trajectories, that budget blows out fast. On top of that, labeling consistency suffers, different annotators interpret the same task differently, leading to noisy feedback loops.
Synthetic data reduces labeling cost to near zero
When you generate synthetic data programmatically, the marginal cost per example drops dramatically. One Fortune 500 fintech firm reported a 15x reduction in labeling spend after switching to synthetic trajectories for their chatbot evaluator. They used rule-based generation plus a small team of domain experts who validated the output rather than built the examples from scratch. The experts handled quality control, not creation, which kept the team small while the volume of training material exploded.
How to scale without hiring more labelers
To scale synthetic data without scaling headcount, you need three things: a repeatable generation pipeline, a feedback loop to fix errors, and a small team focused on domain expertise rather than manual drafting. - **Generation pipeline:** Automate the creation of interaction scenarios using bots that mimic user behavior. This removes manual scripting. - **Feedback loop:** Run your model on synthetic examples, capture failures, and re‑generate scenarios that expose those weaknesses. - **Expert review:** Have a small group of domain experts spot systematic biases and edge cases, then update the generation rules. They are quality gatekeepers, not data writers.
The takeaway: you can massively increase the volume of usable training data by automating generation and focusing labeling effort on quality control rather than creation.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This lets the team capture realistic interaction data and generate synthetic trajectories for your specific workflows. Because the work is custom and contact-led, you get datasets tailored to your domain without having to build and maintain your own generation stack. The Coasty data team works with you to define scenarios, review sample trajectories, and scale production to meet your throughput needs.
If you are ready to grow your labeled data without expanding your labeler headcount, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .
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