Synthetic Data for Conversational and Multi Modal AI: What Teams Should Know

The problem with real data at scale

High-quality labeled data is the biggest cost for most AI projects. A recent analysis of tool-using LLM benchmarks found that synthetic data can match or exceed the performance of smaller models trained on real datasets. Yet many teams still rely on scraped conversations, screenshots, and unstructured clips. These sources are noisy, incomplete, and often come with privacy and compliance constraints. Adding more real data is not always an option.

Why synthetic data works for multi modal systems

Multi modal AI must fuse text, images, and video. Real datasets are rarely balanced across modalities. A dataset rich in customer support tickets may have few examples of UI screenshots. Synthetic pipelines can generate paired descriptions and images that mirror your product interface. They can also simulate complex workflows: a user clicks through a dashboard, receives a response, and the entire interaction is recorded as a trajectory. This creates a realistic, controllable dataset for training and evaluation.

Key tradeoffs to watch

• ●Quality depends on the generative model: better simulators produce scenes and dialog that feel natural.
• ●Diversity must match your domain: synthetic data trained on generic web text does not capture your specific terminology or workflows.
• ●Evaluation is critical: automated scoring and human review help catch hallucinated or off-topic content.
• ●Bias can be inherited: if the simulator reflects common patterns, your model may overfit those patterns.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers. This setup lets teams capture realistic interaction data and turn it into synthetic datasets and trajectories. The service is custom and contact-led. Coasty works with you to design scenarios that match your product, your users, and your evaluation needs. There is no fixed package or self-serve dashboard. You start by talking to the data team.