Why Conversational and Multimodal AI Needs Synthetic Data
Most teams building multimodal or conversational models hit the same wall: not enough high-quality data. Real-world data is expensive to label, risky to use due to privacy, and often biased. Synthetic data offers a practical way to generate diverse, controllable examples that help models learn faster and evaluate more reliably.
Real Costs of Collecting Real Data for Multimodal Models
Collecting multimodal data, text plus images, audio, or video, adds complexity. A typical multimodal dataset can cost between $50,000 and $200,000 to label, depending on domain and quality. For conversational agents, annotating intent, tone, and safety responses can exceed $100 per example. These expenses scale quickly as you grow the model and try edge cases.
How Synthetic Data Helps Multimodal and Conversational AI
- ●Scales to rare edge cases that are hard or impossible to capture in real data.
- ●Enables privacy-safe training, avoiding PII or sensitive content.
- ●Reduces labeling costs by generating labeled examples programmatically.
- ●Provides control over distribution, ensuring balanced coverage across languages, styles, and modalities.
Concrete Tradeoffs to Understand
- ●Synthetic data can introduce distribution shifts if not aligned with real-world patterns.
- ●Models may overfit to synthetic artifacts if not carefully filtered or combined with real data.
- ●Quality depends on how well the generation pipeline matches the target domain and user behavior.
- ●Hybrid approaches, mixing synthetic and real data, often achieve the best balance of scale and realism.
The most effective strategies use synthetic data to cover edge cases and augment real datasets, not as a complete replacement.
How Coasty Fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data. That means the synthetic datasets it produces reflect actual user workflows, UI layouts, and multimodal interactions. Coasty delivers a custom synthetic data service tailored to your specific model and use case. Because it is contact-led, you get a personalized solution rather than a fixed package.
If you’re building or evaluating conversational or multimodal AI, synthetic data can fill gaps in your dataset and improve model safety. To explore what’s possible for your project, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.