Synthetic Data Pipeline: Building a Scalable, Low-Overhead Training Data Flow

AI systems demand increasing amounts of data to perform well—but gathering real-world data can be costly, slow, and limited in scope. That’s why many teams are turning to synthetic data: it’s faster to generate, easier to customize, and ideal for covering rare or hard-to-capture scenarios.

Limitations of Real-World Data

High-quality, large-scale datasets are essential for training robust AI models. However, traditional data collection pipelines face several challenges:

Scarcity: Rare edge cases—like industrial accidents or extreme weather—are difficult to record in the real world.
High Costs: Collecting and manually labeling real-world data is time-intensive and expensive.
Inconsistency: Human annotations can be prone to error and vary across datasets.

Faster, Smarter Data Collection

Synthetic data provides a powerful alternative, especially when real-world samples are difficult or inefficient to obtain. Our pipeline combines generative AI with automated labeling, creating a fast, scalable solution for training data generation.

Data Generation: Generative models like GANs and diffusion models can produce realistic text, images, or videos in large volumes.
Auto Labeling: Because the environment is controlled, annotations—such as bounding boxes, segmentation masks, and class labels—can be generated automatically.

graph TD
    A@{ shape: text, label: "Text/Image/Video" }
    A --> B["Data Generation"]
    B --> C["Auto Labeling"]
    C --> D["Model Training"]

The Synthetic Data Pipeline

This approach minimizes manual effort, ensures consistent labeling, and scales with minimal operational overhead.

How We Generate Data

Today’s generative models can create high-quality visual content from simple prompts. For example, tools like text-to-video models allow users to generate videos using short text inputs, supporting multi-modal and controllable conditions.

Text Prompt: “A worker falls down in a factory”

Changing the input text enables us to generate specific scenarios quickly. However, common challenges still exist:

Physical Inconsistencies: Unrealistic object collisions or gravity
Anatomical Errors: Unnatural body movements or incorrect joint placement

To address these issues and improve both data quality and consistency, we’ve integrated a 3D simulation toolchain with physics and animation control—specifically, NVIDIA Omniverse. This allows us to generate synthetic scenes that are not only visually realistic but also physically plausible.

These controlled 3D-generated assets are ideal for downstream tasks like automated labeling and model training. They significantly improve data usability and help accelerate model convergence and overall performance.

Auto-Labeling at Scale

Once large volumes of synthetic video are generated, the next step is annotation. Traditionally, this involved manually drawing boxes or masks frame by frame—a process that was both tedious and resource-heavy.

Today, with the rapid development of open-source vision models and large-scale training datasets, semantic-driven automatic annotation technologies have become increasingly accessible and practical. We employ a natural language prompt-based automatic annotation pipeline that accurately labels specific objects and actions based on video context, suitable for large-scale synthetic data.

For example, inputting the prompt “hard hat” enables the system to automatically detect and annotate all instances of safety helmets within the video:

Insert annotated image

This automation greatly reduces the need for manual work, improves label consistency, and accelerates dataset development.

Conclusion: Smarter Data for Smarter AI

High-quality training data doesn’t have to come with high costs or heavy workloads. By combining generative models, 3D simulation, and auto-labeling tools, we’ve built a synthetic data pipeline that’s efficient, flexible, and scalable.

If you're facing challenges with data collection, annotation workflows, or model training, we offer not only hands-on expertise and proven technical solutions—but also guidance on selecting the right hardware for inference or training.

Get in touch with us—we’re here to help you build AI systems that are faster, smarter, and more cost-effective.