Data Agent

Gata DVA

At Gata, DataAgent serves as a tangible proof-of-concept. It aggregates compute for large-scale AI inference, currently operating in a fully parallelized manner without inter-node collaboration.

DVA (Data Validation Agent) is Gata's first DataAgent.

It evaluates the quality of image-caption data across the entire internet, assigning a score between -1 and 1. DVA scores are used to identify and select the highest-quality data points from the internet pool, which can then be used to pre-train various vision-language Als, such as stable diffusion, Dall-E, and GPT-4o.

Gata DVA Platform

Want to try Gata DVA? Head to the DataAgent to start.

DVA Mechanism

• Traditional data labeling is human-intensive:

Conventionally, training AI models has required humans to spend countless hours manually labeling data — a slow, expensive, and unscalable process.

• The DataAgent approach:

Instead of relying on human labeling, DataAgent leverages AI to automate the entire data generation pipeline. Users can simply contribute their idle compute through the browser and participate in crowdsourcing high-quality AI data.

• Why AI labeling over human labeling?

AI-generated data is the future. As AI systems approach and surpass human-level intelligence, the most valuable training data will increasingly be produced by AI itself.

• Cost-effective and scalable:

AI-driven data generation is dramatically more efficient and scalable than human labeling, making it a necessary foundation for the next wave of AI development.