Wynd Labs builds data infrastructure designed to meet one of AI development's most pressing bottlenecks: access to public web data at scale. Its flagship product, Grass, is a decentralized residential proxy network that aggregates unused internet bandwidth from personal devices to form a distributed data-delivery system. Companies training large AI models use this network to access the breadth of public web content they require, while individual participants are compensated for the bandwidth they contribute.
Beyond AI training, the infrastructure supports use cases in research, analytics, and business intelligence - any context where large-scale, reliable access to public web data is operationally important. The decentralized model distinguishes Grass from conventional proxy services: the network is built from the ground up by its participants rather than relying on centralised server infrastructure.
The company has raised $4.5 million in seed funding and operates as a lean team. Its work sits at the intersection of distributed systems, data infrastructure, and AI - technical domains that are seeing sustained demand as the scale requirements of AI development continue to grow.