AI is advancing quickly, but building and scaling AI systems is still very expensive and highly complex. Only a handful of large organizations have the resources to train and productionize state-of-the-art models, while smaller teams rarely have the means to do ground-breaking research.

We’re building infrastructure that removes these barriers by making it simple and cost-effective for small teams to create and scale intelligent systems. Our goal is to make AI development seamless, so that teams can focus on research — and that research can move from idea to impact without friction.

Our platform focuses on three core principles:

• Simplicity: Run large-scale training and inference without managing distributed systems or specialized infrastructure; developing and debugging on hundreds or thousands of devices should feel as simple as on one.

• Accessibility: Reduce costs by efficiently using a mix of local, cloud, and distributed compute, while removing complexity. We make it possible to combine personal or on-premise hardware with remote resources, so teams can scale flexibly without large upfront costs.

• Performance: Deliver state-of-the-art efficiency that scales from small experiments to full production workloads, ensuring every unit of compute translates directly into progress.

We’re also an AI research team, using the same infrastructure we build to explore new directions in efficiency, model architecture, algorithms, and large-scale training methods. Our aim is to advance the field while keeping those advancements open and usable for everyone.

Ultimately, we want to make intelligence buildable — giving small teams the ability to shape the future of AI, just as large labs do today.