Everyone is selling robotics data. Most of it isn't what you actually need.
New from @castorhat: what makes good robotics data, and why the answer depends entirely on what you're training โคต๏ธ
Introducing the PrismaX Regional Ambassador Program.
A select cohort of regional leaders building local PrismaX communities in their language, region, and time zone.
Applications open today ๐
Two things separate physical AI from industrial automation:
Behavior shaped by training data, not hand-coded rules.
Operation in real environments, not visually-marked ones.
@castorhat on why that opens a different market entirely ๐
Robotics doesn't have a model problem. It has a data problem. And underneath that, a deployment problem.
Physical AI progresses through real-world interaction. Robots act, fail, recover, and adapt. Without shared standards, every team relearns the same lessons in isolation. Deployment standards determine whether learning compounds or resets.
PrismaX is the service layer for physical AI. We run the systems that define how robots get deployed, standardize how interaction data is generated, and integrate human judgment where models fall short, turning fragmented robotics capability into deployable infrastructure.
Our Mission: Enable people and robots to work together by setting the standards that allow physical AI systems to learn and improve through real-world operation.
Our Vision: A world where intelligent robots are deployed responsibly and at scale, supported by systems that embed human judgment into how intelligence advances.
The next chapter for physical AI is about turning real-world operation into scalable intelligence. More soon.