We're building the data infrastructure for physical AI — capturing, verifying, and delivering the kind of robot-native data that foundation models need to learn from the real world.
Foundation models for robotics are getting really good — vision-language-action policies that can theoretically do anything. But there's a bottleneck: they need dense, high-quality real-world action data to actually learn. Not just video, but actual robot-native data.
Most robotics teams don't have the infrastructure to capture this data at scale. Simulation is cheap but doesn't translate to the real world (the sim-to-real gap is real). Collecting real demonstrations costs $80-150 per hour of usable data, and building an in-house pipeline is a massive undertaking.
So robots stay stuck in labs while the data they need to learn remains locked away.
Kinesis captures, verifies, and delivers robot-native data. The key difference: we don't just record humans doing tasks — we run everything through actual robots to get ground-truth data that reflects real actuator noise, sensor limitations, and physics.
Human operators perform tasks through teleoperation and motion-capture across different robot types.
Every captured task gets rerun on the physical robot itself. This captures real actuator noise and sensor behavior — not just human motion.
Automated quality scoring plus expert review filters for success/failure before packaging structured datasets.
Here's how it actually works end-to-end:
You tell us what you need the robot to do
Our operators perform the task via teleoperation
We run it on the actual robot to capture real physics
Automated scoring + human review
You get clean, structured data ready for training
Teleoperated demonstrations have become the dominant way to train physical AI. The best policies (Pi0, OpenVLA, ACT) all get their best results from real teleoperated demos — not simulation, not just video.
The market's growing fast — data for physical AI is projected to reach $64B by 2035. Raw video is getting commoditized as open datasets drop, but verified robot-native action data? That's still scarce and valuable.
Foundation model labs are increasingly outsourcing this layer rather than building it in-house. That's where we come in.
Priced per hour of delivered, verified data. Rate depends on the robot type, sensors, and task complexity.
We can score and clean your existing demo data using our robot-grounded verification pipeline.
Ongoing API access to a library of verified, task-tagged datasets across different robots and industries.
Most alternatives are missing something critical. General data platforms don't do robot-native data. Teleop tooling vendors just sell you the software, not the service. Video providers give you footage but no robot grounding.
We do the full stack: capture on actual robots, verify with automated QC + human review, and deliver clean datasets. And we're building this in Bangalore, which gives us a real cost advantage on operator labor without sacrificing quality.
The bottom line: We're the only ones offering robot-native data with built-in quality verification, at scale, cost-efficiently.
Interested in working together? We'd love to hear from you.
Location
Bangalore, India