Kinetra Tech provides high-quality, human-labeled motion and action data so robotics AI systems can learn to understand the real world.
Real-world robots don't learn from synthetic datasets. They need structured, high-fidelity human action data — and that's exactly what we produce.
We receive raw robot-captured video, break it into meaningful segments, and annotate each clip with precise natural-language descriptions of human actions.
Trained contributors label body movement, interaction types, spatial context, and intent — giving AI models the structured signal they need to generalize.
Final datasets are quality-reviewed, formatted to spec, and delivered directly to robotics and AI teams ready to plug into their training pipelines.
We are actively building a skilled workforce of data contributors, enabling us to scale output quickly as client demand grows.
A rigorous, multi-stage pipeline that turns unstructured robot-captured video into precise, actionable training datasets.
Robotics partners send us segmented video footage captured by robots in real-world environments — homes, workplaces, and public spaces where humans naturally move and interact.
Clips are distributed to our network of contributors through a structured workflow. Each annotator follows detailed guidelines for consistent, high-quality labeling across every task.
Contributors produce natural-language captions, action classifications, spatial context labels, and motion descriptors for each clip — capturing the full richness of human behavior.
Every annotation passes through a review process to ensure accuracy, consistency, and compliance with client specifications before it progresses.
Clean, structured data is packaged and delivered to AI teams — ready to feed directly into robotics training pipelines, improving model performance on real-world tasks.
We're not a general annotation company. Our focus is exclusively on human motion and action data — the hardest and most valuable problem in robotics training.
Our workflows are designed specifically for human action data in physical environments — not generic document or image labeling.
Multi-layer review ensures every annotation meets the precision standards that AI training requires. No shortcuts.
We are building infrastructure to scale contributor capacity rapidly, so we can meet growing demand without sacrificing quality.
We work closely with robotics partners to understand their data format, schema, and performance needs — not one-size-fits-all output.
Every clip we annotate comes from actual robot-captured footage in real environments — the ground truth that synthetic data simply cannot replicate.
As an early-stage team, we move fast, communicate directly, and adapt our process to fit what early robotics clients actually need.
Whether you're a robotics company looking for training data, or someone who wants to contribute — we'd love to hear from you.
Looking for structured human motion data? Let's discuss your training data needs and pipeline requirements.
Join our contributor network. Work remotely on meaningful AI projects and help shape the future of robotics.