Every hour spent on pipelines is an hour not spent on the model. And the pipelines never stop breaking.
The average robotics ML team spends 40% of their time on data infrastructure. Knonik gives that time back.
Every new robot, task, or team member triggers another brittle pipeline from scratch. The work never compounds.
Raw teleoperation logs are bloated with redundant frames. Storage costs compound before your first model trains, and only grow from there.
10–30% of training compute wasted on I/O stalls while the dataloader catches up to the GPU.
Labeling episodes by hand burns engineering time on work a machine should do.
Datasets are task-specific and non-reusable. Every project starts from zero. Nothing carries forward.
You collect hundreds of episodes with no way to know which are worth training on. Bad data trains bad models, and nobody catches it until the model fails.
This isn't a tool you integrate. It's a system we deploy and operate inside your stack. Your data never moves.
Our pipeline runs on your servers. None of your data leaves your infrastructure. Ever. This is not a policy. It is how the system is built.
100–150× lossless compression on robotics sensor data. Smaller storage footprint, faster I/O, same signal fidelity.
Optimized dataloaders that keep GPU utilization maxed out. No idle cycles waiting on disk reads.
VLM-powered annotation, episode scoring, and quality filtering. Bad demonstrations are flagged before they contaminate your training run.
Compare, inspect, and analyze your multimodal data across episodes. Understand what your robot actually collected before you train on it.
The combination of fully managed delivery with on-prem architecture is what nobody else offers. That is not a feature gap. It is a structural position.
But they require your team to integrate, maintain, and operate them. That's still infrastructure headcount.
But they require your data in their environment. That's a data sovereignty problem for any team with proprietary task data.
Fully managed. On-prem by architecture. Robotics-native from the ground up. This combination doesn't exist elsewhere.
Your teleoperation demonstrations, task embeddings, and manipulation strategies are the moat you are building. Knonik keeps it that way by architecture, not by policy — no data ever leaves your servers.
Every other AI solution requires your data in their cloud. Knonik is different by architecture, not by policy.
Your data is your IP. It stays on your servers. Your teleoperation demonstrations, your task embeddings, your manipulation strategies are the moat you are building. Knonik keeps it that way by architecture, not by policy.
Four converging forces make this the exact right moment to own robotics data infrastructure.
Large robotics models are now compute-cheap but data-starved. The constraint has shifted entirely to data quality and throughput.
Internet-scale datasets exist for language and vision. For physical manipulation, every team collects from scratch. Infrastructure that makes that data compound faster wins.
Robots are cheaper and more accessible than ever. The teams that train better policies faster will define the category. Data ops is the new moat.
Everyone wants generalist, cross-embodiment models. Siloed, manually managed data efforts cannot produce the scale those models need.