Six specialised agents run inside your infrastructure, end to end. Click any stage to learn what it does.
The universal receiving dock for your robot data.
Every robot speaks a slightly different language. Knonik's Ingest Agent is fluent in all of them. It accepts raw data in every format your robots produce - rosbag files, HDF5 archives, raw MP4 video, or live sensor streams - and normalises everything into a single consistent structure without any manual conversion work from your team.
Think of it as a receiving dock staffed 24/7. Every delivery is checked, sorted, and put away correctly - automatically.
Shrink your dataset 100–150× without losing a single bit of learning signal.
Raw robot teleoperation data is extremely redundant. Cameras capture nearly identical frames at high frequency; joint sensors record tiny incremental movements. The Compress Agent orchestrates a carefully chosen combination of existing compression methods - tuned specifically for the patterns in robotics sensor data - and achieves 100–150× size reduction while preserving every meaningful signal your model will ever need. The insight isn't a new algorithm; it's knowing exactly which tools to use, in what order, and with what settings for this data type.
Smaller is faster. Data that fits in memory loads without stalling your GPU. Lower cloud and local memory costs.
An AI reviewer that catches bad demonstrations before they reach your model.
Not every robot demonstration is worth training on. Shaky grasps, incomplete tasks, sensor glitches, and operator mistakes all produce episodes that teach your model the wrong thing. The Score Agent watches every episode and assigns a quality score based on task completion, motion smoothness, sensor consistency, and outcome success - automatically, before your training run even starts.
Garbage in, garbage out. The Score Agent is the quality gate that prevents bad data from ever reaching your model.
A Vision-Language Model that reads your robot's videos and writes descriptions - no human labellers needed.
Training modern robot policies often requires natural-language task descriptions, object labels, and structured episode metadata. The Process Agent uses a Vision-Language Model (VLM) to watch each episode's video, understand what the robot is doing, and automatically generate accurate annotations - descriptions, object labels, task phases, and quality notes. It then filters and curates based on your criteria.
Language-conditioned policies need language labels. This agent generates them at the speed of your data pipeline, not your annotation budget.
A live dashboard for understanding what your robot actually collected.
You shouldn't be training blind. The Visualize Agent generates a rich, interactive dashboard where your team can browse every episode, watch the raw video alongside joint trajectories, compare episodes side by side, and explore annotations and quality scores. It's the difference between trusting your dataset and actually knowing what's in it.
Every robot team has a dataset they've never fully looked at. This makes it possible to actually understand what you have.
High-speed data delivery that keeps your GPU busy instead of waiting.
GPU time is expensive. A dataloader that stalls - even for 100 ms per batch - wastes 10–30% of your training budget. Knonik's Dataloader is purpose-built for compressed robotics data, using parallel decoding and shared-memory inter-process communication to deliver the next batch to your GPU before it's needed, every time.
The fastest loader isn't the one with the highest throughput spec - it's the one that never makes your GPU wait.
We benchmarked Knonik against standard tooling on real robotics data. Compression quality verified across three policy architectures. DataLoader performance measured end-to-end.