Stop Building Pipelines.
Start Building Robots.

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the smallest footprint, with the fastest loading.

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// what knonik does

Six Agents. One Pipeline.
None Of It Yours To Maintain.

01Ingest

Accepts rosbag, HDF5, MP4, LeRobot v2, LeRobot v3, Zarr, and live sensor streams. Normalises everything into a unified schema automatically.

02Compress

Large size reductions on raw robot data. Across three policy architectures, models trained on compressed data matched or beat the uncompressed baseline.

03Score

AI quality gate that flags failed tasks, noisy trajectories, and duplicate episodes before they reach training.

04Process

Vision-language model watches each episode and drafts task descriptions, object labels, and phase annotations for your team to review.

05Visualize

Interactive dashboard to browse every episode, watch video alongside joint trajectories, and explore quality scores.

06Dataloader

4.3× faster per epoch than standard LeRobot v3 on our reference config. Built specifically for compressed robotics data, so the GPU stays fed.

// benchmarks

Every Number Here Links
To The Run That Made It.

We benchmarked Knonik against standard tooling on real robotics data. Compression quality verified across three policy architectures. DataLoader performance measured end-to-end.

CompressionView details →

Training Quality Benchmark

Does Knonik compression preserve learning signal?

Domain gap: < 0.004 MAE
Models tested: 3
Best MAE ratio: 0.774×

Diffusion Policy, ACT, and Flow Matching Policy trained on compressed vs uncompressed data. Full learning curves, domain transfer analysis, and per-model breakdowns.

DataLoaderView details →

DataLoader Performance

How fast can Knonik serve training batches?

GPU util: 98.5%

6 loader configs benchmarked on 49 robot episodes. Knonik Pipelined vs PyTorch, HuggingFace Arrow, and more. Full latency distributions and epoch scaling.