Solutions · Data
Data you can trace back to the measurement.
Many AI projects fail on the data, not the model. We make sure your recordings are complete, traceable and ready to learn from.
Three problems that sink AI projects
Data diversity
Recordings of every condition, in balanced quantities. A model that has only seen good parts never learns a fault.
Data context
A hub and nodes that record primary data with its context: a timing record for every stream, the configuration in use and an event journal.
Data features
Features computed from the raw signal, for vibration for example RMS, kurtosis and spectral bands, the same in training and on the device.
Recorded securely
- Per-segment SHA-256 hashes and an append-only journal
- After a power loss the gap is documented and recording resumes by itself
- Exports to common formats such as NWB, MKV/HEVC, JSONL and CSV
Your data stays yours. Recordings, labels and models belong to you. None of this data ends up in a third-party cloud.
Next step
Already have recordings?
Send us a sample, for example as WAV or CSV. We tell you what can be learned from it.