What you get.
Placeholder: a few lines on how error bounds works and why it matters.
- Absolute or relative bounds
- Per variable and per region
- Verification report with every file
- Science-grade, not just “looks fine”
Used for
Where error bounds helps.
RAN & fronthaul
Raw IQ samples over less fibre.
See howPost-production
Camera originals that don’t fill the SAN.
See howSpectrum monitoring
Record the whole band, not just what you flagged.
See howRadio astronomy
Telescope data that outruns any disk.
See howAI training data & checkpoints
Datasets and checkpoints that fit the budget.
See howDrone mapping & survey
Upload a day’s flights before the next one.
See howDrone inspection
Power lines, towers and turbines, in full detail.
See howTactical edge networks
Sensor and imagery feeds over contested links.
See howSIGINT & electronic warfare
Keep the raw signal for the analyst who needs it later.
See howSonar & maritime sensors
Acoustic and radar data from ships, submarines and buoys.
See howGeospatial & mapping agencies
National imagery and elevation data, smaller.
See howClimate & weather archives
Petabytes of model output in a fraction of the space.
See howForecast distribution
Get each model run to users before the next one lands.
See howWeather radar
Every sweep from every radar, kept.
See howCrop imagery & remote sensing
Season-long imagery for every paddock.
See howMachinery & yield data
Every pass of the header and sprayer, kept.
See howSimulation & HPC
Checkpoints and outputs that no longer fill the cluster.
See howConnected vehicles
Camera and sensor data from the fleet, without the data bill.
See howCondition monitoring
High-rate vibration data from every machine.
See howSeismic & geophysical sensors
Dense arrays, recorded continuously.
See howFeatures
More features.
Neural codecs
Codecs trained on your kind of data.
Learn moreScientific formats
NetCDF, GRIB, Zarr, GeoTIFF and more.
Learn moreEdge & streaming
Compress at the sensor, before the link.
Learn moreSDK & CLI
One line in Python, a command in the shell.
Learn moreCloud, on-premises or air-gapped
Runs where your data already lives.
Learn moreWe’ll cover
- Compress run on a sample of your data
- Size, speed and error compared with what you use now
- Fitting it into your pipeline
- Deployment: cloud, on-premises or edge