Byterat - Battery Data Platform

Byterat pulls data from the major cycler brands and from external test labs, including PDF and CSV drops. Multi-file sequences get stitched together automatically, which removes the step where someone renames files and hopes the channels line up. New formats are handled by the vendor rather than by your engineers, so a supplier software update doesn't turn into a two-week parsing project.

Automated checks run as data lands, flagging format problems before they reach a downstream model. Metadata sync from your schemas keeps cell IDs, build numbers and test conditions attached to the right records. The practical effect is that cycler data analysis starts from clean input, and a bad import gets caught the same day instead of after a report goes out.

Engineers describe the question, for example comparing degradation trends across three formulations, and the platform decides what to pull, writes the analysis code, runs it, searches internal and published literature for context, then produces a report. Every line of generated code stays visible, including hyperparameters and statistical methods. That transparency is the difference between a tool a lab will trust and a black box it won't.

Standard model types can be trained and deployed inside the platform without writing code, and models run inside live pipelines rather than in a notebook rerun. Anomaly detection watches for slope changes and performance deviations that sit outside expected noise. The payoff is early test termination: a program that costs $1 million a year recovers about $200,000 in capacity when average test duration drops 20%, according to the vendor's own estimate.

Live dashboards show what is running now, how each channel is being used, and where a build differs from its predecessor. Operational alerts cover the cases a lab would otherwise catch by walking the floor. As a battery analytics platform it goes past storage here, because the comparison view is the part engineers use during a review meeting rather than after it.

Physics-based models run through native PyBaMM integration next to the experimental data, so simulation and measurement share one environment. Validated workflows can be saved as templates that execute on every new dataset, and the knowledge base keeps the reasoning behind each one. Most battery lab software stops at plotting; this is the layer that carries a method from one engineer to the next.